copy 2016 Ness SES All Rights Reserved1

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Using Swarm Intelligence

to Prepare for the Next

Carmageddon

kmathew | ness_tech

Kuruvilla MathewChief Innovation Officer and SVP

Ness Software Engineering Services

wwwness-sescom

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

copy 2016 Ness SES All Rights Reserved2

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

In the next 30 minutes hellip

copy 2016 Ness SES All Rights Reserved

bull Carmageddon Story

bull The Bigger Problem

bull Swarm Intelligence

bull Particle Swarm Optimization

bull Analyzing Data Flow patterns

bull Actionable Insights

bull Conclusion

kmathew | ness_tech

copy 2016 Ness SES All Rights Reserved3

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Carmageddon

Southern California recently experienced a 55-hour closure of the 91

Freeway resulting in a 6-mile stretch that intersected State Route 71

and Interstate 15 The closure was called the Coronageddon

Just a few years ago a big closure dubbed Carmageddon of

California Highway 405 resulted in a traffic jam that reached

immense proportions

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech3

copy 2016 Ness SES All Rights Reserved4

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

The Bigger Problem

The extreme instances of massive traffic congestion are becoming

increasingly common resulting in daily traffic jams that are created by

early morning traffic as people get to work school traffic the lunch

rush hour and the all-too-familiar and stressful evening traffic

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech4

Traffic flow patterns are studied by cities but

most use a low tech approach They assign

people to count vehicles as they pass through

intersections at peak hours

copy 2016 Ness SES All Rights Reserved5

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Swarm Intelligence

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech5

ldquoA single ant or bee isnt smart but their colonies are The study of swarm

intelligence is providing insights that can help humans manage complex systemshelliprdquo

Source Swarm Theory By Peter Miller National Geographic Staff | httpngmnationalgeographiccom200707swarmsmiller-text

Source Wikipedia httpsenwikipediaorgwikiSwarm_intelligence

Simply put ldquoSwarm intelligence (SI) is the collective behavior

of decentralized self-organized systems natural or artificialrdquo

copy 2016 Ness SES All Rights Reserved6

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Opportunities for Smart Cities

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech6

Implement higher-tech methods such as swarm intelligence to form a more

accurate and complete picture of traffic flows so cities understand where

the real problems are

Apply this analysis to optimize traffic flow and continually monitor so

adjustments can be made more quickly to avoid the next Carmageddon

copy 2016 Ness SES All Rights Reserved7

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Understanding Traffic Flow PatternsUsing Particle Swarm Optimization

kmathew | ness_tech

copy 2016 Ness SES All Rights Reserved7

copy 2016 Ness SES All Rights Reserved8

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Scene of the Coronageddon

Source Google Maps 2016 Google Map of Corona California

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech8

In spite of the couple-day closure work continues on freeways and

adjacent roads and has been going on for a number of months This

aggravates commuters and contributes to a fair share of road rage

Using PSO and the Bees algorithm it is possible to understand and predict the behavior of the

commuters at different times of the day The changes of traffic patterns during the weekdays and

weekends provide insights that can help city planners plan for future street and freeway closures

copy 2016 Ness SES All Rights Reserved9

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Understanding Road Closures

Source Google Maps 2016 Google Map of Corona California

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech9

We can begin to better understand the traffic flow by tagging the beacon from the vehicle

andor the driver and passenger in the vehicle The effect of road closures that include streets

and ramps can be understood by analyzing the vehiclecommuter between 2 points on the

street

Installation of scanners along the streets can

capture Bluetooth and WiFi beacons of commutersrsquo

smart devices as well as the Bluetooth beacons

from vehicles

copy 2016 Ness SES All Rights Reserved10

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Applying the Bees Algorithm

Source Google Maps 2016 Google Map of Corona California

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech10

Applying the Bees algorithm to school traffic is an effective method to understand the traffic flow that is a

combination of foot traffic and vehicular traffic

Traffic as a result of the start of a school day

and dismissal will be an interesting pattern to

observe

For example at this school flow around certain intersection points

had delays but one intersection point was free flowing without

delays

copy 2016 Ness SES All Rights Reserved11

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Capturing Vehicle Data Using Scanners

kmathew | ness_tech

copy 2016 Ness SES All Rights Reserved

As described earlier Scanners can be installed on streets

typically on street light poles to capture the Bluetooth and Wi-Fi

beacons that are coming from the vehicle and commuter smart

phones respectively

The number of Scanners will vary as they need to be placed in a

manner that increases the chance of detection

Having multiple Scanners also helps determine the vector of

vehiclecommuter movement

11

copy 2016 Ness SES All Rights Reserved12

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Computing the Data

kmathew | ness_tech

copy 2016 Ness SES All Rights Reserved

height

distance

12

The Scanner will provide a fair amount of data that needs to be computed and

aggregated before it is usable Some of the key points are

bull The Machine Address (MAC) will help uniquely define the commutervehicle

bull Received Signal Strength Indicator (RSSI) is used to calculate the distance

bull The scan from 2 Scanners on a given MAC will determine the direction vector

bull The Scanner mounted height is fixed and can be calibrated

bull The distance from the sensor and the direction of travel could be determined

based on the position of the vehicle

bull The vehicle MAC commuter(s) MAC could be correlated to determine driving

behavior during rush hour weekends and other commute times

bull The date and time should be synchronized to UTC to collate into a time series

database for correlation analysis

MAC

copy 2016 Ness SES All Rights Reserved13

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Analyzing the Bluetooth Data

kmathew | ness_tech

The captured data looks as indicated from the Bluetooth

data logs This reflects the Received Signal Strength

Indicator (RSSI) including time stamp vendor and a

service tag identifier (ID)

A RSSI closer to 0 means that the vehicle is closer and

a higher value means the vehicle is farther away

Using the class of device (cod) filter you can isolate the

captured frames that are most likely from vehicles

copy 2016 Ness SES All Rights Reserved14

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

WiFi Scans

kmathew | ness_tech

In addition to Bluetooth data another beacon captured

is the WiFi beacons sent from Smart Phones and

devices While the RSSI plays a key role in

determining the distance for a given commuters smart

phone (MAC) it required some fuzzy logic to extract

out the kind of smart device it is from the vendor data

copy 2016 Ness SES All Rights Reserved15

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Insights

RSSI (db)

In the visualization what is

interesting to observe is the

behavior of the swarm

One can see the changing

behavior with time progressing

This can help determine a

tipping point whether it is start

of rush hour or the end of one

These insights are valuable in

understanding commuter

behavior with real data that can

help city planners

Note This data is from one Scanner

copy 2016 Ness SES All Rights Reserved16

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

In Conclusion

kmathew | ness_tech

By using Swarm Intelligence (SI) algorithms such as Particle Swarm Optimization (PSO) city

planners can create simulations to understand potential congestion challenges based on how

vehicles and pedestrians navigate public spaces

PSO is a good algorithm to apply to large businesses in a city as it helps them understand the

behavior of each employee or a group of employees (beginningending of shifts) navigating out of

facilities and getting on streets by walking in vehicles using public transport etc

Simulations using real data collected through this mechanism can help city planners determine

potential traffic challenges at a highly-granular levelmdashby street intersection freeway ramp school

area etc mdash to significantly improve the quality of empirical commuter data used in street flow

planning and addressing existing congestion problems

copy 2016 Ness SES All Rights Reserved17

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

About Ness Software Engineering Services

Fully-integrated user experience design platform development

and data analytics services from visioning to execution

3000 colleagues | Engineering teamrsquos level of experience

exceeds industry-average

Teams designated for clients on ongoing basis | Engineers

commonly work with the same client for multiple years

10 Technology Innovation Centers across 6 countries

Product Engineering rigor is at the foundation of our

approach

Global Scale

Engineering Heritage

Integrated Solution

Design amp Development

Long-Term Client

Relationships

Experienced Personnel

copy 2016 Ness SES All Rights Reserved18

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Further Reading

bull The Evolution of the Connected Home (DATAQUEST)

bull Improving Predictive Maintenance with IoT (IoT Central)

bull Edge Analytics an Antidote to IoT Data Deluge (InformationWeek)

bull Healthcare Things Are Getting Better at Supporting Wellness (IoT Global Network)

bull Improve Loss Prevention in Retail Stores by Applying Swarm Intelligence (Indian Retailer)

bull Contextualising Data Will Help Monetize The Internet of Things (InformationAge)

bull Inside the Connected Carrsquos Ego Network (Auto Tech Review)

bull Ness Whitepaper Capitalizing on the Business Value of the Internet of Things

bull Ness Blog Predictions 2016 ndash IoT Payments amp Loyalty Programs APIs

bull Ness Blog Does EveryrsquoThingrsquo Matter in the Internet of Things

bull Ness Blog Internet of Things and Industrial Analytics

bull Ness Blog When Every Car Becomes a ldquoSmartrdquo Car

And a number of related readings on

bull Ness Insights httpwwwness-sescominsightsresource-library

bull Ness Blog httpwwwness-sescomcategoryblog

Here is a compilation of articles whitepapers and blog posts on IoT They have been presented and

published on various channels

copy 2016 Ness SES All Rights Reserved2

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

In the next 30 minutes hellip

copy 2016 Ness SES All Rights Reserved

bull Carmageddon Story

bull The Bigger Problem

bull Swarm Intelligence

bull Particle Swarm Optimization

bull Analyzing Data Flow patterns

bull Actionable Insights

bull Conclusion

kmathew | ness_tech

copy 2016 Ness SES All Rights Reserved3

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Carmageddon

Southern California recently experienced a 55-hour closure of the 91

Freeway resulting in a 6-mile stretch that intersected State Route 71

and Interstate 15 The closure was called the Coronageddon

Just a few years ago a big closure dubbed Carmageddon of

California Highway 405 resulted in a traffic jam that reached

immense proportions

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech3

copy 2016 Ness SES All Rights Reserved4

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

The Bigger Problem

The extreme instances of massive traffic congestion are becoming

increasingly common resulting in daily traffic jams that are created by

early morning traffic as people get to work school traffic the lunch

rush hour and the all-too-familiar and stressful evening traffic

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech4

Traffic flow patterns are studied by cities but

most use a low tech approach They assign

people to count vehicles as they pass through

intersections at peak hours

copy 2016 Ness SES All Rights Reserved5

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Swarm Intelligence

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech5

ldquoA single ant or bee isnt smart but their colonies are The study of swarm

intelligence is providing insights that can help humans manage complex systemshelliprdquo

Source Swarm Theory By Peter Miller National Geographic Staff | httpngmnationalgeographiccom200707swarmsmiller-text

Source Wikipedia httpsenwikipediaorgwikiSwarm_intelligence

Simply put ldquoSwarm intelligence (SI) is the collective behavior

of decentralized self-organized systems natural or artificialrdquo

copy 2016 Ness SES All Rights Reserved6

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Opportunities for Smart Cities

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech6

Implement higher-tech methods such as swarm intelligence to form a more

accurate and complete picture of traffic flows so cities understand where

the real problems are

Apply this analysis to optimize traffic flow and continually monitor so

adjustments can be made more quickly to avoid the next Carmageddon

copy 2016 Ness SES All Rights Reserved7

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Understanding Traffic Flow PatternsUsing Particle Swarm Optimization

kmathew | ness_tech

copy 2016 Ness SES All Rights Reserved7

copy 2016 Ness SES All Rights Reserved8

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Scene of the Coronageddon

Source Google Maps 2016 Google Map of Corona California

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech8

In spite of the couple-day closure work continues on freeways and

adjacent roads and has been going on for a number of months This

aggravates commuters and contributes to a fair share of road rage

Using PSO and the Bees algorithm it is possible to understand and predict the behavior of the

commuters at different times of the day The changes of traffic patterns during the weekdays and

weekends provide insights that can help city planners plan for future street and freeway closures

copy 2016 Ness SES All Rights Reserved9

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Understanding Road Closures

Source Google Maps 2016 Google Map of Corona California

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech9

We can begin to better understand the traffic flow by tagging the beacon from the vehicle

andor the driver and passenger in the vehicle The effect of road closures that include streets

and ramps can be understood by analyzing the vehiclecommuter between 2 points on the

street

Installation of scanners along the streets can

capture Bluetooth and WiFi beacons of commutersrsquo

smart devices as well as the Bluetooth beacons

from vehicles

copy 2016 Ness SES All Rights Reserved10

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Applying the Bees Algorithm

Source Google Maps 2016 Google Map of Corona California

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech10

Applying the Bees algorithm to school traffic is an effective method to understand the traffic flow that is a

combination of foot traffic and vehicular traffic

Traffic as a result of the start of a school day

and dismissal will be an interesting pattern to

observe

For example at this school flow around certain intersection points

had delays but one intersection point was free flowing without

delays

copy 2016 Ness SES All Rights Reserved11

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Capturing Vehicle Data Using Scanners

kmathew | ness_tech

copy 2016 Ness SES All Rights Reserved

As described earlier Scanners can be installed on streets

typically on street light poles to capture the Bluetooth and Wi-Fi

beacons that are coming from the vehicle and commuter smart

phones respectively

The number of Scanners will vary as they need to be placed in a

manner that increases the chance of detection

Having multiple Scanners also helps determine the vector of

vehiclecommuter movement

11

copy 2016 Ness SES All Rights Reserved12

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Computing the Data

kmathew | ness_tech

copy 2016 Ness SES All Rights Reserved

height

distance

12

The Scanner will provide a fair amount of data that needs to be computed and

aggregated before it is usable Some of the key points are

bull The Machine Address (MAC) will help uniquely define the commutervehicle

bull Received Signal Strength Indicator (RSSI) is used to calculate the distance

bull The scan from 2 Scanners on a given MAC will determine the direction vector

bull The Scanner mounted height is fixed and can be calibrated

bull The distance from the sensor and the direction of travel could be determined

based on the position of the vehicle

bull The vehicle MAC commuter(s) MAC could be correlated to determine driving

behavior during rush hour weekends and other commute times

bull The date and time should be synchronized to UTC to collate into a time series

database for correlation analysis

MAC

copy 2016 Ness SES All Rights Reserved13

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Analyzing the Bluetooth Data

kmathew | ness_tech

The captured data looks as indicated from the Bluetooth

data logs This reflects the Received Signal Strength

Indicator (RSSI) including time stamp vendor and a

service tag identifier (ID)

A RSSI closer to 0 means that the vehicle is closer and

a higher value means the vehicle is farther away

Using the class of device (cod) filter you can isolate the

captured frames that are most likely from vehicles

copy 2016 Ness SES All Rights Reserved14

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

WiFi Scans

kmathew | ness_tech

In addition to Bluetooth data another beacon captured

is the WiFi beacons sent from Smart Phones and

devices While the RSSI plays a key role in

determining the distance for a given commuters smart

phone (MAC) it required some fuzzy logic to extract

out the kind of smart device it is from the vendor data

copy 2016 Ness SES All Rights Reserved15

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Insights

RSSI (db)

In the visualization what is

interesting to observe is the

behavior of the swarm

One can see the changing

behavior with time progressing

This can help determine a

tipping point whether it is start

of rush hour or the end of one

These insights are valuable in

understanding commuter

behavior with real data that can

help city planners

Note This data is from one Scanner

copy 2016 Ness SES All Rights Reserved16

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

In Conclusion

kmathew | ness_tech

By using Swarm Intelligence (SI) algorithms such as Particle Swarm Optimization (PSO) city

planners can create simulations to understand potential congestion challenges based on how

vehicles and pedestrians navigate public spaces

PSO is a good algorithm to apply to large businesses in a city as it helps them understand the

behavior of each employee or a group of employees (beginningending of shifts) navigating out of

facilities and getting on streets by walking in vehicles using public transport etc

Simulations using real data collected through this mechanism can help city planners determine

potential traffic challenges at a highly-granular levelmdashby street intersection freeway ramp school

area etc mdash to significantly improve the quality of empirical commuter data used in street flow

planning and addressing existing congestion problems

copy 2016 Ness SES All Rights Reserved17

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

About Ness Software Engineering Services

Fully-integrated user experience design platform development

and data analytics services from visioning to execution

3000 colleagues | Engineering teamrsquos level of experience

exceeds industry-average

Teams designated for clients on ongoing basis | Engineers

commonly work with the same client for multiple years

10 Technology Innovation Centers across 6 countries

Product Engineering rigor is at the foundation of our

approach

Global Scale

Engineering Heritage

Integrated Solution

Design amp Development

Long-Term Client

Relationships

Experienced Personnel

copy 2016 Ness SES All Rights Reserved18

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Further Reading

bull The Evolution of the Connected Home (DATAQUEST)

bull Improving Predictive Maintenance with IoT (IoT Central)

bull Edge Analytics an Antidote to IoT Data Deluge (InformationWeek)

bull Healthcare Things Are Getting Better at Supporting Wellness (IoT Global Network)

bull Improve Loss Prevention in Retail Stores by Applying Swarm Intelligence (Indian Retailer)

bull Contextualising Data Will Help Monetize The Internet of Things (InformationAge)

bull Inside the Connected Carrsquos Ego Network (Auto Tech Review)

bull Ness Whitepaper Capitalizing on the Business Value of the Internet of Things

bull Ness Blog Predictions 2016 ndash IoT Payments amp Loyalty Programs APIs

bull Ness Blog Does EveryrsquoThingrsquo Matter in the Internet of Things

bull Ness Blog Internet of Things and Industrial Analytics

bull Ness Blog When Every Car Becomes a ldquoSmartrdquo Car

And a number of related readings on

bull Ness Insights httpwwwness-sescominsightsresource-library

bull Ness Blog httpwwwness-sescomcategoryblog

Here is a compilation of articles whitepapers and blog posts on IoT They have been presented and

published on various channels

copy 2016 Ness SES All Rights Reserved3

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Carmageddon

Southern California recently experienced a 55-hour closure of the 91

Freeway resulting in a 6-mile stretch that intersected State Route 71

and Interstate 15 The closure was called the Coronageddon

Just a few years ago a big closure dubbed Carmageddon of

California Highway 405 resulted in a traffic jam that reached

immense proportions

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech3

copy 2016 Ness SES All Rights Reserved4

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

The Bigger Problem

The extreme instances of massive traffic congestion are becoming

increasingly common resulting in daily traffic jams that are created by

early morning traffic as people get to work school traffic the lunch

rush hour and the all-too-familiar and stressful evening traffic

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech4

Traffic flow patterns are studied by cities but

most use a low tech approach They assign

people to count vehicles as they pass through

intersections at peak hours

copy 2016 Ness SES All Rights Reserved5

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Swarm Intelligence

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech5

ldquoA single ant or bee isnt smart but their colonies are The study of swarm

intelligence is providing insights that can help humans manage complex systemshelliprdquo

Source Swarm Theory By Peter Miller National Geographic Staff | httpngmnationalgeographiccom200707swarmsmiller-text

Source Wikipedia httpsenwikipediaorgwikiSwarm_intelligence

Simply put ldquoSwarm intelligence (SI) is the collective behavior

of decentralized self-organized systems natural or artificialrdquo

copy 2016 Ness SES All Rights Reserved6

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Opportunities for Smart Cities

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech6

Implement higher-tech methods such as swarm intelligence to form a more

accurate and complete picture of traffic flows so cities understand where

the real problems are

Apply this analysis to optimize traffic flow and continually monitor so

adjustments can be made more quickly to avoid the next Carmageddon

copy 2016 Ness SES All Rights Reserved7

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Understanding Traffic Flow PatternsUsing Particle Swarm Optimization

kmathew | ness_tech

copy 2016 Ness SES All Rights Reserved7

copy 2016 Ness SES All Rights Reserved8

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Scene of the Coronageddon

Source Google Maps 2016 Google Map of Corona California

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech8

In spite of the couple-day closure work continues on freeways and

adjacent roads and has been going on for a number of months This

aggravates commuters and contributes to a fair share of road rage

Using PSO and the Bees algorithm it is possible to understand and predict the behavior of the

commuters at different times of the day The changes of traffic patterns during the weekdays and

weekends provide insights that can help city planners plan for future street and freeway closures

copy 2016 Ness SES All Rights Reserved9

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Understanding Road Closures

Source Google Maps 2016 Google Map of Corona California

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech9

We can begin to better understand the traffic flow by tagging the beacon from the vehicle

andor the driver and passenger in the vehicle The effect of road closures that include streets

and ramps can be understood by analyzing the vehiclecommuter between 2 points on the

street

Installation of scanners along the streets can

capture Bluetooth and WiFi beacons of commutersrsquo

smart devices as well as the Bluetooth beacons

from vehicles

copy 2016 Ness SES All Rights Reserved10

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Applying the Bees Algorithm

Source Google Maps 2016 Google Map of Corona California

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech10

Applying the Bees algorithm to school traffic is an effective method to understand the traffic flow that is a

combination of foot traffic and vehicular traffic

Traffic as a result of the start of a school day

and dismissal will be an interesting pattern to

observe

For example at this school flow around certain intersection points

had delays but one intersection point was free flowing without

delays

copy 2016 Ness SES All Rights Reserved11

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Capturing Vehicle Data Using Scanners

kmathew | ness_tech

copy 2016 Ness SES All Rights Reserved

As described earlier Scanners can be installed on streets

typically on street light poles to capture the Bluetooth and Wi-Fi

beacons that are coming from the vehicle and commuter smart

phones respectively

The number of Scanners will vary as they need to be placed in a

manner that increases the chance of detection

Having multiple Scanners also helps determine the vector of

vehiclecommuter movement

11

copy 2016 Ness SES All Rights Reserved12

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Computing the Data

kmathew | ness_tech

copy 2016 Ness SES All Rights Reserved

height

distance

12

The Scanner will provide a fair amount of data that needs to be computed and

aggregated before it is usable Some of the key points are

bull The Machine Address (MAC) will help uniquely define the commutervehicle

bull Received Signal Strength Indicator (RSSI) is used to calculate the distance

bull The scan from 2 Scanners on a given MAC will determine the direction vector

bull The Scanner mounted height is fixed and can be calibrated

bull The distance from the sensor and the direction of travel could be determined

based on the position of the vehicle

bull The vehicle MAC commuter(s) MAC could be correlated to determine driving

behavior during rush hour weekends and other commute times

bull The date and time should be synchronized to UTC to collate into a time series

database for correlation analysis

MAC

copy 2016 Ness SES All Rights Reserved13

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Analyzing the Bluetooth Data

kmathew | ness_tech

The captured data looks as indicated from the Bluetooth

data logs This reflects the Received Signal Strength

Indicator (RSSI) including time stamp vendor and a

service tag identifier (ID)

A RSSI closer to 0 means that the vehicle is closer and

a higher value means the vehicle is farther away

Using the class of device (cod) filter you can isolate the

captured frames that are most likely from vehicles

copy 2016 Ness SES All Rights Reserved14

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

WiFi Scans

kmathew | ness_tech

In addition to Bluetooth data another beacon captured

is the WiFi beacons sent from Smart Phones and

devices While the RSSI plays a key role in

determining the distance for a given commuters smart

phone (MAC) it required some fuzzy logic to extract

out the kind of smart device it is from the vendor data

copy 2016 Ness SES All Rights Reserved15

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Insights

RSSI (db)

In the visualization what is

interesting to observe is the

behavior of the swarm

One can see the changing

behavior with time progressing

This can help determine a

tipping point whether it is start

of rush hour or the end of one

These insights are valuable in

understanding commuter

behavior with real data that can

help city planners

Note This data is from one Scanner

copy 2016 Ness SES All Rights Reserved16

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

In Conclusion

kmathew | ness_tech

By using Swarm Intelligence (SI) algorithms such as Particle Swarm Optimization (PSO) city

planners can create simulations to understand potential congestion challenges based on how

vehicles and pedestrians navigate public spaces

PSO is a good algorithm to apply to large businesses in a city as it helps them understand the

behavior of each employee or a group of employees (beginningending of shifts) navigating out of

facilities and getting on streets by walking in vehicles using public transport etc

Simulations using real data collected through this mechanism can help city planners determine

potential traffic challenges at a highly-granular levelmdashby street intersection freeway ramp school

area etc mdash to significantly improve the quality of empirical commuter data used in street flow

planning and addressing existing congestion problems

copy 2016 Ness SES All Rights Reserved17

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

About Ness Software Engineering Services

Fully-integrated user experience design platform development

and data analytics services from visioning to execution

3000 colleagues | Engineering teamrsquos level of experience

exceeds industry-average

Teams designated for clients on ongoing basis | Engineers

commonly work with the same client for multiple years

10 Technology Innovation Centers across 6 countries

Product Engineering rigor is at the foundation of our

approach

Global Scale

Engineering Heritage

Integrated Solution

Design amp Development

Long-Term Client

Relationships

Experienced Personnel

copy 2016 Ness SES All Rights Reserved18

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Further Reading

bull The Evolution of the Connected Home (DATAQUEST)

bull Improving Predictive Maintenance with IoT (IoT Central)

bull Edge Analytics an Antidote to IoT Data Deluge (InformationWeek)

bull Healthcare Things Are Getting Better at Supporting Wellness (IoT Global Network)

bull Improve Loss Prevention in Retail Stores by Applying Swarm Intelligence (Indian Retailer)

bull Contextualising Data Will Help Monetize The Internet of Things (InformationAge)

bull Inside the Connected Carrsquos Ego Network (Auto Tech Review)

bull Ness Whitepaper Capitalizing on the Business Value of the Internet of Things

bull Ness Blog Predictions 2016 ndash IoT Payments amp Loyalty Programs APIs

bull Ness Blog Does EveryrsquoThingrsquo Matter in the Internet of Things

bull Ness Blog Internet of Things and Industrial Analytics

bull Ness Blog When Every Car Becomes a ldquoSmartrdquo Car

And a number of related readings on

bull Ness Insights httpwwwness-sescominsightsresource-library

bull Ness Blog httpwwwness-sescomcategoryblog

Here is a compilation of articles whitepapers and blog posts on IoT They have been presented and

published on various channels

copy 2016 Ness SES All Rights Reserved4

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

The Bigger Problem

The extreme instances of massive traffic congestion are becoming

increasingly common resulting in daily traffic jams that are created by

early morning traffic as people get to work school traffic the lunch

rush hour and the all-too-familiar and stressful evening traffic

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech4

Traffic flow patterns are studied by cities but

most use a low tech approach They assign

people to count vehicles as they pass through

intersections at peak hours

copy 2016 Ness SES All Rights Reserved5

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Swarm Intelligence

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech5

ldquoA single ant or bee isnt smart but their colonies are The study of swarm

intelligence is providing insights that can help humans manage complex systemshelliprdquo

Source Swarm Theory By Peter Miller National Geographic Staff | httpngmnationalgeographiccom200707swarmsmiller-text

Source Wikipedia httpsenwikipediaorgwikiSwarm_intelligence

Simply put ldquoSwarm intelligence (SI) is the collective behavior

of decentralized self-organized systems natural or artificialrdquo

copy 2016 Ness SES All Rights Reserved6

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Opportunities for Smart Cities

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech6

Implement higher-tech methods such as swarm intelligence to form a more

accurate and complete picture of traffic flows so cities understand where

the real problems are

Apply this analysis to optimize traffic flow and continually monitor so

adjustments can be made more quickly to avoid the next Carmageddon

copy 2016 Ness SES All Rights Reserved7

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Understanding Traffic Flow PatternsUsing Particle Swarm Optimization

kmathew | ness_tech

copy 2016 Ness SES All Rights Reserved7

copy 2016 Ness SES All Rights Reserved8

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Scene of the Coronageddon

Source Google Maps 2016 Google Map of Corona California

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech8

In spite of the couple-day closure work continues on freeways and

adjacent roads and has been going on for a number of months This

aggravates commuters and contributes to a fair share of road rage

Using PSO and the Bees algorithm it is possible to understand and predict the behavior of the

commuters at different times of the day The changes of traffic patterns during the weekdays and

weekends provide insights that can help city planners plan for future street and freeway closures

copy 2016 Ness SES All Rights Reserved9

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Understanding Road Closures

Source Google Maps 2016 Google Map of Corona California

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech9

We can begin to better understand the traffic flow by tagging the beacon from the vehicle

andor the driver and passenger in the vehicle The effect of road closures that include streets

and ramps can be understood by analyzing the vehiclecommuter between 2 points on the

street

Installation of scanners along the streets can

capture Bluetooth and WiFi beacons of commutersrsquo

smart devices as well as the Bluetooth beacons

from vehicles

copy 2016 Ness SES All Rights Reserved10

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Applying the Bees Algorithm

Source Google Maps 2016 Google Map of Corona California

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech10

Applying the Bees algorithm to school traffic is an effective method to understand the traffic flow that is a

combination of foot traffic and vehicular traffic

Traffic as a result of the start of a school day

and dismissal will be an interesting pattern to

observe

For example at this school flow around certain intersection points

had delays but one intersection point was free flowing without

delays

copy 2016 Ness SES All Rights Reserved11

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Capturing Vehicle Data Using Scanners

kmathew | ness_tech

copy 2016 Ness SES All Rights Reserved

As described earlier Scanners can be installed on streets

typically on street light poles to capture the Bluetooth and Wi-Fi

beacons that are coming from the vehicle and commuter smart

phones respectively

The number of Scanners will vary as they need to be placed in a

manner that increases the chance of detection

Having multiple Scanners also helps determine the vector of

vehiclecommuter movement

11

copy 2016 Ness SES All Rights Reserved12

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Computing the Data

kmathew | ness_tech

copy 2016 Ness SES All Rights Reserved

height

distance

12

The Scanner will provide a fair amount of data that needs to be computed and

aggregated before it is usable Some of the key points are

bull The Machine Address (MAC) will help uniquely define the commutervehicle

bull Received Signal Strength Indicator (RSSI) is used to calculate the distance

bull The scan from 2 Scanners on a given MAC will determine the direction vector

bull The Scanner mounted height is fixed and can be calibrated

bull The distance from the sensor and the direction of travel could be determined

based on the position of the vehicle

bull The vehicle MAC commuter(s) MAC could be correlated to determine driving

behavior during rush hour weekends and other commute times

bull The date and time should be synchronized to UTC to collate into a time series

database for correlation analysis

MAC

copy 2016 Ness SES All Rights Reserved13

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Analyzing the Bluetooth Data

kmathew | ness_tech

The captured data looks as indicated from the Bluetooth

data logs This reflects the Received Signal Strength

Indicator (RSSI) including time stamp vendor and a

service tag identifier (ID)

A RSSI closer to 0 means that the vehicle is closer and

a higher value means the vehicle is farther away

Using the class of device (cod) filter you can isolate the

captured frames that are most likely from vehicles

copy 2016 Ness SES All Rights Reserved14

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

WiFi Scans

kmathew | ness_tech

In addition to Bluetooth data another beacon captured

is the WiFi beacons sent from Smart Phones and

devices While the RSSI plays a key role in

determining the distance for a given commuters smart

phone (MAC) it required some fuzzy logic to extract

out the kind of smart device it is from the vendor data

copy 2016 Ness SES All Rights Reserved15

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Insights

RSSI (db)

In the visualization what is

interesting to observe is the

behavior of the swarm

One can see the changing

behavior with time progressing

This can help determine a

tipping point whether it is start

of rush hour or the end of one

These insights are valuable in

understanding commuter

behavior with real data that can

help city planners

Note This data is from one Scanner

copy 2016 Ness SES All Rights Reserved16

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

In Conclusion

kmathew | ness_tech

By using Swarm Intelligence (SI) algorithms such as Particle Swarm Optimization (PSO) city

planners can create simulations to understand potential congestion challenges based on how

vehicles and pedestrians navigate public spaces

PSO is a good algorithm to apply to large businesses in a city as it helps them understand the

behavior of each employee or a group of employees (beginningending of shifts) navigating out of

facilities and getting on streets by walking in vehicles using public transport etc

Simulations using real data collected through this mechanism can help city planners determine

potential traffic challenges at a highly-granular levelmdashby street intersection freeway ramp school

area etc mdash to significantly improve the quality of empirical commuter data used in street flow

planning and addressing existing congestion problems

copy 2016 Ness SES All Rights Reserved17

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

About Ness Software Engineering Services

Fully-integrated user experience design platform development

and data analytics services from visioning to execution

3000 colleagues | Engineering teamrsquos level of experience

exceeds industry-average

Teams designated for clients on ongoing basis | Engineers

commonly work with the same client for multiple years

10 Technology Innovation Centers across 6 countries

Product Engineering rigor is at the foundation of our

approach

Global Scale

Engineering Heritage

Integrated Solution

Design amp Development

Long-Term Client

Relationships

Experienced Personnel

copy 2016 Ness SES All Rights Reserved18

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Further Reading

bull The Evolution of the Connected Home (DATAQUEST)

bull Improving Predictive Maintenance with IoT (IoT Central)

bull Edge Analytics an Antidote to IoT Data Deluge (InformationWeek)

bull Healthcare Things Are Getting Better at Supporting Wellness (IoT Global Network)

bull Improve Loss Prevention in Retail Stores by Applying Swarm Intelligence (Indian Retailer)

bull Contextualising Data Will Help Monetize The Internet of Things (InformationAge)

bull Inside the Connected Carrsquos Ego Network (Auto Tech Review)

bull Ness Whitepaper Capitalizing on the Business Value of the Internet of Things

bull Ness Blog Predictions 2016 ndash IoT Payments amp Loyalty Programs APIs

bull Ness Blog Does EveryrsquoThingrsquo Matter in the Internet of Things

bull Ness Blog Internet of Things and Industrial Analytics

bull Ness Blog When Every Car Becomes a ldquoSmartrdquo Car

And a number of related readings on

bull Ness Insights httpwwwness-sescominsightsresource-library

bull Ness Blog httpwwwness-sescomcategoryblog

Here is a compilation of articles whitepapers and blog posts on IoT They have been presented and

published on various channels

copy 2016 Ness SES All Rights Reserved5

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Swarm Intelligence

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech5

ldquoA single ant or bee isnt smart but their colonies are The study of swarm

intelligence is providing insights that can help humans manage complex systemshelliprdquo

Source Swarm Theory By Peter Miller National Geographic Staff | httpngmnationalgeographiccom200707swarmsmiller-text

Source Wikipedia httpsenwikipediaorgwikiSwarm_intelligence

Simply put ldquoSwarm intelligence (SI) is the collective behavior

of decentralized self-organized systems natural or artificialrdquo

copy 2016 Ness SES All Rights Reserved6

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Opportunities for Smart Cities

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech6

Implement higher-tech methods such as swarm intelligence to form a more

accurate and complete picture of traffic flows so cities understand where

the real problems are

Apply this analysis to optimize traffic flow and continually monitor so

adjustments can be made more quickly to avoid the next Carmageddon

copy 2016 Ness SES All Rights Reserved7

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Understanding Traffic Flow PatternsUsing Particle Swarm Optimization

kmathew | ness_tech

copy 2016 Ness SES All Rights Reserved7

copy 2016 Ness SES All Rights Reserved8

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Scene of the Coronageddon

Source Google Maps 2016 Google Map of Corona California

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech8

In spite of the couple-day closure work continues on freeways and

adjacent roads and has been going on for a number of months This

aggravates commuters and contributes to a fair share of road rage

Using PSO and the Bees algorithm it is possible to understand and predict the behavior of the

commuters at different times of the day The changes of traffic patterns during the weekdays and

weekends provide insights that can help city planners plan for future street and freeway closures

copy 2016 Ness SES All Rights Reserved9

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Understanding Road Closures

Source Google Maps 2016 Google Map of Corona California

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech9

We can begin to better understand the traffic flow by tagging the beacon from the vehicle

andor the driver and passenger in the vehicle The effect of road closures that include streets

and ramps can be understood by analyzing the vehiclecommuter between 2 points on the

street

Installation of scanners along the streets can

capture Bluetooth and WiFi beacons of commutersrsquo

smart devices as well as the Bluetooth beacons

from vehicles

copy 2016 Ness SES All Rights Reserved10

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Applying the Bees Algorithm

Source Google Maps 2016 Google Map of Corona California

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech10

Applying the Bees algorithm to school traffic is an effective method to understand the traffic flow that is a

combination of foot traffic and vehicular traffic

Traffic as a result of the start of a school day

and dismissal will be an interesting pattern to

observe

For example at this school flow around certain intersection points

had delays but one intersection point was free flowing without

delays

copy 2016 Ness SES All Rights Reserved11

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Capturing Vehicle Data Using Scanners

kmathew | ness_tech

copy 2016 Ness SES All Rights Reserved

As described earlier Scanners can be installed on streets

typically on street light poles to capture the Bluetooth and Wi-Fi

beacons that are coming from the vehicle and commuter smart

phones respectively

The number of Scanners will vary as they need to be placed in a

manner that increases the chance of detection

Having multiple Scanners also helps determine the vector of

vehiclecommuter movement

11

copy 2016 Ness SES All Rights Reserved12

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Computing the Data

kmathew | ness_tech

copy 2016 Ness SES All Rights Reserved

height

distance

12

The Scanner will provide a fair amount of data that needs to be computed and

aggregated before it is usable Some of the key points are

bull The Machine Address (MAC) will help uniquely define the commutervehicle

bull Received Signal Strength Indicator (RSSI) is used to calculate the distance

bull The scan from 2 Scanners on a given MAC will determine the direction vector

bull The Scanner mounted height is fixed and can be calibrated

bull The distance from the sensor and the direction of travel could be determined

based on the position of the vehicle

bull The vehicle MAC commuter(s) MAC could be correlated to determine driving

behavior during rush hour weekends and other commute times

bull The date and time should be synchronized to UTC to collate into a time series

database for correlation analysis

MAC

copy 2016 Ness SES All Rights Reserved13

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Analyzing the Bluetooth Data

kmathew | ness_tech

The captured data looks as indicated from the Bluetooth

data logs This reflects the Received Signal Strength

Indicator (RSSI) including time stamp vendor and a

service tag identifier (ID)

A RSSI closer to 0 means that the vehicle is closer and

a higher value means the vehicle is farther away

Using the class of device (cod) filter you can isolate the

captured frames that are most likely from vehicles

copy 2016 Ness SES All Rights Reserved14

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

WiFi Scans

kmathew | ness_tech

In addition to Bluetooth data another beacon captured

is the WiFi beacons sent from Smart Phones and

devices While the RSSI plays a key role in

determining the distance for a given commuters smart

phone (MAC) it required some fuzzy logic to extract

out the kind of smart device it is from the vendor data

copy 2016 Ness SES All Rights Reserved15

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Insights

RSSI (db)

In the visualization what is

interesting to observe is the

behavior of the swarm

One can see the changing

behavior with time progressing

This can help determine a

tipping point whether it is start

of rush hour or the end of one

These insights are valuable in

understanding commuter

behavior with real data that can

help city planners

Note This data is from one Scanner

copy 2016 Ness SES All Rights Reserved16

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

In Conclusion

kmathew | ness_tech

By using Swarm Intelligence (SI) algorithms such as Particle Swarm Optimization (PSO) city

planners can create simulations to understand potential congestion challenges based on how

vehicles and pedestrians navigate public spaces

PSO is a good algorithm to apply to large businesses in a city as it helps them understand the

behavior of each employee or a group of employees (beginningending of shifts) navigating out of

facilities and getting on streets by walking in vehicles using public transport etc

Simulations using real data collected through this mechanism can help city planners determine

potential traffic challenges at a highly-granular levelmdashby street intersection freeway ramp school

area etc mdash to significantly improve the quality of empirical commuter data used in street flow

planning and addressing existing congestion problems

copy 2016 Ness SES All Rights Reserved17

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

About Ness Software Engineering Services

Fully-integrated user experience design platform development

and data analytics services from visioning to execution

3000 colleagues | Engineering teamrsquos level of experience

exceeds industry-average

Teams designated for clients on ongoing basis | Engineers

commonly work with the same client for multiple years

10 Technology Innovation Centers across 6 countries

Product Engineering rigor is at the foundation of our

approach

Global Scale

Engineering Heritage

Integrated Solution

Design amp Development

Long-Term Client

Relationships

Experienced Personnel

copy 2016 Ness SES All Rights Reserved18

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Further Reading

bull The Evolution of the Connected Home (DATAQUEST)

bull Improving Predictive Maintenance with IoT (IoT Central)

bull Edge Analytics an Antidote to IoT Data Deluge (InformationWeek)

bull Healthcare Things Are Getting Better at Supporting Wellness (IoT Global Network)

bull Improve Loss Prevention in Retail Stores by Applying Swarm Intelligence (Indian Retailer)

bull Contextualising Data Will Help Monetize The Internet of Things (InformationAge)

bull Inside the Connected Carrsquos Ego Network (Auto Tech Review)

bull Ness Whitepaper Capitalizing on the Business Value of the Internet of Things

bull Ness Blog Predictions 2016 ndash IoT Payments amp Loyalty Programs APIs

bull Ness Blog Does EveryrsquoThingrsquo Matter in the Internet of Things

bull Ness Blog Internet of Things and Industrial Analytics

bull Ness Blog When Every Car Becomes a ldquoSmartrdquo Car

And a number of related readings on

bull Ness Insights httpwwwness-sescominsightsresource-library

bull Ness Blog httpwwwness-sescomcategoryblog

Here is a compilation of articles whitepapers and blog posts on IoT They have been presented and

published on various channels

copy 2016 Ness SES All Rights Reserved6

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Opportunities for Smart Cities

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech6

Implement higher-tech methods such as swarm intelligence to form a more

accurate and complete picture of traffic flows so cities understand where

the real problems are

Apply this analysis to optimize traffic flow and continually monitor so

adjustments can be made more quickly to avoid the next Carmageddon

copy 2016 Ness SES All Rights Reserved7

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Understanding Traffic Flow PatternsUsing Particle Swarm Optimization

kmathew | ness_tech

copy 2016 Ness SES All Rights Reserved7

copy 2016 Ness SES All Rights Reserved8

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Scene of the Coronageddon

Source Google Maps 2016 Google Map of Corona California

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech8

In spite of the couple-day closure work continues on freeways and

adjacent roads and has been going on for a number of months This

aggravates commuters and contributes to a fair share of road rage

Using PSO and the Bees algorithm it is possible to understand and predict the behavior of the

commuters at different times of the day The changes of traffic patterns during the weekdays and

weekends provide insights that can help city planners plan for future street and freeway closures

copy 2016 Ness SES All Rights Reserved9

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Understanding Road Closures

Source Google Maps 2016 Google Map of Corona California

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech9

We can begin to better understand the traffic flow by tagging the beacon from the vehicle

andor the driver and passenger in the vehicle The effect of road closures that include streets

and ramps can be understood by analyzing the vehiclecommuter between 2 points on the

street

Installation of scanners along the streets can

capture Bluetooth and WiFi beacons of commutersrsquo

smart devices as well as the Bluetooth beacons

from vehicles

copy 2016 Ness SES All Rights Reserved10

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Applying the Bees Algorithm

Source Google Maps 2016 Google Map of Corona California

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech10

Applying the Bees algorithm to school traffic is an effective method to understand the traffic flow that is a

combination of foot traffic and vehicular traffic

Traffic as a result of the start of a school day

and dismissal will be an interesting pattern to

observe

For example at this school flow around certain intersection points

had delays but one intersection point was free flowing without

delays

copy 2016 Ness SES All Rights Reserved11

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Capturing Vehicle Data Using Scanners

kmathew | ness_tech

copy 2016 Ness SES All Rights Reserved

As described earlier Scanners can be installed on streets

typically on street light poles to capture the Bluetooth and Wi-Fi

beacons that are coming from the vehicle and commuter smart

phones respectively

The number of Scanners will vary as they need to be placed in a

manner that increases the chance of detection

Having multiple Scanners also helps determine the vector of

vehiclecommuter movement

11

copy 2016 Ness SES All Rights Reserved12

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Computing the Data

kmathew | ness_tech

copy 2016 Ness SES All Rights Reserved

height

distance

12

The Scanner will provide a fair amount of data that needs to be computed and

aggregated before it is usable Some of the key points are

bull The Machine Address (MAC) will help uniquely define the commutervehicle

bull Received Signal Strength Indicator (RSSI) is used to calculate the distance

bull The scan from 2 Scanners on a given MAC will determine the direction vector

bull The Scanner mounted height is fixed and can be calibrated

bull The distance from the sensor and the direction of travel could be determined

based on the position of the vehicle

bull The vehicle MAC commuter(s) MAC could be correlated to determine driving

behavior during rush hour weekends and other commute times

bull The date and time should be synchronized to UTC to collate into a time series

database for correlation analysis

MAC

copy 2016 Ness SES All Rights Reserved13

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Analyzing the Bluetooth Data

kmathew | ness_tech

The captured data looks as indicated from the Bluetooth

data logs This reflects the Received Signal Strength

Indicator (RSSI) including time stamp vendor and a

service tag identifier (ID)

A RSSI closer to 0 means that the vehicle is closer and

a higher value means the vehicle is farther away

Using the class of device (cod) filter you can isolate the

captured frames that are most likely from vehicles

copy 2016 Ness SES All Rights Reserved14

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

WiFi Scans

kmathew | ness_tech

In addition to Bluetooth data another beacon captured

is the WiFi beacons sent from Smart Phones and

devices While the RSSI plays a key role in

determining the distance for a given commuters smart

phone (MAC) it required some fuzzy logic to extract

out the kind of smart device it is from the vendor data

copy 2016 Ness SES All Rights Reserved15

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Insights

RSSI (db)

In the visualization what is

interesting to observe is the

behavior of the swarm

One can see the changing

behavior with time progressing

This can help determine a

tipping point whether it is start

of rush hour or the end of one

These insights are valuable in

understanding commuter

behavior with real data that can

help city planners

Note This data is from one Scanner

copy 2016 Ness SES All Rights Reserved16

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

In Conclusion

kmathew | ness_tech

By using Swarm Intelligence (SI) algorithms such as Particle Swarm Optimization (PSO) city

planners can create simulations to understand potential congestion challenges based on how

vehicles and pedestrians navigate public spaces

PSO is a good algorithm to apply to large businesses in a city as it helps them understand the

behavior of each employee or a group of employees (beginningending of shifts) navigating out of

facilities and getting on streets by walking in vehicles using public transport etc

Simulations using real data collected through this mechanism can help city planners determine

potential traffic challenges at a highly-granular levelmdashby street intersection freeway ramp school

area etc mdash to significantly improve the quality of empirical commuter data used in street flow

planning and addressing existing congestion problems

copy 2016 Ness SES All Rights Reserved17

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

About Ness Software Engineering Services

Fully-integrated user experience design platform development

and data analytics services from visioning to execution

3000 colleagues | Engineering teamrsquos level of experience

exceeds industry-average

Teams designated for clients on ongoing basis | Engineers

commonly work with the same client for multiple years

10 Technology Innovation Centers across 6 countries

Product Engineering rigor is at the foundation of our

approach

Global Scale

Engineering Heritage

Integrated Solution

Design amp Development

Long-Term Client

Relationships

Experienced Personnel

copy 2016 Ness SES All Rights Reserved18

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Further Reading

bull The Evolution of the Connected Home (DATAQUEST)

bull Improving Predictive Maintenance with IoT (IoT Central)

bull Edge Analytics an Antidote to IoT Data Deluge (InformationWeek)

bull Healthcare Things Are Getting Better at Supporting Wellness (IoT Global Network)

bull Improve Loss Prevention in Retail Stores by Applying Swarm Intelligence (Indian Retailer)

bull Contextualising Data Will Help Monetize The Internet of Things (InformationAge)

bull Inside the Connected Carrsquos Ego Network (Auto Tech Review)

bull Ness Whitepaper Capitalizing on the Business Value of the Internet of Things

bull Ness Blog Predictions 2016 ndash IoT Payments amp Loyalty Programs APIs

bull Ness Blog Does EveryrsquoThingrsquo Matter in the Internet of Things

bull Ness Blog Internet of Things and Industrial Analytics

bull Ness Blog When Every Car Becomes a ldquoSmartrdquo Car

And a number of related readings on

bull Ness Insights httpwwwness-sescominsightsresource-library

bull Ness Blog httpwwwness-sescomcategoryblog

Here is a compilation of articles whitepapers and blog posts on IoT They have been presented and

published on various channels

copy 2016 Ness SES All Rights Reserved7

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Understanding Traffic Flow PatternsUsing Particle Swarm Optimization

kmathew | ness_tech

copy 2016 Ness SES All Rights Reserved7

copy 2016 Ness SES All Rights Reserved8

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Scene of the Coronageddon

Source Google Maps 2016 Google Map of Corona California

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech8

In spite of the couple-day closure work continues on freeways and

adjacent roads and has been going on for a number of months This

aggravates commuters and contributes to a fair share of road rage

Using PSO and the Bees algorithm it is possible to understand and predict the behavior of the

commuters at different times of the day The changes of traffic patterns during the weekdays and

weekends provide insights that can help city planners plan for future street and freeway closures

copy 2016 Ness SES All Rights Reserved9

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Understanding Road Closures

Source Google Maps 2016 Google Map of Corona California

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech9

We can begin to better understand the traffic flow by tagging the beacon from the vehicle

andor the driver and passenger in the vehicle The effect of road closures that include streets

and ramps can be understood by analyzing the vehiclecommuter between 2 points on the

street

Installation of scanners along the streets can

capture Bluetooth and WiFi beacons of commutersrsquo

smart devices as well as the Bluetooth beacons

from vehicles

copy 2016 Ness SES All Rights Reserved10

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Applying the Bees Algorithm

Source Google Maps 2016 Google Map of Corona California

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech10

Applying the Bees algorithm to school traffic is an effective method to understand the traffic flow that is a

combination of foot traffic and vehicular traffic

Traffic as a result of the start of a school day

and dismissal will be an interesting pattern to

observe

For example at this school flow around certain intersection points

had delays but one intersection point was free flowing without

delays

copy 2016 Ness SES All Rights Reserved11

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Capturing Vehicle Data Using Scanners

kmathew | ness_tech

copy 2016 Ness SES All Rights Reserved

As described earlier Scanners can be installed on streets

typically on street light poles to capture the Bluetooth and Wi-Fi

beacons that are coming from the vehicle and commuter smart

phones respectively

The number of Scanners will vary as they need to be placed in a

manner that increases the chance of detection

Having multiple Scanners also helps determine the vector of

vehiclecommuter movement

11

copy 2016 Ness SES All Rights Reserved12

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Computing the Data

kmathew | ness_tech

copy 2016 Ness SES All Rights Reserved

height

distance

12

The Scanner will provide a fair amount of data that needs to be computed and

aggregated before it is usable Some of the key points are

bull The Machine Address (MAC) will help uniquely define the commutervehicle

bull Received Signal Strength Indicator (RSSI) is used to calculate the distance

bull The scan from 2 Scanners on a given MAC will determine the direction vector

bull The Scanner mounted height is fixed and can be calibrated

bull The distance from the sensor and the direction of travel could be determined

based on the position of the vehicle

bull The vehicle MAC commuter(s) MAC could be correlated to determine driving

behavior during rush hour weekends and other commute times

bull The date and time should be synchronized to UTC to collate into a time series

database for correlation analysis

MAC

copy 2016 Ness SES All Rights Reserved13

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Analyzing the Bluetooth Data

kmathew | ness_tech

The captured data looks as indicated from the Bluetooth

data logs This reflects the Received Signal Strength

Indicator (RSSI) including time stamp vendor and a

service tag identifier (ID)

A RSSI closer to 0 means that the vehicle is closer and

a higher value means the vehicle is farther away

Using the class of device (cod) filter you can isolate the

captured frames that are most likely from vehicles

copy 2016 Ness SES All Rights Reserved14

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

WiFi Scans

kmathew | ness_tech

In addition to Bluetooth data another beacon captured

is the WiFi beacons sent from Smart Phones and

devices While the RSSI plays a key role in

determining the distance for a given commuters smart

phone (MAC) it required some fuzzy logic to extract

out the kind of smart device it is from the vendor data

copy 2016 Ness SES All Rights Reserved15

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Insights

RSSI (db)

In the visualization what is

interesting to observe is the

behavior of the swarm

One can see the changing

behavior with time progressing

This can help determine a

tipping point whether it is start

of rush hour or the end of one

These insights are valuable in

understanding commuter

behavior with real data that can

help city planners

Note This data is from one Scanner

copy 2016 Ness SES All Rights Reserved16

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

In Conclusion

kmathew | ness_tech

By using Swarm Intelligence (SI) algorithms such as Particle Swarm Optimization (PSO) city

planners can create simulations to understand potential congestion challenges based on how

vehicles and pedestrians navigate public spaces

PSO is a good algorithm to apply to large businesses in a city as it helps them understand the

behavior of each employee or a group of employees (beginningending of shifts) navigating out of

facilities and getting on streets by walking in vehicles using public transport etc

Simulations using real data collected through this mechanism can help city planners determine

potential traffic challenges at a highly-granular levelmdashby street intersection freeway ramp school

area etc mdash to significantly improve the quality of empirical commuter data used in street flow

planning and addressing existing congestion problems

copy 2016 Ness SES All Rights Reserved17

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

About Ness Software Engineering Services

Fully-integrated user experience design platform development

and data analytics services from visioning to execution

3000 colleagues | Engineering teamrsquos level of experience

exceeds industry-average

Teams designated for clients on ongoing basis | Engineers

commonly work with the same client for multiple years

10 Technology Innovation Centers across 6 countries

Product Engineering rigor is at the foundation of our

approach

Global Scale

Engineering Heritage

Integrated Solution

Design amp Development

Long-Term Client

Relationships

Experienced Personnel

copy 2016 Ness SES All Rights Reserved18

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Further Reading

bull The Evolution of the Connected Home (DATAQUEST)

bull Improving Predictive Maintenance with IoT (IoT Central)

bull Edge Analytics an Antidote to IoT Data Deluge (InformationWeek)

bull Healthcare Things Are Getting Better at Supporting Wellness (IoT Global Network)

bull Improve Loss Prevention in Retail Stores by Applying Swarm Intelligence (Indian Retailer)

bull Contextualising Data Will Help Monetize The Internet of Things (InformationAge)

bull Inside the Connected Carrsquos Ego Network (Auto Tech Review)

bull Ness Whitepaper Capitalizing on the Business Value of the Internet of Things

bull Ness Blog Predictions 2016 ndash IoT Payments amp Loyalty Programs APIs

bull Ness Blog Does EveryrsquoThingrsquo Matter in the Internet of Things

bull Ness Blog Internet of Things and Industrial Analytics

bull Ness Blog When Every Car Becomes a ldquoSmartrdquo Car

And a number of related readings on

bull Ness Insights httpwwwness-sescominsightsresource-library

bull Ness Blog httpwwwness-sescomcategoryblog

Here is a compilation of articles whitepapers and blog posts on IoT They have been presented and

published on various channels

copy 2016 Ness SES All Rights Reserved8

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Scene of the Coronageddon

Source Google Maps 2016 Google Map of Corona California

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech8

In spite of the couple-day closure work continues on freeways and

adjacent roads and has been going on for a number of months This

aggravates commuters and contributes to a fair share of road rage

Using PSO and the Bees algorithm it is possible to understand and predict the behavior of the

commuters at different times of the day The changes of traffic patterns during the weekdays and

weekends provide insights that can help city planners plan for future street and freeway closures

copy 2016 Ness SES All Rights Reserved9

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Understanding Road Closures

Source Google Maps 2016 Google Map of Corona California

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech9

We can begin to better understand the traffic flow by tagging the beacon from the vehicle

andor the driver and passenger in the vehicle The effect of road closures that include streets

and ramps can be understood by analyzing the vehiclecommuter between 2 points on the

street

Installation of scanners along the streets can

capture Bluetooth and WiFi beacons of commutersrsquo

smart devices as well as the Bluetooth beacons

from vehicles

copy 2016 Ness SES All Rights Reserved10

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Applying the Bees Algorithm

Source Google Maps 2016 Google Map of Corona California

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech10

Applying the Bees algorithm to school traffic is an effective method to understand the traffic flow that is a

combination of foot traffic and vehicular traffic

Traffic as a result of the start of a school day

and dismissal will be an interesting pattern to

observe

For example at this school flow around certain intersection points

had delays but one intersection point was free flowing without

delays

copy 2016 Ness SES All Rights Reserved11

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Capturing Vehicle Data Using Scanners

kmathew | ness_tech

copy 2016 Ness SES All Rights Reserved

As described earlier Scanners can be installed on streets

typically on street light poles to capture the Bluetooth and Wi-Fi

beacons that are coming from the vehicle and commuter smart

phones respectively

The number of Scanners will vary as they need to be placed in a

manner that increases the chance of detection

Having multiple Scanners also helps determine the vector of

vehiclecommuter movement

11

copy 2016 Ness SES All Rights Reserved12

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Computing the Data

kmathew | ness_tech

copy 2016 Ness SES All Rights Reserved

height

distance

12

The Scanner will provide a fair amount of data that needs to be computed and

aggregated before it is usable Some of the key points are

bull The Machine Address (MAC) will help uniquely define the commutervehicle

bull Received Signal Strength Indicator (RSSI) is used to calculate the distance

bull The scan from 2 Scanners on a given MAC will determine the direction vector

bull The Scanner mounted height is fixed and can be calibrated

bull The distance from the sensor and the direction of travel could be determined

based on the position of the vehicle

bull The vehicle MAC commuter(s) MAC could be correlated to determine driving

behavior during rush hour weekends and other commute times

bull The date and time should be synchronized to UTC to collate into a time series

database for correlation analysis

MAC

copy 2016 Ness SES All Rights Reserved13

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Analyzing the Bluetooth Data

kmathew | ness_tech

The captured data looks as indicated from the Bluetooth

data logs This reflects the Received Signal Strength

Indicator (RSSI) including time stamp vendor and a

service tag identifier (ID)

A RSSI closer to 0 means that the vehicle is closer and

a higher value means the vehicle is farther away

Using the class of device (cod) filter you can isolate the

captured frames that are most likely from vehicles

copy 2016 Ness SES All Rights Reserved14

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

WiFi Scans

kmathew | ness_tech

In addition to Bluetooth data another beacon captured

is the WiFi beacons sent from Smart Phones and

devices While the RSSI plays a key role in

determining the distance for a given commuters smart

phone (MAC) it required some fuzzy logic to extract

out the kind of smart device it is from the vendor data

copy 2016 Ness SES All Rights Reserved15

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Insights

RSSI (db)

In the visualization what is

interesting to observe is the

behavior of the swarm

One can see the changing

behavior with time progressing

This can help determine a

tipping point whether it is start

of rush hour or the end of one

These insights are valuable in

understanding commuter

behavior with real data that can

help city planners

Note This data is from one Scanner

copy 2016 Ness SES All Rights Reserved16

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

In Conclusion

kmathew | ness_tech

By using Swarm Intelligence (SI) algorithms such as Particle Swarm Optimization (PSO) city

planners can create simulations to understand potential congestion challenges based on how

vehicles and pedestrians navigate public spaces

PSO is a good algorithm to apply to large businesses in a city as it helps them understand the

behavior of each employee or a group of employees (beginningending of shifts) navigating out of

facilities and getting on streets by walking in vehicles using public transport etc

Simulations using real data collected through this mechanism can help city planners determine

potential traffic challenges at a highly-granular levelmdashby street intersection freeway ramp school

area etc mdash to significantly improve the quality of empirical commuter data used in street flow

planning and addressing existing congestion problems

copy 2016 Ness SES All Rights Reserved17

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

About Ness Software Engineering Services

Fully-integrated user experience design platform development

and data analytics services from visioning to execution

3000 colleagues | Engineering teamrsquos level of experience

exceeds industry-average

Teams designated for clients on ongoing basis | Engineers

commonly work with the same client for multiple years

10 Technology Innovation Centers across 6 countries

Product Engineering rigor is at the foundation of our

approach

Global Scale

Engineering Heritage

Integrated Solution

Design amp Development

Long-Term Client

Relationships

Experienced Personnel

copy 2016 Ness SES All Rights Reserved18

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Further Reading

bull The Evolution of the Connected Home (DATAQUEST)

bull Improving Predictive Maintenance with IoT (IoT Central)

bull Edge Analytics an Antidote to IoT Data Deluge (InformationWeek)

bull Healthcare Things Are Getting Better at Supporting Wellness (IoT Global Network)

bull Improve Loss Prevention in Retail Stores by Applying Swarm Intelligence (Indian Retailer)

bull Contextualising Data Will Help Monetize The Internet of Things (InformationAge)

bull Inside the Connected Carrsquos Ego Network (Auto Tech Review)

bull Ness Whitepaper Capitalizing on the Business Value of the Internet of Things

bull Ness Blog Predictions 2016 ndash IoT Payments amp Loyalty Programs APIs

bull Ness Blog Does EveryrsquoThingrsquo Matter in the Internet of Things

bull Ness Blog Internet of Things and Industrial Analytics

bull Ness Blog When Every Car Becomes a ldquoSmartrdquo Car

And a number of related readings on

bull Ness Insights httpwwwness-sescominsightsresource-library

bull Ness Blog httpwwwness-sescomcategoryblog

Here is a compilation of articles whitepapers and blog posts on IoT They have been presented and

published on various channels

copy 2016 Ness SES All Rights Reserved9

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Understanding Road Closures

Source Google Maps 2016 Google Map of Corona California

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech9

We can begin to better understand the traffic flow by tagging the beacon from the vehicle

andor the driver and passenger in the vehicle The effect of road closures that include streets

and ramps can be understood by analyzing the vehiclecommuter between 2 points on the

street

Installation of scanners along the streets can

capture Bluetooth and WiFi beacons of commutersrsquo

smart devices as well as the Bluetooth beacons

from vehicles

copy 2016 Ness SES All Rights Reserved10

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Applying the Bees Algorithm

Source Google Maps 2016 Google Map of Corona California

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech10

Applying the Bees algorithm to school traffic is an effective method to understand the traffic flow that is a

combination of foot traffic and vehicular traffic

Traffic as a result of the start of a school day

and dismissal will be an interesting pattern to

observe

For example at this school flow around certain intersection points

had delays but one intersection point was free flowing without

delays

copy 2016 Ness SES All Rights Reserved11

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Capturing Vehicle Data Using Scanners

kmathew | ness_tech

copy 2016 Ness SES All Rights Reserved

As described earlier Scanners can be installed on streets

typically on street light poles to capture the Bluetooth and Wi-Fi

beacons that are coming from the vehicle and commuter smart

phones respectively

The number of Scanners will vary as they need to be placed in a

manner that increases the chance of detection

Having multiple Scanners also helps determine the vector of

vehiclecommuter movement

11

copy 2016 Ness SES All Rights Reserved12

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Computing the Data

kmathew | ness_tech

copy 2016 Ness SES All Rights Reserved

height

distance

12

The Scanner will provide a fair amount of data that needs to be computed and

aggregated before it is usable Some of the key points are

bull The Machine Address (MAC) will help uniquely define the commutervehicle

bull Received Signal Strength Indicator (RSSI) is used to calculate the distance

bull The scan from 2 Scanners on a given MAC will determine the direction vector

bull The Scanner mounted height is fixed and can be calibrated

bull The distance from the sensor and the direction of travel could be determined

based on the position of the vehicle

bull The vehicle MAC commuter(s) MAC could be correlated to determine driving

behavior during rush hour weekends and other commute times

bull The date and time should be synchronized to UTC to collate into a time series

database for correlation analysis

MAC

copy 2016 Ness SES All Rights Reserved13

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Analyzing the Bluetooth Data

kmathew | ness_tech

The captured data looks as indicated from the Bluetooth

data logs This reflects the Received Signal Strength

Indicator (RSSI) including time stamp vendor and a

service tag identifier (ID)

A RSSI closer to 0 means that the vehicle is closer and

a higher value means the vehicle is farther away

Using the class of device (cod) filter you can isolate the

captured frames that are most likely from vehicles

copy 2016 Ness SES All Rights Reserved14

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

WiFi Scans

kmathew | ness_tech

In addition to Bluetooth data another beacon captured

is the WiFi beacons sent from Smart Phones and

devices While the RSSI plays a key role in

determining the distance for a given commuters smart

phone (MAC) it required some fuzzy logic to extract

out the kind of smart device it is from the vendor data

copy 2016 Ness SES All Rights Reserved15

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Insights

RSSI (db)

In the visualization what is

interesting to observe is the

behavior of the swarm

One can see the changing

behavior with time progressing

This can help determine a

tipping point whether it is start

of rush hour or the end of one

These insights are valuable in

understanding commuter

behavior with real data that can

help city planners

Note This data is from one Scanner

copy 2016 Ness SES All Rights Reserved16

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

In Conclusion

kmathew | ness_tech

By using Swarm Intelligence (SI) algorithms such as Particle Swarm Optimization (PSO) city

planners can create simulations to understand potential congestion challenges based on how

vehicles and pedestrians navigate public spaces

PSO is a good algorithm to apply to large businesses in a city as it helps them understand the

behavior of each employee or a group of employees (beginningending of shifts) navigating out of

facilities and getting on streets by walking in vehicles using public transport etc

Simulations using real data collected through this mechanism can help city planners determine

potential traffic challenges at a highly-granular levelmdashby street intersection freeway ramp school

area etc mdash to significantly improve the quality of empirical commuter data used in street flow

planning and addressing existing congestion problems

copy 2016 Ness SES All Rights Reserved17

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

About Ness Software Engineering Services

Fully-integrated user experience design platform development

and data analytics services from visioning to execution

3000 colleagues | Engineering teamrsquos level of experience

exceeds industry-average

Teams designated for clients on ongoing basis | Engineers

commonly work with the same client for multiple years

10 Technology Innovation Centers across 6 countries

Product Engineering rigor is at the foundation of our

approach

Global Scale

Engineering Heritage

Integrated Solution

Design amp Development

Long-Term Client

Relationships

Experienced Personnel

copy 2016 Ness SES All Rights Reserved18

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Further Reading

bull The Evolution of the Connected Home (DATAQUEST)

bull Improving Predictive Maintenance with IoT (IoT Central)

bull Edge Analytics an Antidote to IoT Data Deluge (InformationWeek)

bull Healthcare Things Are Getting Better at Supporting Wellness (IoT Global Network)

bull Improve Loss Prevention in Retail Stores by Applying Swarm Intelligence (Indian Retailer)

bull Contextualising Data Will Help Monetize The Internet of Things (InformationAge)

bull Inside the Connected Carrsquos Ego Network (Auto Tech Review)

bull Ness Whitepaper Capitalizing on the Business Value of the Internet of Things

bull Ness Blog Predictions 2016 ndash IoT Payments amp Loyalty Programs APIs

bull Ness Blog Does EveryrsquoThingrsquo Matter in the Internet of Things

bull Ness Blog Internet of Things and Industrial Analytics

bull Ness Blog When Every Car Becomes a ldquoSmartrdquo Car

And a number of related readings on

bull Ness Insights httpwwwness-sescominsightsresource-library

bull Ness Blog httpwwwness-sescomcategoryblog

Here is a compilation of articles whitepapers and blog posts on IoT They have been presented and

published on various channels

copy 2016 Ness SES All Rights Reserved10

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Applying the Bees Algorithm

Source Google Maps 2016 Google Map of Corona California

copy 2016 Ness SES All Rights Reserved

kmathew | ness_tech10

Applying the Bees algorithm to school traffic is an effective method to understand the traffic flow that is a

combination of foot traffic and vehicular traffic

Traffic as a result of the start of a school day

and dismissal will be an interesting pattern to

observe

For example at this school flow around certain intersection points

had delays but one intersection point was free flowing without

delays

copy 2016 Ness SES All Rights Reserved11

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Capturing Vehicle Data Using Scanners

kmathew | ness_tech

copy 2016 Ness SES All Rights Reserved

As described earlier Scanners can be installed on streets

typically on street light poles to capture the Bluetooth and Wi-Fi

beacons that are coming from the vehicle and commuter smart

phones respectively

The number of Scanners will vary as they need to be placed in a

manner that increases the chance of detection

Having multiple Scanners also helps determine the vector of

vehiclecommuter movement

11

copy 2016 Ness SES All Rights Reserved12

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Computing the Data

kmathew | ness_tech

copy 2016 Ness SES All Rights Reserved

height

distance

12

The Scanner will provide a fair amount of data that needs to be computed and

aggregated before it is usable Some of the key points are

bull The Machine Address (MAC) will help uniquely define the commutervehicle

bull Received Signal Strength Indicator (RSSI) is used to calculate the distance

bull The scan from 2 Scanners on a given MAC will determine the direction vector

bull The Scanner mounted height is fixed and can be calibrated

bull The distance from the sensor and the direction of travel could be determined

based on the position of the vehicle

bull The vehicle MAC commuter(s) MAC could be correlated to determine driving

behavior during rush hour weekends and other commute times

bull The date and time should be synchronized to UTC to collate into a time series

database for correlation analysis

MAC

copy 2016 Ness SES All Rights Reserved13

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Analyzing the Bluetooth Data

kmathew | ness_tech

The captured data looks as indicated from the Bluetooth

data logs This reflects the Received Signal Strength

Indicator (RSSI) including time stamp vendor and a

service tag identifier (ID)

A RSSI closer to 0 means that the vehicle is closer and

a higher value means the vehicle is farther away

Using the class of device (cod) filter you can isolate the

captured frames that are most likely from vehicles

copy 2016 Ness SES All Rights Reserved14

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

WiFi Scans

kmathew | ness_tech

In addition to Bluetooth data another beacon captured

is the WiFi beacons sent from Smart Phones and

devices While the RSSI plays a key role in

determining the distance for a given commuters smart

phone (MAC) it required some fuzzy logic to extract

out the kind of smart device it is from the vendor data

copy 2016 Ness SES All Rights Reserved15

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Insights

RSSI (db)

In the visualization what is

interesting to observe is the

behavior of the swarm

One can see the changing

behavior with time progressing

This can help determine a

tipping point whether it is start

of rush hour or the end of one

These insights are valuable in

understanding commuter

behavior with real data that can

help city planners

Note This data is from one Scanner

copy 2016 Ness SES All Rights Reserved16

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

In Conclusion

kmathew | ness_tech

By using Swarm Intelligence (SI) algorithms such as Particle Swarm Optimization (PSO) city

planners can create simulations to understand potential congestion challenges based on how

vehicles and pedestrians navigate public spaces

PSO is a good algorithm to apply to large businesses in a city as it helps them understand the

behavior of each employee or a group of employees (beginningending of shifts) navigating out of

facilities and getting on streets by walking in vehicles using public transport etc

Simulations using real data collected through this mechanism can help city planners determine

potential traffic challenges at a highly-granular levelmdashby street intersection freeway ramp school

area etc mdash to significantly improve the quality of empirical commuter data used in street flow

planning and addressing existing congestion problems

copy 2016 Ness SES All Rights Reserved17

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

About Ness Software Engineering Services

Fully-integrated user experience design platform development

and data analytics services from visioning to execution

3000 colleagues | Engineering teamrsquos level of experience

exceeds industry-average

Teams designated for clients on ongoing basis | Engineers

commonly work with the same client for multiple years

10 Technology Innovation Centers across 6 countries

Product Engineering rigor is at the foundation of our

approach

Global Scale

Engineering Heritage

Integrated Solution

Design amp Development

Long-Term Client

Relationships

Experienced Personnel

copy 2016 Ness SES All Rights Reserved18

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Further Reading

bull The Evolution of the Connected Home (DATAQUEST)

bull Improving Predictive Maintenance with IoT (IoT Central)

bull Edge Analytics an Antidote to IoT Data Deluge (InformationWeek)

bull Healthcare Things Are Getting Better at Supporting Wellness (IoT Global Network)

bull Improve Loss Prevention in Retail Stores by Applying Swarm Intelligence (Indian Retailer)

bull Contextualising Data Will Help Monetize The Internet of Things (InformationAge)

bull Inside the Connected Carrsquos Ego Network (Auto Tech Review)

bull Ness Whitepaper Capitalizing on the Business Value of the Internet of Things

bull Ness Blog Predictions 2016 ndash IoT Payments amp Loyalty Programs APIs

bull Ness Blog Does EveryrsquoThingrsquo Matter in the Internet of Things

bull Ness Blog Internet of Things and Industrial Analytics

bull Ness Blog When Every Car Becomes a ldquoSmartrdquo Car

And a number of related readings on

bull Ness Insights httpwwwness-sescominsightsresource-library

bull Ness Blog httpwwwness-sescomcategoryblog

Here is a compilation of articles whitepapers and blog posts on IoT They have been presented and

published on various channels

copy 2016 Ness SES All Rights Reserved11

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Capturing Vehicle Data Using Scanners

kmathew | ness_tech

copy 2016 Ness SES All Rights Reserved

As described earlier Scanners can be installed on streets

typically on street light poles to capture the Bluetooth and Wi-Fi

beacons that are coming from the vehicle and commuter smart

phones respectively

The number of Scanners will vary as they need to be placed in a

manner that increases the chance of detection

Having multiple Scanners also helps determine the vector of

vehiclecommuter movement

11

copy 2016 Ness SES All Rights Reserved12

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Computing the Data

kmathew | ness_tech

copy 2016 Ness SES All Rights Reserved

height

distance

12

The Scanner will provide a fair amount of data that needs to be computed and

aggregated before it is usable Some of the key points are

bull The Machine Address (MAC) will help uniquely define the commutervehicle

bull Received Signal Strength Indicator (RSSI) is used to calculate the distance

bull The scan from 2 Scanners on a given MAC will determine the direction vector

bull The Scanner mounted height is fixed and can be calibrated

bull The distance from the sensor and the direction of travel could be determined

based on the position of the vehicle

bull The vehicle MAC commuter(s) MAC could be correlated to determine driving

behavior during rush hour weekends and other commute times

bull The date and time should be synchronized to UTC to collate into a time series

database for correlation analysis

MAC

copy 2016 Ness SES All Rights Reserved13

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Analyzing the Bluetooth Data

kmathew | ness_tech

The captured data looks as indicated from the Bluetooth

data logs This reflects the Received Signal Strength

Indicator (RSSI) including time stamp vendor and a

service tag identifier (ID)

A RSSI closer to 0 means that the vehicle is closer and

a higher value means the vehicle is farther away

Using the class of device (cod) filter you can isolate the

captured frames that are most likely from vehicles

copy 2016 Ness SES All Rights Reserved14

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

WiFi Scans

kmathew | ness_tech

In addition to Bluetooth data another beacon captured

is the WiFi beacons sent from Smart Phones and

devices While the RSSI plays a key role in

determining the distance for a given commuters smart

phone (MAC) it required some fuzzy logic to extract

out the kind of smart device it is from the vendor data

copy 2016 Ness SES All Rights Reserved15

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Insights

RSSI (db)

In the visualization what is

interesting to observe is the

behavior of the swarm

One can see the changing

behavior with time progressing

This can help determine a

tipping point whether it is start

of rush hour or the end of one

These insights are valuable in

understanding commuter

behavior with real data that can

help city planners

Note This data is from one Scanner

copy 2016 Ness SES All Rights Reserved16

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

In Conclusion

kmathew | ness_tech

By using Swarm Intelligence (SI) algorithms such as Particle Swarm Optimization (PSO) city

planners can create simulations to understand potential congestion challenges based on how

vehicles and pedestrians navigate public spaces

PSO is a good algorithm to apply to large businesses in a city as it helps them understand the

behavior of each employee or a group of employees (beginningending of shifts) navigating out of

facilities and getting on streets by walking in vehicles using public transport etc

Simulations using real data collected through this mechanism can help city planners determine

potential traffic challenges at a highly-granular levelmdashby street intersection freeway ramp school

area etc mdash to significantly improve the quality of empirical commuter data used in street flow

planning and addressing existing congestion problems

copy 2016 Ness SES All Rights Reserved17

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

About Ness Software Engineering Services

Fully-integrated user experience design platform development

and data analytics services from visioning to execution

3000 colleagues | Engineering teamrsquos level of experience

exceeds industry-average

Teams designated for clients on ongoing basis | Engineers

commonly work with the same client for multiple years

10 Technology Innovation Centers across 6 countries

Product Engineering rigor is at the foundation of our

approach

Global Scale

Engineering Heritage

Integrated Solution

Design amp Development

Long-Term Client

Relationships

Experienced Personnel

copy 2016 Ness SES All Rights Reserved18

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Further Reading

bull The Evolution of the Connected Home (DATAQUEST)

bull Improving Predictive Maintenance with IoT (IoT Central)

bull Edge Analytics an Antidote to IoT Data Deluge (InformationWeek)

bull Healthcare Things Are Getting Better at Supporting Wellness (IoT Global Network)

bull Improve Loss Prevention in Retail Stores by Applying Swarm Intelligence (Indian Retailer)

bull Contextualising Data Will Help Monetize The Internet of Things (InformationAge)

bull Inside the Connected Carrsquos Ego Network (Auto Tech Review)

bull Ness Whitepaper Capitalizing on the Business Value of the Internet of Things

bull Ness Blog Predictions 2016 ndash IoT Payments amp Loyalty Programs APIs

bull Ness Blog Does EveryrsquoThingrsquo Matter in the Internet of Things

bull Ness Blog Internet of Things and Industrial Analytics

bull Ness Blog When Every Car Becomes a ldquoSmartrdquo Car

And a number of related readings on

bull Ness Insights httpwwwness-sescominsightsresource-library

bull Ness Blog httpwwwness-sescomcategoryblog

Here is a compilation of articles whitepapers and blog posts on IoT They have been presented and

published on various channels

copy 2016 Ness SES All Rights Reserved12

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Computing the Data

kmathew | ness_tech

copy 2016 Ness SES All Rights Reserved

height

distance

12

The Scanner will provide a fair amount of data that needs to be computed and

aggregated before it is usable Some of the key points are

bull The Machine Address (MAC) will help uniquely define the commutervehicle

bull Received Signal Strength Indicator (RSSI) is used to calculate the distance

bull The scan from 2 Scanners on a given MAC will determine the direction vector

bull The Scanner mounted height is fixed and can be calibrated

bull The distance from the sensor and the direction of travel could be determined

based on the position of the vehicle

bull The vehicle MAC commuter(s) MAC could be correlated to determine driving

behavior during rush hour weekends and other commute times

bull The date and time should be synchronized to UTC to collate into a time series

database for correlation analysis

MAC

copy 2016 Ness SES All Rights Reserved13

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Analyzing the Bluetooth Data

kmathew | ness_tech

The captured data looks as indicated from the Bluetooth

data logs This reflects the Received Signal Strength

Indicator (RSSI) including time stamp vendor and a

service tag identifier (ID)

A RSSI closer to 0 means that the vehicle is closer and

a higher value means the vehicle is farther away

Using the class of device (cod) filter you can isolate the

captured frames that are most likely from vehicles

copy 2016 Ness SES All Rights Reserved14

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

WiFi Scans

kmathew | ness_tech

In addition to Bluetooth data another beacon captured

is the WiFi beacons sent from Smart Phones and

devices While the RSSI plays a key role in

determining the distance for a given commuters smart

phone (MAC) it required some fuzzy logic to extract

out the kind of smart device it is from the vendor data

copy 2016 Ness SES All Rights Reserved15

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Insights

RSSI (db)

In the visualization what is

interesting to observe is the

behavior of the swarm

One can see the changing

behavior with time progressing

This can help determine a

tipping point whether it is start

of rush hour or the end of one

These insights are valuable in

understanding commuter

behavior with real data that can

help city planners

Note This data is from one Scanner

copy 2016 Ness SES All Rights Reserved16

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

In Conclusion

kmathew | ness_tech

By using Swarm Intelligence (SI) algorithms such as Particle Swarm Optimization (PSO) city

planners can create simulations to understand potential congestion challenges based on how

vehicles and pedestrians navigate public spaces

PSO is a good algorithm to apply to large businesses in a city as it helps them understand the

behavior of each employee or a group of employees (beginningending of shifts) navigating out of

facilities and getting on streets by walking in vehicles using public transport etc

Simulations using real data collected through this mechanism can help city planners determine

potential traffic challenges at a highly-granular levelmdashby street intersection freeway ramp school

area etc mdash to significantly improve the quality of empirical commuter data used in street flow

planning and addressing existing congestion problems

copy 2016 Ness SES All Rights Reserved17

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

About Ness Software Engineering Services

Fully-integrated user experience design platform development

and data analytics services from visioning to execution

3000 colleagues | Engineering teamrsquos level of experience

exceeds industry-average

Teams designated for clients on ongoing basis | Engineers

commonly work with the same client for multiple years

10 Technology Innovation Centers across 6 countries

Product Engineering rigor is at the foundation of our

approach

Global Scale

Engineering Heritage

Integrated Solution

Design amp Development

Long-Term Client

Relationships

Experienced Personnel

copy 2016 Ness SES All Rights Reserved18

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Further Reading

bull The Evolution of the Connected Home (DATAQUEST)

bull Improving Predictive Maintenance with IoT (IoT Central)

bull Edge Analytics an Antidote to IoT Data Deluge (InformationWeek)

bull Healthcare Things Are Getting Better at Supporting Wellness (IoT Global Network)

bull Improve Loss Prevention in Retail Stores by Applying Swarm Intelligence (Indian Retailer)

bull Contextualising Data Will Help Monetize The Internet of Things (InformationAge)

bull Inside the Connected Carrsquos Ego Network (Auto Tech Review)

bull Ness Whitepaper Capitalizing on the Business Value of the Internet of Things

bull Ness Blog Predictions 2016 ndash IoT Payments amp Loyalty Programs APIs

bull Ness Blog Does EveryrsquoThingrsquo Matter in the Internet of Things

bull Ness Blog Internet of Things and Industrial Analytics

bull Ness Blog When Every Car Becomes a ldquoSmartrdquo Car

And a number of related readings on

bull Ness Insights httpwwwness-sescominsightsresource-library

bull Ness Blog httpwwwness-sescomcategoryblog

Here is a compilation of articles whitepapers and blog posts on IoT They have been presented and

published on various channels

copy 2016 Ness SES All Rights Reserved13

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Analyzing the Bluetooth Data

kmathew | ness_tech

The captured data looks as indicated from the Bluetooth

data logs This reflects the Received Signal Strength

Indicator (RSSI) including time stamp vendor and a

service tag identifier (ID)

A RSSI closer to 0 means that the vehicle is closer and

a higher value means the vehicle is farther away

Using the class of device (cod) filter you can isolate the

captured frames that are most likely from vehicles

copy 2016 Ness SES All Rights Reserved14

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

WiFi Scans

kmathew | ness_tech

In addition to Bluetooth data another beacon captured

is the WiFi beacons sent from Smart Phones and

devices While the RSSI plays a key role in

determining the distance for a given commuters smart

phone (MAC) it required some fuzzy logic to extract

out the kind of smart device it is from the vendor data

copy 2016 Ness SES All Rights Reserved15

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Insights

RSSI (db)

In the visualization what is

interesting to observe is the

behavior of the swarm

One can see the changing

behavior with time progressing

This can help determine a

tipping point whether it is start

of rush hour or the end of one

These insights are valuable in

understanding commuter

behavior with real data that can

help city planners

Note This data is from one Scanner

copy 2016 Ness SES All Rights Reserved16

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

In Conclusion

kmathew | ness_tech

By using Swarm Intelligence (SI) algorithms such as Particle Swarm Optimization (PSO) city

planners can create simulations to understand potential congestion challenges based on how

vehicles and pedestrians navigate public spaces

PSO is a good algorithm to apply to large businesses in a city as it helps them understand the

behavior of each employee or a group of employees (beginningending of shifts) navigating out of

facilities and getting on streets by walking in vehicles using public transport etc

Simulations using real data collected through this mechanism can help city planners determine

potential traffic challenges at a highly-granular levelmdashby street intersection freeway ramp school

area etc mdash to significantly improve the quality of empirical commuter data used in street flow

planning and addressing existing congestion problems

copy 2016 Ness SES All Rights Reserved17

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

About Ness Software Engineering Services

Fully-integrated user experience design platform development

and data analytics services from visioning to execution

3000 colleagues | Engineering teamrsquos level of experience

exceeds industry-average

Teams designated for clients on ongoing basis | Engineers

commonly work with the same client for multiple years

10 Technology Innovation Centers across 6 countries

Product Engineering rigor is at the foundation of our

approach

Global Scale

Engineering Heritage

Integrated Solution

Design amp Development

Long-Term Client

Relationships

Experienced Personnel

copy 2016 Ness SES All Rights Reserved18

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Further Reading

bull The Evolution of the Connected Home (DATAQUEST)

bull Improving Predictive Maintenance with IoT (IoT Central)

bull Edge Analytics an Antidote to IoT Data Deluge (InformationWeek)

bull Healthcare Things Are Getting Better at Supporting Wellness (IoT Global Network)

bull Improve Loss Prevention in Retail Stores by Applying Swarm Intelligence (Indian Retailer)

bull Contextualising Data Will Help Monetize The Internet of Things (InformationAge)

bull Inside the Connected Carrsquos Ego Network (Auto Tech Review)

bull Ness Whitepaper Capitalizing on the Business Value of the Internet of Things

bull Ness Blog Predictions 2016 ndash IoT Payments amp Loyalty Programs APIs

bull Ness Blog Does EveryrsquoThingrsquo Matter in the Internet of Things

bull Ness Blog Internet of Things and Industrial Analytics

bull Ness Blog When Every Car Becomes a ldquoSmartrdquo Car

And a number of related readings on

bull Ness Insights httpwwwness-sescominsightsresource-library

bull Ness Blog httpwwwness-sescomcategoryblog

Here is a compilation of articles whitepapers and blog posts on IoT They have been presented and

published on various channels

copy 2016 Ness SES All Rights Reserved14

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

WiFi Scans

kmathew | ness_tech

In addition to Bluetooth data another beacon captured

is the WiFi beacons sent from Smart Phones and

devices While the RSSI plays a key role in

determining the distance for a given commuters smart

phone (MAC) it required some fuzzy logic to extract

out the kind of smart device it is from the vendor data

copy 2016 Ness SES All Rights Reserved15

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Insights

RSSI (db)

In the visualization what is

interesting to observe is the

behavior of the swarm

One can see the changing

behavior with time progressing

This can help determine a

tipping point whether it is start

of rush hour or the end of one

These insights are valuable in

understanding commuter

behavior with real data that can

help city planners

Note This data is from one Scanner

copy 2016 Ness SES All Rights Reserved16

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

In Conclusion

kmathew | ness_tech

By using Swarm Intelligence (SI) algorithms such as Particle Swarm Optimization (PSO) city

planners can create simulations to understand potential congestion challenges based on how

vehicles and pedestrians navigate public spaces

PSO is a good algorithm to apply to large businesses in a city as it helps them understand the

behavior of each employee or a group of employees (beginningending of shifts) navigating out of

facilities and getting on streets by walking in vehicles using public transport etc

Simulations using real data collected through this mechanism can help city planners determine

potential traffic challenges at a highly-granular levelmdashby street intersection freeway ramp school

area etc mdash to significantly improve the quality of empirical commuter data used in street flow

planning and addressing existing congestion problems

copy 2016 Ness SES All Rights Reserved17

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

About Ness Software Engineering Services

Fully-integrated user experience design platform development

and data analytics services from visioning to execution

3000 colleagues | Engineering teamrsquos level of experience

exceeds industry-average

Teams designated for clients on ongoing basis | Engineers

commonly work with the same client for multiple years

10 Technology Innovation Centers across 6 countries

Product Engineering rigor is at the foundation of our

approach

Global Scale

Engineering Heritage

Integrated Solution

Design amp Development

Long-Term Client

Relationships

Experienced Personnel

copy 2016 Ness SES All Rights Reserved18

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Further Reading

bull The Evolution of the Connected Home (DATAQUEST)

bull Improving Predictive Maintenance with IoT (IoT Central)

bull Edge Analytics an Antidote to IoT Data Deluge (InformationWeek)

bull Healthcare Things Are Getting Better at Supporting Wellness (IoT Global Network)

bull Improve Loss Prevention in Retail Stores by Applying Swarm Intelligence (Indian Retailer)

bull Contextualising Data Will Help Monetize The Internet of Things (InformationAge)

bull Inside the Connected Carrsquos Ego Network (Auto Tech Review)

bull Ness Whitepaper Capitalizing on the Business Value of the Internet of Things

bull Ness Blog Predictions 2016 ndash IoT Payments amp Loyalty Programs APIs

bull Ness Blog Does EveryrsquoThingrsquo Matter in the Internet of Things

bull Ness Blog Internet of Things and Industrial Analytics

bull Ness Blog When Every Car Becomes a ldquoSmartrdquo Car

And a number of related readings on

bull Ness Insights httpwwwness-sescominsightsresource-library

bull Ness Blog httpwwwness-sescomcategoryblog

Here is a compilation of articles whitepapers and blog posts on IoT They have been presented and

published on various channels

copy 2016 Ness SES All Rights Reserved15

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Insights

RSSI (db)

In the visualization what is

interesting to observe is the

behavior of the swarm

One can see the changing

behavior with time progressing

This can help determine a

tipping point whether it is start

of rush hour or the end of one

These insights are valuable in

understanding commuter

behavior with real data that can

help city planners

Note This data is from one Scanner

copy 2016 Ness SES All Rights Reserved16

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

In Conclusion

kmathew | ness_tech

By using Swarm Intelligence (SI) algorithms such as Particle Swarm Optimization (PSO) city

planners can create simulations to understand potential congestion challenges based on how

vehicles and pedestrians navigate public spaces

PSO is a good algorithm to apply to large businesses in a city as it helps them understand the

behavior of each employee or a group of employees (beginningending of shifts) navigating out of

facilities and getting on streets by walking in vehicles using public transport etc

Simulations using real data collected through this mechanism can help city planners determine

potential traffic challenges at a highly-granular levelmdashby street intersection freeway ramp school

area etc mdash to significantly improve the quality of empirical commuter data used in street flow

planning and addressing existing congestion problems

copy 2016 Ness SES All Rights Reserved17

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

About Ness Software Engineering Services

Fully-integrated user experience design platform development

and data analytics services from visioning to execution

3000 colleagues | Engineering teamrsquos level of experience

exceeds industry-average

Teams designated for clients on ongoing basis | Engineers

commonly work with the same client for multiple years

10 Technology Innovation Centers across 6 countries

Product Engineering rigor is at the foundation of our

approach

Global Scale

Engineering Heritage

Integrated Solution

Design amp Development

Long-Term Client

Relationships

Experienced Personnel

copy 2016 Ness SES All Rights Reserved18

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Further Reading

bull The Evolution of the Connected Home (DATAQUEST)

bull Improving Predictive Maintenance with IoT (IoT Central)

bull Edge Analytics an Antidote to IoT Data Deluge (InformationWeek)

bull Healthcare Things Are Getting Better at Supporting Wellness (IoT Global Network)

bull Improve Loss Prevention in Retail Stores by Applying Swarm Intelligence (Indian Retailer)

bull Contextualising Data Will Help Monetize The Internet of Things (InformationAge)

bull Inside the Connected Carrsquos Ego Network (Auto Tech Review)

bull Ness Whitepaper Capitalizing on the Business Value of the Internet of Things

bull Ness Blog Predictions 2016 ndash IoT Payments amp Loyalty Programs APIs

bull Ness Blog Does EveryrsquoThingrsquo Matter in the Internet of Things

bull Ness Blog Internet of Things and Industrial Analytics

bull Ness Blog When Every Car Becomes a ldquoSmartrdquo Car

And a number of related readings on

bull Ness Insights httpwwwness-sescominsightsresource-library

bull Ness Blog httpwwwness-sescomcategoryblog

Here is a compilation of articles whitepapers and blog posts on IoT They have been presented and

published on various channels

copy 2016 Ness SES All Rights Reserved16

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

In Conclusion

kmathew | ness_tech

By using Swarm Intelligence (SI) algorithms such as Particle Swarm Optimization (PSO) city

planners can create simulations to understand potential congestion challenges based on how

vehicles and pedestrians navigate public spaces

PSO is a good algorithm to apply to large businesses in a city as it helps them understand the

behavior of each employee or a group of employees (beginningending of shifts) navigating out of

facilities and getting on streets by walking in vehicles using public transport etc

Simulations using real data collected through this mechanism can help city planners determine

potential traffic challenges at a highly-granular levelmdashby street intersection freeway ramp school

area etc mdash to significantly improve the quality of empirical commuter data used in street flow

planning and addressing existing congestion problems

copy 2016 Ness SES All Rights Reserved17

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

About Ness Software Engineering Services

Fully-integrated user experience design platform development

and data analytics services from visioning to execution

3000 colleagues | Engineering teamrsquos level of experience

exceeds industry-average

Teams designated for clients on ongoing basis | Engineers

commonly work with the same client for multiple years

10 Technology Innovation Centers across 6 countries

Product Engineering rigor is at the foundation of our

approach

Global Scale

Engineering Heritage

Integrated Solution

Design amp Development

Long-Term Client

Relationships

Experienced Personnel

copy 2016 Ness SES All Rights Reserved18

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Further Reading

bull The Evolution of the Connected Home (DATAQUEST)

bull Improving Predictive Maintenance with IoT (IoT Central)

bull Edge Analytics an Antidote to IoT Data Deluge (InformationWeek)

bull Healthcare Things Are Getting Better at Supporting Wellness (IoT Global Network)

bull Improve Loss Prevention in Retail Stores by Applying Swarm Intelligence (Indian Retailer)

bull Contextualising Data Will Help Monetize The Internet of Things (InformationAge)

bull Inside the Connected Carrsquos Ego Network (Auto Tech Review)

bull Ness Whitepaper Capitalizing on the Business Value of the Internet of Things

bull Ness Blog Predictions 2016 ndash IoT Payments amp Loyalty Programs APIs

bull Ness Blog Does EveryrsquoThingrsquo Matter in the Internet of Things

bull Ness Blog Internet of Things and Industrial Analytics

bull Ness Blog When Every Car Becomes a ldquoSmartrdquo Car

And a number of related readings on

bull Ness Insights httpwwwness-sescominsightsresource-library

bull Ness Blog httpwwwness-sescomcategoryblog

Here is a compilation of articles whitepapers and blog posts on IoT They have been presented and

published on various channels

copy 2016 Ness SES All Rights Reserved17

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

About Ness Software Engineering Services

Fully-integrated user experience design platform development

and data analytics services from visioning to execution

3000 colleagues | Engineering teamrsquos level of experience

exceeds industry-average

Teams designated for clients on ongoing basis | Engineers

commonly work with the same client for multiple years

10 Technology Innovation Centers across 6 countries

Product Engineering rigor is at the foundation of our

approach

Global Scale

Engineering Heritage

Integrated Solution

Design amp Development

Long-Term Client

Relationships

Experienced Personnel

copy 2016 Ness SES All Rights Reserved18

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Further Reading

bull The Evolution of the Connected Home (DATAQUEST)

bull Improving Predictive Maintenance with IoT (IoT Central)

bull Edge Analytics an Antidote to IoT Data Deluge (InformationWeek)

bull Healthcare Things Are Getting Better at Supporting Wellness (IoT Global Network)

bull Improve Loss Prevention in Retail Stores by Applying Swarm Intelligence (Indian Retailer)

bull Contextualising Data Will Help Monetize The Internet of Things (InformationAge)

bull Inside the Connected Carrsquos Ego Network (Auto Tech Review)

bull Ness Whitepaper Capitalizing on the Business Value of the Internet of Things

bull Ness Blog Predictions 2016 ndash IoT Payments amp Loyalty Programs APIs

bull Ness Blog Does EveryrsquoThingrsquo Matter in the Internet of Things

bull Ness Blog Internet of Things and Industrial Analytics

bull Ness Blog When Every Car Becomes a ldquoSmartrdquo Car

And a number of related readings on

bull Ness Insights httpwwwness-sescominsightsresource-library

bull Ness Blog httpwwwness-sescomcategoryblog

Here is a compilation of articles whitepapers and blog posts on IoT They have been presented and

published on various channels

copy 2016 Ness SES All Rights Reserved18

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Further Reading

bull The Evolution of the Connected Home (DATAQUEST)

bull Improving Predictive Maintenance with IoT (IoT Central)

bull Edge Analytics an Antidote to IoT Data Deluge (InformationWeek)

bull Healthcare Things Are Getting Better at Supporting Wellness (IoT Global Network)

bull Improve Loss Prevention in Retail Stores by Applying Swarm Intelligence (Indian Retailer)

bull Contextualising Data Will Help Monetize The Internet of Things (InformationAge)

bull Inside the Connected Carrsquos Ego Network (Auto Tech Review)

bull Ness Whitepaper Capitalizing on the Business Value of the Internet of Things

bull Ness Blog Predictions 2016 ndash IoT Payments amp Loyalty Programs APIs

bull Ness Blog Does EveryrsquoThingrsquo Matter in the Internet of Things

bull Ness Blog Internet of Things and Industrial Analytics

bull Ness Blog When Every Car Becomes a ldquoSmartrdquo Car

And a number of related readings on

bull Ness Insights httpwwwness-sescominsightsresource-library

bull Ness Blog httpwwwness-sescomcategoryblog

Here is a compilation of articles whitepapers and blog posts on IoT They have been presented and

published on various channels

copy 2016 Ness SES All Rights Reserved19

Background Image This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona California 2016

Kuruvilla Mathew bull Chief Innovation Officer and SVP Office of CTO

Ness Software Engineering Services

2001 Gateway Place Suite 480W San Jose CA 95110 USA

Mobile +1 949 678 9364

kuruvillamathewnesscom | wwwness-sescom

kmathew | ness_tech

httpwwwness-sescomcategoryblog