TOWARD SMART CITIESM I N A FA R M A N BA RR E S E A R C H E R , U N I V E R S I T Y O F S TAVA N G E R

PART 1

What is a city?

What is a smart city?

Implications on the management and the governance

Digitalization

Overall effect of digitalization

Implications of Smart Cities

Smart City Applications

Development and Implementation of Smart Cities

Case study: Triangulum

WHAT IS A CITY?

Cities are the new relevant entities in the world. Economically relevant, socially relevant, ecologically relevant, politically relevant culturally relevant.

Cities can be understood as sociotechnical systems, a place where people live and work together.Cities can further be understood through different dimensions: it can be an economic system, a place where wealth, and a location in which jobs are generated. It can be a social system, a way of life, and with a separate culture, with its own activities, traditions and more. It can be considered as a political system, where different actors interact with each other, who decides about what, who has

power over what. It is also a technological system, which are systems comprised of physical components. Such as infrastructure (bridges, roads, generators, etc.), as well as organizations (such as manufacturing companies or utilities) and scientific

components such as research programs

systemic view of a city

WHAT IS A SMART CITY?

Traditionally, systems are comprised of two main layers: an infrastructure a service layer

Digital or data layer: The main point that differentiates a smart city is the addition of a third layer in between the original

two This layer grows through the increasing number of sources for data generation in cities, such as sensors,

cameras, GPS, smart phones, and many others

Smart city is based on intelligent exchanges of information that flowbetween its many different subsystems. The city will act on this information flowto make its wider ecosystem more resource efficient and sustainable.

WHAT IS A SMART CITY?

The city is a place where people live and work. The urban infrastructures enable such collective livingand working. The infrastructures enable the way that people work, the way that people live, the waythat people decide.

urban infrastructure systems can be viewed as a socio-technical system in itself as part of a larger urbansystem.

IMPLICATIONS ON THE MANAGEMENT AND THE GOVERNANCEThe most important concept here is the concept of feedback loops. The social dimensions interact with the technical dimensions. The technical dimensions interact with the economic dimensions and all this creates feedback loops. So decisions that have been made at some point in the past deploy their effects later in the future.

We have classified performance in terms of: Efficiency Resilience Sustainability of citiesbut it is easily understandable that all these feedback loops, the complexity, the path dependency makes themanagement and the governance of these cities extremely complex, unpredictable and we need to take thatfact into account when we manage and govern cities.

DIGITALIZATION

Digitalization comes as a new layer, coming on top of the systemic view, doesto the management and the governance of urban infrastructure systems.The base of this data layer is: Telecommunications infrastructure (both cables and wire- less), which allows for the physical

transmission and storage of information. Wired infrastructures, the cables, the fibre Wireless infrastructures also where the capacity is increasing 1G, 2G, 3G, 4G, WiMax, WiFi, satellites.

Network layer that is the different devices that are being used need to be connected to each other through the telecommunications infrastructure

and this is done by so called protocols, physically connecting the different data storing devices to each other

Identification and exchange What is typically being called the World Wide Web

DIGITALIZATION

We have basically 4 layers: telecommunications infrastructure, network, identification and exchange of the different data data generation and storage.

This process of analysis of large amounts of data is known as ”Big Data”.

DIGITALIZATION

Data Analytics:

All this data needs to be analyzed and there is a rapid development in the abilityand the capacity to analyses and visualize these.

The visualization is necessary because analytics often cannot really show what theoutcome is.Artificial intelligence, machine learning, deep learning tools basically allow for the intelligent analysis

of all these huge amounts of data.

The combination of all these trends in the evolution of information and communication technologies and their uptake in the society is called

”digitalization”.

OVERALL EFFECT OF DIGITALIZATION

Now, what changes with digitalization: It is the fact that we are able to mirror this physical value chain in a data layer. All the information that

is contained in the physical value chain is now duplicated and replicated at the digital layer The interface with the customer now becomes a digital interface.

OVERALL EFFECT OF DIGITALIZATION

Potential industries that can be affected by this mirroring and by this digitalization: The ones that are particularly information intensive. For example, education or health care, where a lot of information is contained, is known by the doctors or the teachers, and

mirroring these information on a digital layer and repackaging them to sell to the customers has a huge disruptive effect on theseindustries.

The ones that are non-scalable industries: A typical example here is the taxi industry, which has been disrupted by Uber.

Aggregating this information and creating new services outof it really disrupts the traditional industry.

Many of these categories coexist within the city level; therefore,digitalization has a heavy impact on cities and urban infrastructures

SMART CITY APPLICATIONS

Examples of smart city applications for different services:

DEVELOPMENT AND IMPLEMENTATION OF SMART CITIES1) The first vision is that of vendors: hardware companies, infrastructure companies and data management and analytics companiesThese have accepted the smart city concept, because there is an opportunity to monetize on thesetechnologies and services being deployed at an urban level.

2) The second vision is from the city or metropolitan area

In this perspective, users are viewed as citizens. Additionally, due to the nature of government’s role, this isa demand-pull case; technologies are implemented to answer an existing need of the population.

TRIANGULUM

One of the European Smart Cities and Communities Lighthouse Projects

Lighthouse Cities: serve as testbeds for innovative projects focusing on sustainable mobility, energy, ICT and business opportunities Manchester (UK)

Eindhoven (NL)

Stavanger (NO)

22 partners from: industry, research and municipalities

Follower Cities: Leipzig (D)

Prague (CZ)

Sabadell (ES)

Tianjin (CHN)

GOALS

Triangulum is a Smart Cities project to integrate multiple ICT and IoT solutions in a secure fashion to manage a city’s assets.

Goal: Improving quality of life by using urban informatics and technology to improve the efficiency of services and meet residents’ needs.”

The overall aim of Triangulum is: Develop an open source solution Facilitate access to the data Generate impact assessment and monitoring frameworks Provide analytics toolkit for visualization The reduction of CO2 emissions, Increased use of renewable energy sources, Increased energy efficiency and user awareness, Change of user habits by involving, and engaging citizens

And more …

CITY OF STAVANGER

Stavanger is the fourth largest city of Norway-The region has a high level of digital service development and provision, due to its High-speeddigital infrastructure-The Stavanger region is regarded as one of the most innovative regions in Norway

The Stavanger Triangulum consortium consists of: The Municipality,

Lyse AS (the energy provider and owner of the fiber ICT infrastructure),

The University of Stavanger with research partner IRIS and the big data CIPSI center,

The Rogaland County Council with partner Kolumbus, the public transport company.

UISUiS has focused on identifying the baseline data available and working towards developing interfaces to accept the data which will be stored in the cloud data platform.

The aim of this framework was to provide a holistic and integrated view of the data for Stavanger, in a way that can plan their activities in a more integrated way

UiS tasks:•Data Collection•Data Storage and preprocessing•Data Integration and Analysis

UIS

UiS Modules Deliverables: Module 544 Cloud data platform (CDP) Module 542 Data analytics toolkit

Motivation: Providing a standard ICT solution for documenting and analysis the impact of all modules.

Cloud data platform (CDP) The Cloud Data Hub utilizes a group of open source technologies that offer a high-level analysis and management on large

and varied datasets, as far as it will be applicable for the citizens and replicable for other cities and businesses.

Data analytics toolkit It is closely integrated with the implementation of Module 544: Cloud data platform The goal is to be an enabler in adding value from data, and to provide the various partners with tools so that their

particular interests can be realized. The data analytics toolkit can be used to quantify impacts based on data collected across the Lighthouse cities

HOW CAN DATA HELP US

•Helps make energy systems more efficient from creation to consumption• The data on the consumer side, can give information on how to optimize this usage.• It is also possible that the user gets suggestions on how to lower the consumption It can be in form of

replacing an outdated fridge to renewing the building’s isolations.

•Better traffic management• From changing the duration of traffic lights in peak hours and during night to re-routing and

distributing the traffic to places with less traffic.

•More data would make defining patterns and trends easier• When having high amount of data, it is possible to fuse all or some of them together to get a more

clear picture of things.• For example using weather data with transportation or energy usage. If a certain weather causes

more traffic, it will be shown in the data and so measures can be taken to make it easier.

•Better healthcare• With the advent of IoTs in houses and in general smart houses, it is now possible to monitor patient’s health remotely

and send medical help if necessary.

CHALLENGES

Processing collected Data Non-standard data formats Missing data Recoverable

Un-recoverable

Privacy Role based access

Data anonymization

CHALLENGES

Security of the platform Denial of Service attacks where the aim is to force the platform to be out of service

Man in the middle attacks (end user) where an adversary listens to the data being transmitted. Perhaps using an SSL type of communication might reduce this type of

risk.

Attack on the database Man in the middle attacks (data source)

Accessing and modifying database

Accessing user credentials

GENERAL CONSIDERATIONS FOR DESIGNING DATA PLATFORMData storage

Scalability

Reliability

Security

Privacy

Fault tolerance

Open source

BASIC DIAGRAM OF CLOUD DATA PLATFORM

ACHIEVEMENT

In the Triangulum project, the partners in Stavanger demonstrate innovative integrated solutions betweenenergy, mobility and ICT, which will contribute to reaching the objective of becoming a smarter city.

Some highlights of the activities are the following:

Energy

Smart meters and gateways were installed in all 56 households in the designated area

Completed smart installations and connectivity in 2 public buildings (Lyse)

The Central Energy Plant (CEP) was built and finished in April 2017. Two out of 3 buildings are being heatedor cooled by the new installation, making use of the sewer as the main energy source, since June 2017

Mobility

5 battery buses have been deployed by the bus operator in the city. Drivers were trained and the buses weretaken into operation in April 2017. They are being operated upon several main bus routes in Stavanger.

Smart chargers for EV were installed in 10 homes; Data is being sent to the UiS.

ACHIEVEMENT

ICT

A version of Cloud Data Platform is operational, hosted at the UiS data centre (CCP)

Ongoing data collection from one data source in each Lighthouse city has been established

A beta version of the dashboard for data analytics toolkit was presented in Nordic Edge, webinar fordata platforms and Triangulum General Assembly.

Three master dissertations have been proposed to run data analytics into actionable insights acrossmultiple use cases at UiS. Use cases are defined based on the data from Stavanger.

The Municipality of Stavanger has launched 142 open data sets to the public as an additional benefit ofTriangulum.

CONCLUSION

In this section, we have conceptualized cities, and, more importantly, smart cities.

We have understood the mechanisms that have enabled and driven the trends of digitalizationwe see today.

We have also explored what digitalization is, and what are its implications for cities.

Lastly, we saw what the main challenges of the development and implementation of smartcities are.

We have analyzed the case study of Triangulum project and the Smart City Barcelona tounderstand the role of digitalization in a real-life smart city, and some of the opportunitiesand challenges that come with it.

DATA ANALYTIC TOOLKIT

PART 2

What is data analysis?

Big data

Big Data Analytics

Approach to analytics development

Visualisation

Values of Big Data Analytics

Benefits of Big Data analytics

Challenges

Requirements

WHAT IS DATA ANALYSIS?

Tools for cleaning, analyzing and telling a story with data.

Data Analysis process:

WHAT IS DATA ANALYTICS?

Analytics is an encompassing and multidimensional field.

It uses: Mathematics, Statistics, Predictive modeling, Data Warehouse Machine-learning and data mining techniques to find meaningful patterns and knowledge in recorded data.

BIG DATA

Big data is high volume, high velocity, and/or high variety information assets thatrequire new forms of processing to enable enhanced decision-making, insightdiscovery and process optimization

BIG DATA ANALYTICS

Big data analytics is the process of probing big data set to reveal hidden patterns,unknown correlations and other important information that can be used to makedecisions.

Big data analytics uses: advance techniques like predictive modeling, text analytics, machine learning, forecasting and statistical analysis.

It will help to identify trends, weak spots or determine conditions for making better and faster decisionsabout the future.

BIG DATA ANALYTICS

Big Data Analytics helps us to understand our organization better. With the use of Bigdata analytics, one can make the informed decisions without blindly relying onguesses.

It can help answer the following types of questions: (4 types of Analytics)

•Descriptive: What happened?

•Diagnostic: How or why did it happen?

•Predictive: What’s likely to happen?

•Prescriptive: What should I do about it?

APPROACHES TO ANALYTICS DEVELOPMENT

Identify the data sources

Select the right tools and technology to collect, store, aggregate the data

Understand the business domain

Identify tools and technology to process the data

Build mathematical models for the analytics

Visualize

Validate your result

VISUALISATION

When analysis is done, the results need to be communicated to various stakeholders. One of the hardest parts of an analysis is producing quality supporting graphics. Conversely, a good graph is one of the best ways to present findings.

Graphics are used primarily for two reasons: exploratory data analysis presenting results.

BENEFITS OF BIG DATA ANALYTICS

By integrating data from organizations across the private, public and non-profit sectors, wecan collectively build solutions that improve the reality of living the city life; from optimizingpublic transport routes to creating incentives for people to recycle by tracking citizens’ litterhabits.

Some real-life examples of cities who have greatly benefited from it:

Water Management Real-time analysis and sensors can help detect the flow of water, pollution level, predict scarcity on the basis

of usage, reduce areas of leakage, sewage overflow, etc. Sensors help keep track of the water’s quality: its salinity, pH, etc., giving notification alerts in case there has been an unusual change.

This eliminates the need to collect information manually and test the water quality, which would be a time-consuming process

BENEFITS OF BIG DATA ANALYTICS

Garbage Management An example can be: garbage bins might be placed with sensors that will be connected with garbage

disposal centers. Perhaps, garbage cans might come with a feature that indicates when a garbage can have become full, which will, in turn, notify

the trash can collectors, who can then predict and decide which route to focus and map their path

Controlling Pollution Tracking illegal factories, or those releasing a heavy and unacceptable amount of toxic substance can

become easier. For example, currently, Beijing is facing a problem of acute air pollution. IBM has teamed with the

Government for its project called ‘Green Horizon’ to combat this problem. Big data can be helpful to identify the sources of pollution, its quantity; which will be of great help to

tackle the issue. It can also be used to predict the occurrences of smog. By enhancing the information available and

forecasting abilities, finding a solution will become much easier.

BENEFITS OF BIG DATA ANALYTICS

Traffic Congestion Management

Monitoring traffic performance: By monitoring traffic performance and patterns over time, cities can make significant progress in cutting

congestion, emissions and noise; determine where to place buses and build mass transit stations to ensurethey operate at full capacity; and improve emergency vehicle response times.

Reduce accidents: With Big Data analytics – Traffic department have beforehand knowledge on the traffic situation at

particular location which they can share with commuters and advice them to take the detour or avoidcongested area which can help in reducing accidents occur due to congestion.

Road Maintenance: Through sensor on the roads, video camera, Analyst can analyze which road demands repairing.

Predict speed of the traffic and volume: Data aggregates from multiple devices - cameras, detectors, Bluetooth, mobile and social media can help in

identify and measure traffic speed and volume on city roads using predictive analytics

BENEFITS OF BIG DATA ANALYTICS

Energy Congestion Management

Demand and Capacity Management: Data gather from both producer and consumer side can help in analyzing demand and capacity. It will

help in understanding the peak/non hours of usage.

Forecasting based on seasonality: Data collected on regular interval during the years can help analyst in understanding the pattern of

usage of the energy. Seasonality pattern can help in looking for when and in which region the demand will be high or low.

Green Energy: Through predictive analytics, producer can generate electricity from renewable sources with small plant

set up and in incremental way depending upon the needs of a city. Large dataset can help in doing analysis on sources of carbon emission which can be reduced

CHALLENGES

Some of key Challenges for smart cities using big data:

Data source and characteristics: Big data is generated from many different formats and many different sources There are many data formats Which are unstructured (Eg: Audio, Video, Server logs, etc..) This data classified and managed into a structured format using advanced database systems. Collecting data by itself is complex by existence of multiple sources with different access polities and usage and with

different types and formats

Data and information sharing: Sharing information and data among different city is challenge. Each city and government agency or department typically has their own warehouse and public information. Here we will collect our data based on ensuring citizens right of privacy in big data analysis.

Data Quality: Big data provide a data quality, data captured by different people under any standard formats and stored in distinctive

database. Multiple data will be suffering from lack of heterogeneity, consistency and disparity issues will occur. Accordingly,” there is no universal way to transform and retrieve the data source from useful analysis”.

CHALLENGES

Security and privacy: Big Data breaches will be big with potential of more serious damages to reputation and legal

repercussion. Movement of data across various sources in a secure manner is a biggest challenge inSmart city application implementation. Security here means right information to a right person at right time and at right place. While designing smart city, it is important to look at how data can be masked and secured so that it

cannot be reached to unwanted persons.

Thank you for listening!

REQUIREMENTS

The application of Big data to smart cities is classified into two types, real-time big data and offline big data application.Real-time Big data big data are fast to access data from database and we can make a decision withshot period of time line. It is important to make an data from timely fashion and that is analysis to donein a fast and reliable way.Big data applications for smart cities are planning in areas like health care, traffic, education, controlson real-time application

REQUIREMENTS

Big data application based on smart cities, it is address several requirement smart city nature needs and big data characteristic. These requirements are identified based on challenges of smart cities applications and based on big data applications

Advanced Algorithms:

Big data can’t handle the regular application due to unique requirement and applications and need for high volume and speed. Big data cannot handle the all the data mining algorithms .Big data algorithm are based on limited and well defined data sets. Smart cities using Big data application need to implement sophisticated and advance algorithm will be excused in Big data analysis. Some applications are designing for a real-time application and another for offline applications. This algorithm based high data volume and large data sets and decision making processes to be optimized data. These algorithm are based on heterogeneous environments and capability of handle the highly dynamic environments.

REQUIREMENTS

Open standard Technology: Advantages of open standards implementing and designing data large scale of Heterogeneous data

and systems in big data in smart cities and it will be more flexibility for upgrading and maintaining application for smart cities.

Security and Privacy: The data collected and processed in smart cities will contain in a from private information to ensure the

applications and technological and maintain a high level of security and privacy mechanisms. Smart cities provide many positive advantages; it poses several threats to their relying data. It will secure our data from illegal attacks or malicious attacks. Big data application provide security and privacy policies design during develop and implementation of codes.

REQUIREMENTS

Citizen Awareness: Citizen aware to use ICT solutions for smart cities safely and correctly. The different issues may

encounter with smart cities applications quality of data collection, performance of data applications. Based on results decision making made from collected data to enhance smart cities components in big data applications. Citizen awareness is important role in their knowledge of good safety, privacy and security. B

Government Roles: Smart cities in Governing entities much guiding principles of collaboration, participation to exchanging flow of big data in control [10].It requires a big data systems to collected data from government entities. It required essential role in a smart cities. The government must review the information on privacy, data accuracy, data access and preservation [10]. Documentation and code books are use of the data sets [10]. Effectively Big data application help in smart cities beneficial uses of data individual privacy concepts of privacy laws.ig data will be protecting and awareness of their own data.

VALUES OF BIG DATA ANALYTICS

Big data analytics helps organizations harness their data and use it to identify newopportunities. That, in turn, leads to smarter business moves, more efficient operations, higherprofits and happier customers.

Here are the most important values of Big Data:

Cost reduction: Big data technologies such as Hadoop and cloud-based analytics bringsignificant cost advantages when it comes to storing large amounts of data – plus they canidentify more efficient ways of doing business.

Faster, better decision making: With the speed of Hadoop and in-memory analytics,combined with the ability to analyze new sources of data, businesses are able to analyzeinformation immediately – and make decisions based on what they’ve learned.

New products and services: With the ability to gauge customer needs and satisfactionthrough analytics comes the power to give customers what they want. with big data analytics,more companies are creating new products to meet customers’ needs.