The Internet of Things (IoT) solutions have the ability to transform public services in a positive way and when powered by cloud-based services, solutions can securely and cost-effectively scale and provide benefits to people that need them the most. Nexleaf’s mission is to preserve human life and protect the planet by designing sensor technologies, generating data analytics, and advocating for data-driven solutions. Nexleaf developed ColdTrace and StoveTrace solutions to improve the distribution of life-saving vaccines and to increase the adoption of cleaner cookstoves. City of Virginia Beach, in collaboration with Virginia Institute of Marine Science, developed a sensor based flood alerting system to give advance notice to citizens and first responders. In this session, you will hear from both about how these AWS powered solutions are driving effective results and helping communities.

1. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Cloud Powered IoT: Connected
Solutions Helping Communities
Nithya Ramanathan
CEO & Co-founder, Nexleaf Analytics
Session ID: 195422
Sridhar Katragadda
Systems Analyst, Information Technology,
Virginia Beach
Sri Elaprolu
Sr. Manager, Public Sector IoT
Amazon Web Services

2. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Cloud Powered IoT: Connected
Solutions Helping Communities
Nithya Ramanathan
CEO & Co-founder, Nexleaf Analytics
Session ID: 195422

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Where do the future customers of the
world live?

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2030: Emerging Markets Will Drive the Global Economy

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Origins: Why Supply Chains and Maintenance Matter

6. 6
Nexleaf’s mission is to preserve human
life and protect our planet by designing
sensor technologies, generating data analytics,
and advocating for data-driven solutions to
global challenges.

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Challenges in Designing Solutions for Emerging Markets
Infrastructure Gaps Lack of Connectivity Local Context
Remote Areas Financing for Scale Maintenance

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Picture of vaccination

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Our Approach to Target Every Level of the Market
IoT Solutions Analytics Data-driven
Interventions

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Combatting Climate Change with
Sensor Verified Clean Cooking

16. 3.8 MILLION
Indoor air pollution kills
people every year
causing more
deaths than
AIDS, Malaria
and TB
combined

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Monitoring of Traditional and Cleaner Cookstoves

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Users
Powered by AWS
Potential Future Service
Use:
• Amazon ElasticCache
• Elastic Load Balancing
• Amazon Route53
Amazon EC2
Amazon S3
Amazon CloudFront
Cleaner Cookstove
StoveTrace

19. USER-FRIENDLY
TECHNOLOGY
AFFORDABILITY
BEHAVIOR
CHANGE
SUSTAINABLE
MARKETS

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COEL Bangle: IoT Wearable to Protect Pregnant Women

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Nexleaf Analytics Impact

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Our Solution: Fortifying Broader
Markets with the Power of Data

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Nexleaf Targets 11 UN Sustainable Development Goals

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Thank you!

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Sridhar Katragadda
Systems Analyst, Information Technology, Virginia Beach
195422
StormSense AWS: Building Predictive
Tools Through Data Assimilation Using
IoT, Voice and Multi Cloud Environments
Dr. Jon Derek Loftis
Research Scientist, Virginia Institute of Marine Science, William & Mary

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StormSense - Summary
• Addressing flooding problems pro-actively in Hampton Roads region
• Funding, regional perspective, and partnerships
• IoT sensor deployment, proliferation, model development and predictive
tools
• The journey begins with AWS Cloud
• Predictive tools and multi cloud environments
• Mapping and visualization
• Planning for the future
•

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Problem
Existing flood communication and messaging systems have not yet responded to
the changing risk patterns brought by the rise in sea level and have not been able to
meet the diverse needs of stakeholders at-risk of flooding in the next decade
• IoT sensors can observe and provide adaptive flood warnings for
National Flood Insurance Program (NFIP) Community Rating System (CRS)
(Loftis et al., 2017)
• Predictive models can aid in preparation for these events
(Loftis et al., 2018)
A better understanding of flood-risk perception, information-seeking behavior and
decision-making can inform the development of new communications tools and
flood-risk messaging

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Being Pro-Active...Hampton Roads...StormSense
• In 2013 - Reviewed current implementations and research in sensors and
flood modeling
• Before July 2015, After July 2015 - 4 Sensors (NOAA), 10 Sensors (USGS)
• Mid 2016 – Won Replicable Smart City Technologies (RSCT) funding from usignite/NIST
Global City Teams Challenge (GCTC) - Newport News and Virginia Institute of
Marine Science (VIMS) of College of William & Mary. Norfolk and City of Virginia Beach
participated.
• June 2017 - First Sensor Installation followed by 6 Sensors in Newport News,
10 Sensors (Pilot Project) in Virginia Beach
• Additional 23+ sensor installations in progress in 2018

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Funding, Regional Perspective and Partnerships
Project Partners (as of November 20

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Sensor Deployment
• Stage 1: Sensor
Deployment
• Stage 2: Tidal
Calibration
(via Tidewatch)
• Stage 3: Model
Development
• Stage 4: Model
Release for EM’s
Green Stream - Sensor

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Sensor Proliferation
Great Neck, Virginia Beach
Dam Neck, Virginia Beach
Leeward Marina,
Newport News
Shellabarger Drive, Newport News
Dorchester Lane, Virginia Beach
June 2017 - First sensor installation followed by 6 sensors in Newport News,
10 Sensors (pilot project) in Virginia Beach...Additional 30 + sensor purchase in progress in 2018

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Deployment...Lessons Learned...
• Installation - Multiple options used (City, Contractor…)
• Battery...solar power issues...weather problems...snow...
• timer, data frequency
• Sensor configurations (in-house and field)
• Data spikes (previous reading...)
• Data frequency adjustment (for modeling) – 6 minutes
• Sensor calibration (optimal range adjustment - Distance)

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Tidal Calibration
• Stage 1: Sensor
Deployment
• Stage 2: Tidal
Calibration
(via Tidewatch)
• Stage 3: Model
Development
• Stage 4: Model
Release for EM’s

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Model Development
• Stage 1: Sensor
Deployment
• Stage 2: Tidal
Calibration
(via Tidewatch)
• Stage 3: Model
Development
• Stage 4: Model
Release for EM’s
How the StormSense
Flood Model
is built:
• Sensor-Driven
• Predictions too
• Embed Lidar
& bathymetry
• Add Buildings
Chesterfield Heights Represented by Sub-Grid
Granby Park
Moseley Creek
Middle Towne Arch

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Model Development
How the
StormSense
Flood Model
is shared:
• Tidal forecasts at
sensors
• Flood maps via:
ArcGIS Online &
Google Earth
• Stage 1: Sensor
Deployment
• Stage 2: Tidal
Calibration
(via Tidewatch)
• Stage 3: Model
Development
• Stage 4: Model
Release for EM’s

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Model Release
• Stage 1: Sensor
Deployment
• Stage 2: Tidal
Calibration
(via Tidewatch)
• Stage 3: Model
Development
• Stage 4: Model
Release for EM’s
“Catch the King” Tide Monitoring -
Nov. 5, 2017 - Validate and Improve
Predictive Models:
 510 known participants on
Nov. 5th
 53,006 time-stamped GPS
max. flood extent
measurements collected
 1,126 geotagged
photographs

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The journey begins with AWS
• Support of Replicable Smart City Technologies (RSCT) grant
• December 2016 – Christmas – idea generation and prototyping
• Introduction by NIST – AWS Team
• Presented at Smart City Operations and Platforms Engineering
(SCOPE) Conference, 2017 - emerging flood model validation
frameworks for street-level inundation modeling with StormSense
• Applied for “City on a Cloud” innovation challenge
• Idea to win – 2017 - Best Practices Category (Large) - $50,000 AWS
Credits

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Initial prototype presented at SCOPE Conference

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Emerging Ideas and Architecture Change
Amazon
CloudWatch
Real-time Processing
stormsensehist
Sensor Data
Ingest
Multi-cloud
Batch Processing
stormsensedata
Real-time Data
csv, json
Skill “storm sense”
csv, json
Skill fulfillment
Historic Data
De-duplication Amazon SNS Insertion
function
REST APIAWS Storage Gateway
stormsensedata
stormsensehist
Items Insert @
Specified Day TTL

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Alexa Skill “storm sense”
Amazon
Lambda
Python (boto3)
Alexa voice
service
Amazon Echo
Serverless
AWS
DynamoDB
StormSense
(Real-time)
Echo Show, Echo Spot
StormSense
Visuals
Chatbots
Amazon
Lex
Amazon
Polly

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Predictive Tools and Multi-cloud Environments
• IoT data to support model results in real-time
• Hydrodynamic model building
• Predictive charting
• Hindcasting and forecasting...scenario simulation of past events
• AWS, ESRI and Microsoft, mapping and visualization – support City
officials for decision-making
• Citizen awareness - voice and notification
• IoT big data analytics and machine learning
• Long-term planning

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Sensor Assets Map

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Mapping and Messaging

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Visualizing Water Level Data

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Visualizing Rainfall Data

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Planning for the Future
Cameras in strategic locations
such as bridges and vulnerable
intersections...seasonal
Amazon Machine
Learning
Amazon
Rekognition
Serverless photo recognition/
frame-based analysis of video or
images
AWS
DeepLens
Camera
Amazon Sagemaker

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Planning for the Future
stormsensehist Amazon
Quicksight
Amazon
Redshift
Amazon
EMR
Amazon
Athena
Analytics

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Speaker Contact
Systems Analyst
Information Technology
Department
City of Virginia Beach
skatraga@vbgov.com
Sridhar Katragadda
Research Scientist
Virginia Institute of Marine Science
College of William & Mary
jdloftis@vims.edu
Dr. Jon Derek Loftis

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