1 Copyright © 2011 Tata Consultancy Services

Limited

RIPSAC – Real-Time Integrated

Platform for Services & Analytics

Dated : March 2013

2

RIPSAC - Objectives

Make it easy to develop applications that use

– sensors that can be reached using web protocols

– Sensor available on smart phones

Unlock sensor data from application silos

Provide a scalable and secure cloud based platform for these

apps

Provide a scalable platform for analytics can be performed

3

RIPSAC - A PaaS platform for IoT Applications

PaaS

Provider

Inte

rne

t

Sensor

Services

Analytics

Storage

Services

Internet

RIPSAC

App Developers

End User

Sensors

Sensor Providers

For Platform Provider

Core IoT Services

Identity , Security, Privacy

End User License Mgmt

Ad Delivery

Multi-tenancy

Sandboxes

Operation Support

Systems

For Sensor Provider

Feature, Phenomena &

Sensor description

Define feeds & sensor

streams

Publish & share sensor

streams

Define access control and

privacy preferences

For Application Developer

Dev & Test Sandboxes

SDK & APIs

Test Data

Publish Apps

Define EULAs

Manage App Life Cycle

For End User

Download Apps

Subscribe / Unsubscribe

Services

Control Privacy Settings

View usage history,

billing etc.

RIPSAC PaaS Cloud Use Case

RIPSAC – Real-time Integrated Platform for Services & AnalytiCs

Overview Java PaaS for Sensor Web & Mobile Crowd

Sensing Apps

Provides OGC SWE standards

SDK and APIs provided in RIPSAC

4

How Sensors connect with RIPSAC

Internet

Sensors

directly

connected to

Internet

Sensors connected to internet via gateways or handheld

devices

RIPSAC platform services

Sensor Networks

Web Services calls over Wireless / cellular

networks / m2m networks

Gateway Devices

handhelds

Sensors

directly

connected to

handheld device

Web Services interfaces exposed by RIPSAC

Platform services

5

RIPSAC Platform Services & APIs

RIPSAC provides the following services and APIs

Multi-tenant platform with secure virtual environment for each tenant

Java and JavaScript APIs

Sensor Services

– Sensor & sensor observation description, metadata & discovery

– Sensor observation recording and query

– Support for geo-spatial and spatio- temporal queries

– Can support any type of sensor & sensor observations

Database & Storage

– Relational and Document database services

– Load balanced and scale-out services

Analytics & Visualization Services

– Auto-scaling and load balanced „R‟ server farms

– Message driven batch analytics framework

– JavaScript APIs for visualizing and exploring Sensor Data

6

RIPSAC Applications

Producer

Applications

Consumer

Applications

RIPSAC

Platform

Services Producer

Applications

RIPSAC

“Producer”

Applications

RIPSAC

Platform

Services

RIPSAC

Platform

Services

Consumer

Applications RIPSAC

“Consumer”

Applications

Sensors, Gateway

Devices, Handhelds

( Source of sensor

observations )

End User

Application outputs

7

Integration with Infrastructure Cloud Services

Producer

Applications Producer

Applications RIPSAC

Applications

RIPSAC Platform Services RIPSAC Platform Services RIPSAC Platform Services

Infrastructure Cloud Services

RIPSAC Portal

Auto Scaling & Load Balancing Services

VM VM VM

Virtual Machines

Infrastructure Cloud Services API calls

8

Components in RIPSAC

Sensor Application @

Edge Gateway

Application @ Backend

Gateway

Sensor

Observation

Service

SOS

Authentication

SOS

Logging

SOS RDBMS

Sensor

discovery

RDBMS for App

Task Queue Load Balancer

R R

Portal

Visualization

Control

Logic Analytics

Consum

er

Applic

ation

Java SOS

Library

App Life Cycle

Mgt

DB Mgt

Analytics

Dashboard Multi Tenancy

Maven

Archetype

Service

Dashboard Visualization

Library

MongoDB

ObjectStore

JavaScript

SOS Library

On

Openstack

based TCS

Cloud

On

Amazon

ASW Cloud

9

RIPSAC Features

Feature List

• OGC Sensor Observation Service ( SOS)

• Sensor catalog service / registry service

• Time Series Database

• Sensor Simulator

• Template Project/Code Generator

• Authentication & Logging for SOS

• APIs

• Java Client libraries

• JavaScript client libraries

• Relational Database Service

• Document Database (NoSQL) service

• Play Framework support

• Task Queues

Feature List

• R based Scalable Analytics Service

• Batch jobs framework

• Tenant Management

• Multi-tenant app deployment support

• Application life cycle management

• Service Dashboard

• OpenStack infrastructure cloud integration

• JavaSacript Sensor Data Visualization libraries

• Horizontal scalability of sensor and analytics services

• Runs on OpenStack based TCS Cloud

• Runs on Amazon AWS cloud

10 Copyright © 2011 Tata Consultancy Services

Limited

Example RIPSAC Applications

11

Smart Transportation Solution

Sensor Interface

Services

Storage & Database

Services

Analytics Services

RIPSAC + Apps

iDigi M2m

Cloud

Bus Tracking System

Parking Management

Cab & Passenger Tracking

Reports & Alerts

Location ( GPS), Speed,

Accelerometer, Passenger Ids

Valid passenger

lists, Route Info

Images / video

feeds of entry &

exit ramps of

underground

parking lots

Images of cab

registration

number,

Passenger Ids

ReSolver: Wind Forecasting on RIPSAC

Protocol

Convertor

System Network

SCADA Workstation 2

SCADA Workstation 1

Wind Operator Control Room

Internet

Historian Workstation

Forecaster Workstation

Dash board Workstation

Forecast 1

Forecast 2

Adaptive Combination

Forecast 3 . .

RIPSAC Communication & Reporting

Adaptive Wind Forecasting Boost forecast accuracy of a given

predictor Adaptively combine multiple

predictors for better accuracy Benefits Better asset utilization Real-time competitive advantage in

energy trading markets Minimize imbalance charges Features Adaptive forecast Program maintenance Data security 24x7 service SLA compliance Reporting

13

Intelligent Healthcare – Remote Medical Consultation

ECG

Body Fat Analyzer

Blood Pressure Monitor

Pulse OxyMeter

Mobile gateway

433MHz Wireless gateway

Patient Records

Health Center / Home

Expert Doctor

14

RIPSAC and Big Data

Big Data Approaches in RIPSAC

Scale out of SOS and Analytics Server

Map Reduce on Mongo DB

Export of SOS Data to HDFS

Meta Data in SOS, Big Data in Hadoop HBase

Scale out of SOS and Analytics Server

Sensor Observation Service ( SOS)

Front-end

SOS

Server

Load Balancer

R

Analytics Server Farm

Application

SOS

Server SOS

Server R R

Map Reduce on Mongo DB

Sensor Observation Service

Mongo DB

Map Reduce on

Mongo

Export of SOS Data to HDFS

Sensor Observation

Service

SOS to

HDFS

Adapter HDFS

Map Reduce

109 207 246

Dark 1

255 255 255

Light 1

131 56 155

Dark 2

0 99 190

Light 2

85 165 28

Accent 1

214 73 42

Accent 2

185 175 164

Accent 3

151 75 7

Accent 4

193 187 0

Accent 5

255 221 62

Accent 6

255 255 255

Hyperlink

236 137 29

Followed Hyperlink

127 175 221

Tata Blue 50%

203 215 238

Tata Blue 25%

179 149 197

Purple 50 %

212 195 223

Purple 25 %

255 242 171

Yellow 50 %

255 249 213

Yellow 25 %

229 205 186

Brown 50 %

248 241 235

Brown 25 %

180 213 154

Green 50 %

214 231 200

Green 25 %

241 240 202

Light Green 50%

251 251 241

Light Green 25%

Title and Content

Sensor Services in RIPSAC

OGC Sensor Web Enablement

Sensor Web Vision

Sensor Data Discovery

Sensor Data Access

Synchronous

Asynchronous

Control Sensor parameters

Information Model Interface Specification

• SensorML

• Observation & Measurement ( O & M )

• SOS

• SES

• SPS

SensorML

Sensor ID URN preferred

Sensor Location Last known location - latitude, longitude, (optionally) altitude

Sensor Attributes Is it Mobile ? Is it Active ?

Input Phenomena to Sensor Optional

Sensor Outputs One or more fields along with their type

Offering Sensor outputs are clustered into offering

Sensor Components Optional

• Describe Sensor or Sensor System • Used to model Meta Data about sensors • Can describe physical sensor, sensor system, soft-sensors – method, algorithm

Observation & Measurement

Generic schema for observations & measurements from

heterogeneous sensors

Sampling Time Observation Time

Sensor ID Sensor that produced this observation

Feature of Interest – ID, Location Real-world object which is being observed

Observed Property Sensor Outputs

Result • Numeric, Text, Category, Time, Location, etc

• Scalar, Record, Array of Record • Inline, Out-of-Band

Observation & Measurement Schema

Sensor services

Sensor Observation Service(SOS)

Provides access to observations for heterogeneous sensor systems.

Horizontal model since it applies to all domains that use sensors to

collect data.

Spatio-temporal query support

Sensor Event Service(SES)

Provides publish subscribe based service on different events

exchange between producer and consumer.

Provides registration and publishing of event service.

Provides subscribe service and subsequently notification of service.

Provides subscribe/unsubscribe of choice able events trough different

filtration mechanism.

Sensor services

Sensor Planning Service

• Control sensors and provides operations for task management in

Sensor.

• Checks feasibility ,define , submit a task in sensor

• Provides getStatus, update or modify , cancellation of an running

task.

• Uses WNS to communicate Client in asynchronous manner

• New task created via SPS would subsequently is a service of SOS

Web Notification Service (WNS)

• Provides standard web service interface for asynchronous delivery of

messages or alerts

• The SES either sends the alert directly to the client, or makes use of

the WNS in order to deliver the alert message

SOS – Simple Query

<?xml version="1.0" encoding="UTF-8"?>

<GetObservation xmlns="http://www.opengis.net/sos/1.0"

xmlns:ows="http://www.opengis.net/ows/1.1"

xmlns:gml="http://www.opengis.net/gml"

xmlns:ogc="http://www.opengis.net/ogc"

xmlns:om="http://www.opengis.net/om/1.0"

xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"

xsi:schemaLocation="http://www.opengis.net/sos/1.0

http://schemas.opengis.net/sos/1.0.0/sosGetObservation.xsd"

service="SOS" version="1.0.0" srsName="urn:ogc:def:crs:EPSG::4326">

<offering>GAUGE_HEIGHT</offering>

<observedProperty>urn:ogc:def:phenomenon:OGC:1.0.30:waterlevel</observedProperty>

<responseFormat>text/xml;subtype=&quot;om/1.0.0&quot;</responseFormat>

</GetObservation>

SOS : Spatio-Temporal Query

<offering>GAUGE_HEIGHT</offering>

<eventTime>

<ogc:TM_During>

<ogc:PropertyName>om:samplingTime</ogc:Pro

pertyName>

<gml:TimePeriod>

<gml:beginPosition>2011-10-

01T17:44:15+00:00</gml:beginPosition>

<gml:endPosition>2011-12-

31T17:44:15+00:00</gml:endPosition>

</gml:TimePeriod>

</ogc:TM_During>

</eventTime>

<procedure>urn:ogc:object:feature:Sensor:IFGI:ifgi-

sensor-1</procedure>

<observedProperty>urn:ogc:def:phenomenon:O

GC:1.0.30:waterlevel</observedProperty>

<featureOfInterest>

<ogc:BBOX>

<ogc:PropertyName>urn:ogc:data:location</

ogc:PropertyName>

<gml:Envelope

srsName="urn:ogc:def:crs:EPSG::4326">

<gml:lowerCorner>50.0

7.0</gml:lowerCorner>

<gml:upperCorner>53.0

10.0</gml:upperCorner>

</gml:Envelope>

</ogc:BBOX>

</featureOfInterest>

<result>

<ogc:PropertyIsGreaterThan>

<ogc:PropertyName>urn:ogc:def:phenomenon:

OGC:1.0.30:waterlevel</ogc:PropertyName>

<ogc:Literal>5</ogc:Literal>

</ogc:PropertyIsGreaterThan>

</result>

RIPSAC Demonstrator -Siruseri Smart Campus

Solution

Sensor Interface Services

Storage & Database Services

Analytics Services

RIPSAC + Apps

iDigi M2m Cloud

Bus Tracking System

Parking Management

Cab & Passenger Tracking

Reports & Alerts

Location ( GPS), Speed, Accelerometer, Passenger Ids

Valid passenger lists, Route Info

Images / video feeds of entry & exit ramps of underground parking lots

Images of cab registration number, Passenger Ids

Siruseri Bus Fleet Management - Deployment

Bus Fleet – Sensor Observation & Measurement

Schema

Related Work

Project Who & When Contribution

Cooltown HP Labs, 2001 Web based ubiquitous computing

IrisNet Intel Research & CMU , 2003

World wide sensor web design

CarTel MIT CSAIL , 2007 Vehicular cyber-physical system

CitySense Harvard, 2007 Urban scale wireless sensor network testbed

WikiCity MIT Senseable City Lab, 2007

Real-time urban dynamics ( “Real Time Rome”)

Nericell Microsoft Research, 2008

Road and Traffic Condition monitoring using Mobile Crowdsensing

UBI Univ of Oulu, Finland, 2009

Open Urban Computing testbed

Sensor Web 52 North, Germany OGC SWE reference implementation

Spitfire EU Project Architecture for semantic applications involving Internet connected sensors

Further Reading

1. IrisNet: An Architecture for a Worldwide Sensor Web, Philip B. Gibbons, et.al,

October 2003 IEEE Pervasive Computing , Volume 2 Issue 4

2. OGC Sensor Web Enablement Architecture, Open Geospatial Consortium,

December 2008

3. Using Google App Engine, Charles Severance, O Reilly | Google Press, May

2009

4. Participatory Sensing: Applications and Architecture, Deborah Estrin ,

January/February 2010, IEEE Internet Computing

5. The Internet of Things, Michael Chui, et.al, McKinsey Quarterly 2010,

Number 2

6. Semantic Sensor Network XG Final Report, W3C Incubator Group Report 28,

June 2011

7. SPITFIRE: Towards a Semantic Web of Things, Dennis Pfisterer et.al,

November 2011, IEEE Communication Magazine,

109 207 246

Dark 1

255 255 255

Light 1

131 56 155

Dark 2

0 99 190

Light 2

85 165 28

Accent 1

214 73 42

Accent 2

185 175 164

Accent 3

151 75 7

Accent 4

193 187 0

Accent 5

255 221 62

Accent 6

255 255 255

Hyperlink

236 137 29

Followed Hyperlink

127 175 221

Tata Blue 50%

203 215 238

Tata Blue 25%

179 149 197

Purple 50 %

212 195 223

Purple 25 %

255 242 171

Yellow 50 %

255 249 213

Yellow 25 %

229 205 186

Brown 50 %

248 241 235

Brown 25 %

180 213 154

Green 50 %

214 231 200

Green 25 %

241 240 202

Light Green 50%

251 251 241

Light Green 25%

Title and Content

Thank You Contact: prateep.misra@tcs.com