IoT Platforms and Architecture

IoT Reference Architecture and Deployment Alternatives

Juxtology Proprietary

End to End IoT Solutions

IoT Reference Architecture – Multiple AlternativesAllSeen, OneM2M, Industrial Internet Alliance, Open Internet Consortium, …


The reference architecture converges on Devices, Gateways, Data Accumulation, Analytics and UI

World Forum’s more granular 7 Layer Model Microsoft Azure model is much more generalized

IoT “Standards” vary in terminology and focus – But, they provide good guidelines for platform deployment needs

Industrial Internet Consortium – Three tier architecture including Edge, Platform and Enterprise tiers

Open Connectivity Foundation (and iotivity.org) – 4 tiers: discovery , data transmission, device mgt, and data mgt

Open Interconnect, AllSeen – more HAN focused, less focus to analytics, cloud and business intelligence

OneM2M Standard – Three tier architecture including Network Services, Common Services and Application Layers

Due to its granularity the IoT World Forum Reference Model provides a good model to assess platform requirements

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IoT Reference Model – Physical Devices and ControllersField Assets, Events, Objects – The “Things” in IoT


This layer is a little ambiguous as legacy “Things” may need to be retrofitted with sensors and connectivity

Architectures and Implementation Alternatives:

Asset IoT solutions can be very simple or very complex

o Single sensor monitoring and reporting

o Multiple sensor monitoring, reporting and control

o Multiple sensor monitoring, reporting, intelligence and control

o Field based analytics, local intelligence, and fault management

Power / Cost / Range / Bandwidth all influence design decisions

Fast time-to-market solutions are typically loosely-coupled due to

engineering time / cost and regulatory / compliance issues

o Early deployments can be “multi-piece” solutions

o Market / design / P&L validation should trigger full integration

o Tiered solutions connects “capillaries” to GW to Cloud

o “Capillaries nodes” can connect directly to Cloud

o With integration, field equipment can direct connect to Cloud

Asset “Enterprise Cost” is a key motivator in IoT design

o “Cost” is enterprise cost (maintenance, asset cost, failure cost)

o High cost assets justify more IoT management investment

o Lower “cost” assets may force simpler, cheaper IoT solutions

o Critical assets may require very low latency response times

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IoT Reference Model – Connectivity (Focus on Wireless)Communications / Connectivity can be Embedded or Wired to Assets


Multiple technologies, design alternatives and suppliers makes for a rich but complex ecosystem

Important Connectivity Considerations:

Power is critical to many applications, and is a

range/bandwidth/availability trade-off

Multi-tier solutions can be more complex, but can

optimize cost/range/availability issues

Mesh technologies are also more complex but provide

higher availability

Range / Power / Bandwidth Trade-offs:

LTE for high BW distributed assets (mode 0 coming)

LoRa / SigFox for low bandwidth / low power / long range

WiFi high power / low range / high speed

WiFi 802.11ac coming with power & range benefits

802.15.4g mesh is a good range / power / BW alternative

Very short range: Bluetooth, WirelessHART and ISA100

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IoT Reference Model – Connectivity (Focus on Wireless)

Communications / Connectivity can be Embedded or Wired to Assets


The nature of the IoT requirements will drive Field Area Network (FAN) architecture

Bandwidth

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eSingle or Multiple Connectivity Options may be Used:

Where practical (power & coverage) LTE is simple

Power / Serviceability may force use of LPWAN

Bandwidth needs may force multiple technologies

Hierarchical field area networks are a design pattern:

oLow cost / low power “Edge Nodes” connect to GW

oGateways provide “Edge Intelligence” and backhaul

Range / Power / Bandwidth Trade-offs:

LTE for high BW distributed assets (mode 0 coming)

LoRa / SigFox for low bandwidth / low power / long range

WiFi high power / low range / high speed

WiFi 802.11ac coming with power & range benefits

802.15.4g mesh is a good range / power / BW alternative

Very short range: Bluetooth, WirelessHART and ISA100

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IoT Reference Model – Connectivity (Focus on Wireless)

Communications / Connectivity can be Embedded or Wired to Assets


The nature of the IoT requirements will drive Field Area Network (FAN) architecture

Backhaul Network

(Hybrid)

LTE

Licensed or

Unlicensed Private

Long Reach Low Access

(LoRa, Mesh, Others)

Wired

Alternative Deployment Scenarios

Technologies SigFox LoRa LTECat-M 802.11ah Dash71.0 IngenuRPMA nWave Zigbee

Frequency868/902ISM

433/868/780/91

5ISMCellular

sub-1GHz

unlicensed

433/868/915

ISM2.4GHzISM sub-GHzISM

2.4GHz,915,

868ISM

ChannelWidth UNB 8-64/125kHz 1.4MHz 1-16MHz 25-200KHz 1MHz UNB

Range30-50km

2-5Kmurban

15Krural2.5-5km 1km to5km 500kmLoS 10km 300m

EndNodePower

10uW-100mW

dBm<27dBm 100mW 1mW-1W TBD to20dBm 25-100mW 0dBm

Uplink

100bpsto140

msg/dayto100kbps 200kbps 150-350kbps 9.6to166kbps 624kbps/sector 100bps 250kbps

Downlink4msgof8b/day to100kbps 200kbps 150-350kbps 9.6to166kbps 156kbps/sector -- 250kbps

Devices/AP 1M 100K 20k 8191 connectionless 384k/sector 1M 65k

TopologyStar staronstar Star Star,Tree

Node-Node,

Star,Tree

Star,treew/

extenderStar Mesh,Star,Tree

Roaming Yes Yes Yes w/802.11r Yes Yes Yes

Status Deployment Deployment 2016 2016 Deployment Deployment Deployment Deployment

Comparison of IoT Radio Technologies

Cloud Application

Tiered

Solution

Direct

ConnectFully

Integrated

Gateways

Edge Nodes

Sensors

Edge Nodes

Sensors Embedded

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IoT Reference Model – Edge ComputingEdge Computing may be at the “Edge Node”, Gateway, or Cloud Edge


Intelligence at the edge enables bandwidth limiting, local autonomy, and low latency feedback loops

Edge Computing Architecture and Design Considerations:

Power availability & serviceability for the IoT deployment

Real-time feedback, analysis and control needs

Requirements for intelligence at the edge:

o Local data processing to minimize connectivity bandwidth

o Local decision making to address real-time control needs

o Customer configurable or designed edge applications

Device “Ruggedness” requirements (thermal, shock, EMI, etc.)

Over-the-air updates and bandwidth required for updates

Overall Gateway / Edge Node / purpose suitability:

o Flexibility to handle multiple sensor input types

o Low-latency detection of trigger events in “moving data”

o Cost effectiveness for purpose (edge vs GW vs computing)

Design and Architectural Drivers:

OS – Linux or small footprint embedded OS

CPU – ultra-low, low, or higher power (data pipe or analysis)

Connectivity – Range + BW = higher power needs

These considerations often drive the need for multi-tier edge node

plus gateway (edge processor) architectures as mentioned before.

Cloud Application

Tiered

Solution

Direct

ConnectFully

Integrated

Gateways

Edge Nodes

Sensors

Edge Nodes

Sensors Embedded

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IoT Reference Model – Edge ComputingEdge Computing may be at the “Edge Node”, Gateway, or Cloud Edge


Intelligence at the edge enables bandwidth limiting, local autonomy, and low latency feedback loops

Operating Systems

IoT and Gateway Hardware

Distributed Intelligence & Control

Connectivity

Protocols & Translations CoAP

Wired ISP

Sensors and I/O Connectivity

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Field Assets, Smart Cities, Smart Grid

Microwave

There is a Sea of Options for Edge Computing:

The needs of the IoT solution MAY drive very

specific set of technologies – but MAY NOT

Many solutions can leverage simple off-the-

shelf solutions from OEM suppliers

For more demanding solutions a design can be

created from specific ecosystem technologies

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IoT Reference Model – Data AccumulationManaging the high Velocity, Volume and Variety of IoT Data


This layer must handle not only data accumulation, but also real-time data distribution to applications

The ”Data Accumulation” layer is critical to IoT Systems and for

more complex systems may be significantly oversimplified:

Per the reference architecture, this layer is for data storage

But this “Cloud Edge” layer may add other needed functions in

beyond just storing field data in a “data lake” for later processing:

o DDS middleware such as publisher / subscriber, data

classification, filtering and in-motion transformation capabilities

o Low latency data screening, aggregation, and transformation

for critical system / low latency operations

o Data parsing and mapping to multiple DB types and schema

o Pre-processing between IoT and legacy application syntax

Some IoT solutions will require only simple SQL management

High Volume, Velocity, Variety IoT solutions will require NoSQL

Highly scalable solutions will require DDS and device intelligence

Architectural Requirements for Data Accumulation Layer:

High availability and reliability suitable to the application

Performance matched to velocity, volume and latency needs

DB ETL / Schema management to simplify upstream data mgt

Efficient services tied to data abstraction, applications and I/O

System design that enables ingress/egress latency needs

Data Accumulation / Storage Key Issues:

IoT data is Big Data with Big Data problems of

Volume, Velocity and Variety

High volume, velocity, variety assessment of in

motion data is an important IoT design aspect

Need Hot path - immediate processing & Cold

path for storage and application processing

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IoT Reference Model – Data AccumulationManaging the high Velocity, Volume and Variety of IoT Data


Unique requirements (3V’s) will drive the simplicity or complexity of storage and tool complexity

Data Accumulation / Storage Key Issues:

IoT data is Big Data with Big Data problems of

Volume, Velocity and Variety

When IoT solutions are integrated into legacy

systems interoperability is important

High volume, velocity, variety assessment of in

motion data is an important IoT design aspect

Compute Clusters

Infrastructure …aaS Big Data Storage …aaS

Big Data Access and Control

Unique

Applications

Platform …aaS

The Data Accumulation Architecture Drives Decisions:

”3V” IoT drives need for Cloud-based NoSQL or hybrid solutions

In-memory high speed processing provides low-latency responses

Small enterprise (some Utility) may require on-premise solutions

Winning OEMs / SPs will provide solutions that provide:

Proven “canned” solutions that are easy to create and deploy

Tools to assist customers in designing data storage design

Data Distribution Services to manage disparate system needs

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IoT Reference Model – Data AbstractionThe Data Abstraction Layer ”Makes Sense of the Data” for Applications


Data reduction, context creation, augmentation with meta data, derived data creation …

The data density of IoT systems (Velocity, Volume and Variety)

makes data abstraction and overall volume reduction essential

Data processing in the Data Abstraction Layer transforms the high

volumes of data from IoT readings into more valuable information

For example, IoT readings are transformed from multiple

readings to levels, trends, conditions, exceptions and classified:

o Temperature readings reduced to “instance counts”

o Time series events reduced to relevant change vectors

oDevice data is augmented with meaningful IoT meta data

oAnomalous or critical readings can be queued to fast paths

This process is executed through the cleanup and processing of IoT

ingress data and creating the abstract data formats for applications

Multiple micro-services can be used for data transformation and

as front-end logic to application services

Appropriate ETL or Big Data operations transform raw data-lake

messages into formats and queues for applications

Multiple data abstractions may be created for the different types

of back-end applications (SQL / NoSQL) and latency needs

Virtual device and virtual device state models are maintained

based on the abstracted data and accessible to applications

Raw Sensor Data

Aggregation and Filtering

Metadata Integration

Abstraction and Reduction

Storage of IoT Models

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IoT Reference Model – Application LayerReporting, Analytics and Control – and Integration into Back End Systems


Initial IoT Application Layer may focus on “Things” but integration into the larger business processes is the goal

The core value of IoT is connectivity to business logic and

analytics. Get the data, manage it, ANALYZE it - then ACT!

The primary business drivers for IoT fall into three main categories:

Improved customer awareness, accessibility and engagement

Improved operational efficiency, profitability, and predictability

Advancement of social good with technology delivered services

The Application Layer Software Services deliver this value:

Business Logic Applications from consumer transactions to real-

time industrial controls deliver new levels of value with IoT data

Analytics applications identify trends from consumer behavior to

field asset operation and enable new business insights

Improved “resolution” from real-time IoT data allows rules based

engines to deliver higher value control, prediction and insight

Machine learning algorithms identify and exploit trends and

correlations previously hidden in the high volume (3V) IoT data

Automated report generation enhances compliance requirements

IoT is processed and transformed and fed in to advanced field

asset, factory equipment, fleet, or logistical control software

Data Visualization, Reporting, Analytics provide insight and

connectivity across workers – from the office to the field

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Combine near-real-time IoT information with historical operations data for next gen analytics

Real Time IoT Advances will Require Edge Intelligence, Low

Latency, and Fast-Path Applications in Concert “Real-time awareness” and event- based systems create new

opportunities for businesses deploying end-to-end IoT systems

Asset location, monitoring, specialized diagnostics , real-time telemetry,

usage pattern data become available for applications and analytics

Next gen tools that leverage real-time data put new power in machine

data analytics, operations personnel and business users

Fusing real-time data and historical structured and unstructured data

creates a new dimension of operational and real-time intelligence

Real-time, state-based visibility will transform automation and efficiency

with closed-loop rules based systems (on-demand or event-driven)

Machine intelligence applications will eliminate run to failure operations

and deliver less human attention and more economic value

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Source: Vitria.com



Move from monitor, to control, to system level optimization and to transformative business insight

The full value of IoT integration will evolve over time

Initial focus is simple connectivity, data acquisition, condition

monitoring and control – moving to performance optimization

Automation and closed loop control is the next wave

Building IoT and near-real-time results into business operations

applications will bring the biggest wave of economic value

Architecture

IoT and old structured Data

Source: GE Automation

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IoT Analytics has become an industry to itself with an entire ecosystem of tools, suppliers and consultants

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IoT Reference Model – UI, Collaboration, and ProcessUsing IoT Information / Visualization / Analytics to Transform Business Operations


From simple operational visualization, to advanced IoT control systems, to new process creation

Connect People with Data Portals and Business Processes -

Integration of IoT information with other business logic and

Application Enablement Platforms and Total System Visualization is

where the value of IoT deployments is realized and enhanced

Key Operations metrics, Asset information, Consumer behavior

become tangible with near-real-time visualization

Interactive visualization tools enable hands-on inspection and

analysis to uncover issues and discover event correlations

IoT device policy, rules and state management tools enable

optimization of operations and system lifecycles

Application Enablement Platforms simplify integration of new

data and derived data to enhance back end business applications

Field maintenance actions and prioritization based on near-real-

time information transforms the efficiency of field personnel

Architectural and deployment considerations:

RESTful web services to orchestrate IoT information with

enterprise applications and cloud services

Automation enablement and exception based alarms through

rules engines using IoT and business application integration

Customize and optimize interfaces for specific employee needs

Create workflows based on near-real-time events / transactions

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IoT Reference Model – UI, Collaboration, and ProcessUsing IoT Information / Visualization / Analytics to Transform Business Operations


From simple operational visualization, to advanced IoT control systems, to new process creation

Combine historical data and real-time IoT data to

create new system modeling and business insight

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Presentation / Visualization

IoT Reference Model Coverage – Few Suppliers Provide “Whole Products”Customers must work with multiple IIoT solution providers to deploy solutions

This graph provides a subjective view of IoT function coverage based on recent Juxtology research

End-User Integration Services

IoT Solution Designer Software Tools

IoT Cloud-connect / Dev. Mgt.

IoT LTE Transport

IoT Gateway / Aggregation

Field Area Networks

Edge Intelligence

IoT Edge Devices

Sensor Integration

Juxtology Proprietary18

Processing / Analytics

Machine Learning

Strong Differentiation

Emerging Leaders

Strong Players

Solutions for IoT Remain Very FragmentedCustomers still must work too hard to field working solutions

Solution providers that make IoT easy for customers will have critical competitive advantage

Compute Clusters

Presentation

Infrastructure …aaS Big Data Storage …aaS

Big Data Access and Control

Cloud-based

Applications

IoT Analysis and Insight

Web Interface and Device Control

Operating Systems

IoT and Gateway Hardware

Distributed Intelligence & Control

Connectivity

Protocols & Translations CoAP

Wired ISP

Sensors and I/O Connectivity

Clo

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Platform …aaS

Transport: Wired and Wireless Private Networks

Field Assets, Smart Cities, Smart Grid

Users and

Customers

Microwave

Juxtology Proprietary19

End


Data & Analytics

IoT Platforms and Architecture