Making Actionable Decisions at the Network’s Edge

In the evolving hyper-connected world of the Internet of Things, immense new possibilities are emerging from interlinked ecosystems that can make fast, actionable decisions uncon-strained by traditional analytical processes. Edge analytics is fast emerging as a way of extending the limits of cloud-enabled decision-making.

November 2017

DIGITAL SYSTEMS & TECHNOLOGY

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Digital Systems & Technology

| Making Actionable Decisions at the Network’s Edge

EXECUTIVE SUMMARY

The Internet of Things (IoT) is enabling innovative new connected ecosystems to emerge

that amplify the value organizations provide to end customers. Smart cities, smart infra-

structure, driverless cars and real-time guidance using location intelligence are just a few

of the ways these new solutions touch our everyday lives. However, what makes such con-

nected ecosystems tick, succeed and evolve is their ability to process in near-real time

the huge amounts of data generated at the network’s edge by sensors and instrumented

devices.

The emergence of the cloud has made building such ecosystems possible, but addition-

al time and enhanced connectivity is still required to inform fact-based decisions after

analysis. Sending data to the cloud and awaiting analytical results costs precious millisec-

onds, damaging real-time responsiveness. Billions of devices produce data persistently, but

managing and making sense of this big data requires a huge investment in computational

analytics, storage and networking software, as well as powerful computing platforms. Or-

ganizations that hue to traditional approaches will be unable to tap the IoT’s full potential.

A new and evolving technique — commonly known as edge analytics — is rapidly emerging

as the go-to mechanism for overcoming existing infrastructure limitations. While edge ana-

lytics derives from cloud analytics, it goes one step further by democratizing the ability to

analyze data — not just in cloud data centers but at the point of data collection, on devices

themselves and in the gateways that interconnect enterprise ecosystems.

Edge analytics has reached a major inflection point. In data-warehouse-oriented analytics,

data is typically sent, via gateways, to cloud systems where the entire analytics process

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Digital Systems & Technology

occurs. Data visualization is presented after a series of steps performed in the cloud. While

this works fine in situations that do not require decisions to be made quickly, digital-era

demands are quickly changing the business calculus.

In edge analytics, data is collected and analyzed close to the source of data generation.

So, instead of sending data to the cloud for analysis and then waiting for a response, edge

analytics brings more computation to the edge, saving on time as well as cost of data trans-

mission. Simple model-based analytics can be conducted on the device/sensor itself while

more complex analytics that require data from multiple devices can be performed on IoT

gateways, and finally the most sophisticated form of analytics — commonly called big data

analytics — can be handled on the cloud. This analytics hierarchy reduces the complexity

and burden on the network and the data centers. Distributing the analysis of data to the

edge is a powerful way of unlocking IoT value.

Edge analytics is not just about gaining operational efficiencies or making the business

more scalable. Many businesses do not require complex or sophisticated data analytics, but

do need speed and automation. Any delay in delivering results or loss of data due to con-

nectivity failure can cause reputational and even financial damage to the organization. It is

another reason why gateways need to be placed closer to the source so the data generated

can be cleaned, batched and sent back to decision-makers in shorter timeframes.

This white paper explores how to build a connected ecosystem that not only has a brain in

the cloud, but also reflex actions at the edge. It also offers some case illustrations to help

decision-makers envision edge analytics’ art of the possible.

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THE CLOUD ON EDGE

Large organizations have benefitted from the concept of cloud computing. But as the IoT grows, along

with the amount of the data generated to inform real-time decision-making, it is essential to access

this data quickly — but without incurring huge investments of time and money. This is where edge ana-

lytics comes into play. The trick is to incorporate both models to their best effectiveness: deploy edge

analytics where time is of the essence, and use cloud analytics where security and data volumes are

the deciding factors. It is imperative that IoT strategies make use of the best of both cloud computing

techniques and edge analytics processing to optimize IoT ecosystems (see Figure 1).

Edge vs. Cloud

Cloud Analytics Edge Analytics

Cloud

Internet

Network

Devices

Dat

a Data

Cloud

Internet

Network

Edge

Devices

Dat

a

Data

Figure 1

Making Actionable Decisions at the Network’s Edge | 5

Digital Systems & Technology

The trick is to incorporate both models to their best effectiveness: deploy edge analytics where time is of the essence, and use cloud analytics where security and data volumes are the deciding factors. It is imperative that IoT strategies make use of the best of both cloud computing techniques and edge analytics processing to optimize IoT ecosystems.

6 | Making Actionable Decisions at the Network’s Edge

In the IoT age, nearly every connected and instrumented devices generates huge amounts of data. The underlying metadata, however, is useless unless it is analyzed for meaning.

AN INTRODUCTION TO EDGE ANALYTICS ARCHITECTURE

The hierarchy of edge analytics can be represented as a three-tiered architecture (see Figure 2, next

page). The flow of data begins with sourcing of raw data from smart devices or sensors followed by

more sophisticated analysis on gateways at the edge of the network and finally some “heavy lifting,”

or big data analysis, using complex cloud computing models.

Tier 1: The Sourcing of Raw Data

In the IoT age, nearly every connected and instrumented devices generates huge amounts of data.

The underlying metadata, however, is useless unless it is analyzed for meaning. Much of the data

collected does not require complex analytics, hence data from these devices can be analyzed on the

“edge” - i.e., close to the source of data generation – to deliver near-instant automated results.

Tier 2: Processing Data on the Network’s Edge

Edge analytics deploys gateways on the edge of the network. These gateways connect, collect and

analyze data in near-real time. The outcome of this analysis can be transferred back to the devices

immediately or can be stored in a small, low-cost memory device. The stored data can be further

transferred or routed to the cloud for advanced analytics.

Distributing analytics on the network to different edge nodes has many advantages. It decreases the

complexity that companies face while computing huge amounts of real-time data and increases the

scalability by distributing the computation workload across multiple edge nodes.

Tier 3: Sophisticated Cloud Computing

Filtered data from the edge of the network is transferred to the cloud for more complex processing.

Data is sent to the cloud from multiple gateways to store, process or analyze. Generally, data that does

not require an instant response is transferred to the cloud for heavy-duty processing.

EDGE ANALYTICS BUSINESS DRIVERS

With the deluge of data has come the need to use it quickly and in real time. IoT devices pump out

data in huge amounts and with the increasing number of smart connected devices, the need for edge

computing is clear. According to Gartner, there will be 20.4 billion IoT devices by the year 2020.1 In this

scenario, it is important to identify which business drivers spur a demand for ongoing edge analytics.

7Making Actionable Decisions at the Network’s Edge |

Three-Tier Edge Analytics Architecture

Tran

sfe

r o

f D

ata

Tier 3: Analytics on the Cloud

Tier 1: Data Sourcing

Tier 2:Analytics on the “Edge”

Figure 2

• High data volumes: Huge amounts of data in the cloud will spur scalability issues and pose a

problem of bandwidth, increasing storage costs. This factor makes it the primary business driver

of edge analytics.

• Latency: This can be a killer where predictions drive decisions and actions in real time. It may not

be prudent to have predictions made in the cloud and then sent back to ground zero. In the IoT

world, prediction has to be made within 100 milliseconds, which can be accomplished only with

edge analytics.

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• Robotics: Autonomous systems, which are often used to inform decisions such as object recog-

nition, shortest path and control of actuators, must be made in real time. Edge analytics is an

essential component in these deployments.

• Power saving: Continuous data transmission to the cloud leads to huge power consumption.

Avoiding the transmission of data is one way to conserve power in low-power IoT devices.

• Regulatory problems: Countries such as Germany have passed regulations to keep the data

generated in their country within their political boundaries.2 Cloud servers are often located thou-

sands of miles away from the data’s originating country. With such new regulations, taking the data

to the cloud for analytics will become an issue. Edge analytics again will come in handy in such

situations.

ADDRESSING THE REQUIREMENTS

In the digital age, data has become the most valuable asset to organizations. Data-driven companies

are heavily dependent on sophisticated and heavy-duty analytical techniques to achieve business

objectives and stay competitive with rivals, globally. And edge analytics, without proper implementa-

tion know-how, will not add any value to envisioned business outcomes. Hence it is imperative that the

aforementioned business drivers are properly addressed to advance corporate goals.

High Volume Data

As the pressure increases on CIOs to reduce the volume of data transferred to the cloud, there is a

growing need for decentralized distributed computational power at the edge of the networks, close to

the generation sources, to monitor data volumes. Monitoring this high volume of data at the edge can

deliver meaningful insights in the following ways:

• Threshold crossing alerts (TCAs): The majority of the data received at the edge may not be

of much interest, assuming the system is working normally. To filter out such data, companies

can install a tool or software with predefined threshold values for parameters. When the param-

eter value crosses the threshold value, it will trigger an alert to the monitoring tool. Using this

approach, companies can save a lot of time and money when evaluating this data.

• Summary extraction: With this technique, companies can extract a summary of the analyzed data

at the network’s edge. This summary can then be sent to the cloud for more sophisticated analysis.

Companies can set a timer to extract the summary on a periodic basis. Doing so can dramatically

reduce the amount of data being sent to the cloud.

• Parameterized models: This more sophisticated technique is an advanced version of summary

extraction. Here, companies run an appropriate algorithm on the data periodically and extract only

the parameters for the model and not the complete summary. This method is used with advanced

computing techniques to transfer only the parameters to the cloud. In the example below, only

after the model is extracted in the cloud are the parameters used in the next iteration onwards.

This limits the amount of data sent to the cloud.

» Parameterized model: a𝑥 +b𝑥2 +c𝑥3 +d, Extracted Parameters (coefficients): a:5, b:8, c:3, d:9.

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QUICK TAKE

The Case for Connected Energy

Large corporate office complexes and buildings benefit from hardware/soft-

ware tools that monitor energy generation and consumption in real time.

The data is typically collected from all energy sources onto one IoT device

at the so-called edge of the corporate network. On this platform, the data

(kilowatt units) is continuously monitored around a predefined threshold

limit. When the power generated falls below this threshold, an alarm is acti-

vated. In this technique, data is not always sent to the central servers – only

when an abnormal situation occurs.

This real-time monitoring technique helps corporations maximize their use

of renewable energy. It saves bandwidth and space storage in the cloud. It

provides environmental benefits by helping companies reduce their carbon

footprints and meet regulatory targets on energy consumption. By prevent-

ing energy waste, corporations can monetize unused power by selling it to

the grid after work hours and during the weekends.

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Low Latency Decisions

Edge analytics simulates the working of the

human nervous system: The edge environment

simulates the spinal cord and the cloud simulates

the human brain. Most of the sensory signals that

require instant reflex action are responded to

directly by the spinal cord without transmitting

the signals to the brain. Similarly, data requests

that demand immediate action are analyzed on

the network edge and are responded to immedi-

ately. This swift analysis drastically reduces the

latency overhead that results when data is sent

to the cloud for analysis.

The spinal cord transmits only those signals to

the brain that do not require an impulsive reflex

action, or those signals that can be handled only

by the brain. Similarly, the edge filters the data

that is sent to the cloud. The results of the edge

analysis that require more heavy-duty analytical

treatment are routed to the cloud through the

gateways at the edge of the network.

In our Digital Technologies Lab, we have built

solutions using edge analytics that have reduced

decision-making time to a few hundred millisec-

onds, from a few seconds.

Low latency can be achieved using two techniques:

• Static reflex: Reflex action at the edge is

triggered by the prediction made by the

computing model. This model can be a static

version, unchanging over time. A static model

is used when the data environment does not

change much and the corresponding data

falls into fixed patterns. The upside of this

approach is the model needs to be refreshed

only infrequently.

• Adaptive reflex: Adaptive models are more

sophisticated, and learn as they evolve. Such

adaptive models change and enhance their

analyzing capabilities according to the data

set. These adaptive models are used to handle

more complex analysis where data changes

are quite frequent.

Comparing the Human Nervous System & Edge Computing

!

CLOUD

EDGE

DataSources

Gateways&

Sensors

Figure 3

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QUICK TAKE

Indoor Navigation

Indoor navigation tools provide navigational services inside a building when

and where GPS is not available. The objective is to provide the shortest path

between two points as people move from place to place.

This technology displays real-time locations, similar to how GPS works. Since

the technology operates with minimal latency, a model is trained using pre-

dictive algorithms that run on a server at the network’s edge, and which

are then distributed to the user’s mobile phone during runtime (when the

person is moving). Indoor navigation functions primarily on a hard-coded

reflex action since the models are pre-configured and changed infrequently.

In-Place Analytics

In-place analytics tools/software are designed to analyze and process data in its local environment.

By employing in-place analytics on native data, organizations can filter down these large data sets

collected from their IoT devices. The result generated from the analysis is more targeted and helps

organizations gain quick access to key facts about operational matters, thus empowering them to

make more informed strategic decisions. Working with smaller data collections also helps organiza-

tions avoid duplication of data and to more efficiently dispose of data that isn’t business critical.

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One of the approaches to implement in-place analytics is geo-distributed machine learning (GDML)

(see Figure 4).

• GDML: In the GDML technique, each compute node runs a local component of the algorithm on

the data available and calculates interim results — local values of objective function, gradient and

direction.

» The interim results are communicated to a central node on the cloud layer.

» At the central node, a global component of the algorithm aggregates all the results it receives

from the network’s edges and calculates global approximate objective function, gradient and

direction.

» Global approximate values are broadcast back to the edge layer.

» This iteration is repeated until a desired convergence in the model is achieved.

THE ROAD AHEAD FOR BUSINESSES

Edge analytics provides an appropriate platform for numerous IoT services and applications, such

as driverless cars, smart grids/buildings and smart cities, as well as wireless sensors and actuators

networks (WSANs). Every business opportunity across any industry that requires low latency and

communications accuracy — including automotive, consumer electronics, energy and utilities, and

healthcare — will find the implementation of edge analytics extremely helpful.

Three-Tier Edge Analytics Architecture S

enso

rsS

enso

rs

Edge Layer Cloud Layer

Training

Temporary Model

Edge Layer

Gateway

Prediction

Action

Training

Model Convergence

Gateway

Prediction

Action

Training

Model Convergence

Data Data

Figure 4

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QUICK TAKE

The Case for Smart Cities

In a smart city, traffic lights at each intersection are equipped with various

sensors to monitor passing cars and predict whether a driver will jump the

red signal. To develop this model, the data collected from all the traffic sig-

nals must be analyzed in order to identify the patterns. This can be done by

collecting the data from all the traffic signals at a centralized server, or we

can use the GDML approach to develop the model.

In GDML, the data collected in each traffic signal is kept locally — only the

objective functions generated locally are sent to the central server. Once a

converged model is arrived at, it is distributed to all traffic signals, and it

is then used for prediction. This approach drastically reduces data trans-

mission rates and at the same time generates an effective and predictive

global model.

An enormous number of applications are available for edge analytics, and with IoT gaining force in

the coming years, businesses interacting directly with consumers will have to realign their business

models to earn strategic advantage over their competitors. These businesses should know that it

would become extremely difficult to build a growth strategy based on the existing legacy systems as

they are very expensive to maintain and have low response times.

In the future, cloud-only computing will continue to be costly and time-consuming. The expected

growth in IoT-based applications will encourage vendors to commercialize edge analytics to deliver

on the promises of lower latency, near-real-time response rates and more-optimized user experience

than existing cloud analytics systems can provide.

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FOOTNOTES

1 https://which-50.com/iot-connected-devices-reach-20-4-billion-2020-says-gartner/

2 https://reef.apache.org/papers/2015-12-GeoML.pdf

REFERENCES

• www.travancoreanalytics.com/can-edge-analytics-future-big-data/.

• http://analyticsindiamag.com/edge-analytics-taking-data-processing-from-cloud-to-edge-of-network/.

• http://ijarcet.org/wp-content/uploads/IJARCET-VOL-2-ISSUE-2-568-571.pdf.

• http://ijcsmc.com/docs/papers/October2014/V3I10201441.pdf.

• www.cio.com/article/3200846/cloud-computing/the-difference-between-edge-and-cloud-computing-all-cio-s-should-know.html.

• www.ioti.com/iot-strategy/why-iot-ecosystem-nervous-system.

• https://reef.apache.org/papers/2015-12-GeoML.pdf.

• www.gartner.com/doc/3675917/cool-vendors-iot-edge-computing.

• http://dataconomy.com/2016/03/fog-computing-future-cloud/.

• https://blogs.cisco.com/perspectives/iot-from-cloud-to-fog-computing.

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Ranga VangipuramChief Architect, Cognizant Global Technology Office

Ranga Vangipuram is a Chief Architect within the Cognizant Global

Technology Office’s Digital Technologies Lab. He has 30 years of

experience in the manufacturing, telecomm and IT industries, with

nine of those years spent in the U.S. telecom sector. Ranga previ-

ously led new product development and innovation in Cognizant’s

social, mobile, analytics and cloud (SMAC) group. He is passionate

about emerging technologies and specializes in providing tech-

nological solutions to business problems. Ranga’s current area

of focus is in IoT and data science, with a focus on the connected

energy, indoor navigation, edge analytics and location-aware-things

spaces. He has a master’s degree in engineering from Anna Univer-

sity. Ranga can be reached at Ranga.Vangipuram@cognizant.com |

www.linkedin.com/in/ranga-vangipuram-8b2b541/.

ABOUT THE AUTHORS

Ashish AnandSenior Business Analyst, Cognizant Global Technology Office

Ashish Anand is a Senior Business Analyst within Cognizant’s

Global Technology Office (GTO). He is part of the GTO marketing and

alliances team. Ashish’s passion is creating fresh perspectives on

future technologies. He received a postgraduate degree in business

management from the Institute of Management Technology, Gha-

ziabad. Ashish can be reached at Ashish.Anand6@cognizant.com |

www.linkedin.com/in/ashish-anand-265724102/.

GLOBAL TECHNOLOGY OFFICE

Global Technology Office (GTO) is a core business unit of Cognizant, with a mandate to power technology-driven transformation. GTO’s mission is to power and accelerate Cognizant’s capability to harness transformative technologies and enable our customers, people and processes to navigate the shift in the work ahead.

As part of GTO, Cognizant Technology Labs is the specialist group that researches, pilots and prototypes emerging technologies with the greatest potential to spark transformative business innovation. Our labs focus on pursuing tomorrow’s technologies to put our clients ahead, and extending our footprint in specific areas where we believe technology trends are headed.

We develop solutions that bring real value to our clients’ businesses, and help them own and embrace emerging technologies in ways that make sense for their organizations. We also draw on our existing building blocks and emerging technology expertise to accelerate the devel-opment of new applications.

ABOUT COGNIZANT

Cognizant (NASDAQ-100: CTSH) is one of the world’s leading professional services companies, transforming clients’ business, operating and technology models for the digital era. Our unique industry-based, consultative approach helps clients envision, build and run more innova-tive and efficient businesses. Headquartered in the U.S., Cognizant is ranked 205 on the Fortune 500 and is consistently listed among the most admired companies in the world. Learn how Cognizant helps clients lead with digital at www.cognizant.com or follow us @Cognizant.

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