Arpan pal u world2012
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This document proposes a system for personal and community context discovery as a service. It suggests using mobile phone sensors, 3D cameras, and web data in a non-intrusive way to gather information on location, proximity, activity, and identity to discover individual behaviors and community patterns. A multimodal fusion approach is recommended to integrate these context sources for improved accuracy. An IoT platform is presented as a way to implement and deploy this context discovery service for application developers.
Arpan pal u world2012
- 1. 1 Personal and Community Context Discovery 17 May 2015 Arpan Pal Principal Scientist and Research Head Innovation Lab, Cyber physical Systems Tata Consultancy Services (TCS)
- 2. 2 Tata Consultancy Services Ltd. (TCS) Pioneer & Leader in Indian IT TCS was established in 1968 One of the top ranked global software service provider Largest Software service provider in Asia 250,000+ associates USD 10B + annual revenue Global presence First Software R&D Center in India - 2 -
- 3. 3 The Heart of Innovation – TCS Innovation Labs Bangalore, India1 TCS Innovation Labs - Bangalore Chennai, India2 TCS Innovation Labs - Chennai TCS Innovation Labs - Retail TCS Innovation Labs - Travel & Hospitality TCS Innovation Labs - Insurance TCS Innovation Labs - Web 2.0 TCS Innovation Labs - Telecom Cincinnati, USA3 TCS Innovation Labs - Cincinnati Delhi, India4 TCS Innovation Labs - Delhi Hyderabad, India5 TCS Innovation Labs - Hyderabad TCS Innovation Labs - CMC Kolkata, India6 TCS Innovation Labs - Kolkata Mumbai, India7 TCS Innovation Labs - Mumbai TCS Innovation Labs - Performance Engineering Peterborough, UK8 TCS Innovation Labs - Peterborough Pune, India9 TCS Innovation Labs - TRDDC - Process Engineering TCS Innovation Labs - TRDDC - Software Engineering TCS Innovation Labs - TRDDC - Systems Research TCS Innovation Labs - Engineering & Industrial Services 1 2 3 4 5 97 6 8 2000 + Associates in Research, Development and Asset Creation 19 Innovation Labs
- 4. 4 Integrated Platform for Intelligent Infrastructure People Feedback & Emotions Social Media Integrated Services Sensors & IoT Platform Traditional Monitoring & Control Systems Citizen Data Smart Integration Platform Transportation Healthcare Electricity WaterPublic Safety Tourism Smart Integrated Services Sense Analyze Extract Respond Intelligence Smart Domain Services Community etc. Sense: People Activity, Appliances, Vehicles , Road, Home/Bldg, Utility Infrastructure Detect gas leakage/water contamination : mobilize rescue team, suggest optimum route Divert Road Traffic in case of Water Pipeline Burst Correlate Electricity/Water /Gas consumption patterns Intelligent Integration Platform Integrated Intelligent Services
- 5. Outline What we mean by Context Example Use Cases Proposed System Conclusion
- 6. 6 Personal and Community Context Discovery Context - patterns of individual, group and societal behaviours. Broadly classified into three categories – Personal Physical Network Discovery Who is interacting with whom? What is the level of interaction? Who all are part of similar-interest networks? Individual Context Discovery Who is doing what? Community Context Discovery Can we discover how a community / group behaves as a whole?
- 7. 7 Example Use Case - Campus Source: Zhang et. al., “Extracting Social and Community Intelligence from Digital Footprints: An Emerging Research Area”, UIC 2010, LNCS 6406, pp. 4–18, 2010. © Springer-Verlag Berlin Heidelberg 2010
- 8. 8 Other Example Use Cases Organizational Behavior Analysis Team Efficiency Study Best Practice Study Workspace Ergonomics Study Customer Behavior Study in Retail Stores Customer movement pattern Customer interaction pattern with shelves / merchandize Crowdedness measure in public places Efficient scheduling of public transport Crowd Behavior analysis Evacuation planning during disaster Ref. - Alex Pentland et. al., MIT media Lab
- 9. 9 What do we need to Sense Location Proximity Activity Identity Provide Context discovery as a Service
- 10. 10 How to Sense Needs to be Ubiquitous and Unobtrusive There should not be any new hardware / device to carry for an individual Proposal o Use smartphone-based sensors (GPS, accelerometer, compass, microphone) o Use 3D surveillance cameras (like Kinect) o Augment with social network data and email data analytics o Multimodal fusion of all the above Privacy can be an issue – needs to be handled on an use case-by-use case basis o Privacy vs. Utility
- 11. 11 Proposed Architecture Platform Service c Sensors (location, proximity, activity) Camera Web-based soft sensing Personal Physical Network Discovery Individual Context Discovery Microphone Community Context Discovery Behavioral Analytics Applications Context Discovery Service Mobile Phones, Kinect, Email, Social Network cMultimodal Fusion
- 12. 12 Mobile Phone Based Sensing Proximity / presence – Using Bluetooth for finding nearby mobiles – Using Wi-Fi to discover other mobiles nearby Location – Using ultrasound beacon – Using GPS (outdoors) – Using Accelerometer / compass Activity – Using Accelerometer Interaction Level – Using Microphone Audio Identity – From Network ID On-board sensors Accelerometer, GPS, Compass Camera, Microphone Network Bluetooth, WiFi, 2G/GPRS, 3G Network 2G/GPRS, Bluetooth On-board sensors Microphone, Camera
- 13. 13 Sensor Penetration and power consumption in Mobile Phones 0 20 40 60 80 100 Bluetooth USB Edge GPRS Wifi 3G Camera GPS Accelarometer Digital Compass Consolidated Market Penetration Source: Nericell: Rich Monitoring of of Road and Traffic Conditions using Mobile Smartphones, Prashant Mohan et. al., Microsoft Research, SenSys 2008, North Carolina, USA
- 14. 14 Kinect Based Sensing Human Identification – Skeleton Model Based – External Stimulus based refinement Network Discovery – Network discovery through proximity – Level of Interaction through Audio • 2D Camera with IR depth sensor • Excitation by IR light pattern • Directional Mic.
- 15. 15 Kinect Based Sensing (contd …) Working on a public Kinect dataset • People Discussion • Give/Put/Take an object • Enter/leave a room • Leave baggage unattended • Handshaking • Typing on a keyboard • Telephone conversation (Mobile, landline) Image and the corresponding 3D cloud point Human Interaction – Activity Detection on 3D Point Cloud – Physical object Identification – Interaction with Objects Human activities recognition and localization competition (HARL), ICPR 2012
- 16. 16 Soft sensing from Web Unstructured Data • Social network posts such as tweets, facebook • Blog posts • Email bodies Structured Data • Social network profiles and network information • Email headers • Tweet Attributes Personal Network
- 17. 17 IoT Platform from TCS Internet End Users Administrators Device Integration & Management Services Analytics Services Application Services Storage Messaging & Event Distribution Services ApplicationServices Presentation Services Application Support Services Middleware Edge Gateway Sensors Internet Back-end on Cloud RIPSAC – Real-time Integrated Platform for Services & AnalytiCs Traditional Internet Service Delivery Platform & App Development Platform Security/Privacy Framework Lightweight M2M Protocols Analytics-as-a- Service Social Network Integration SDKs and APIs for App developer
- 18. 18 Summary Introduced the concept of personal and community context discovery as a service with help of example use cases Proposed an unobtrusive and ubiquitous way to gather the context through mobile phone based sensors, 3D camera and web data Each method has its own limitation both from application and technology perspective Need for a multimodal fusion for improved accuracy A generic IoT platform to implement and deploy the services for application developers