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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 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
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 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
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 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
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 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
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 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
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 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
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 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
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 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
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 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
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 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
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 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
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 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
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 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
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 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
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 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
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 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
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 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
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
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