Io t platform-infotech_arpanpal

Arpan Pal
Principal Consultant and Head, Embedded Systems and Robotics, TCS Research at Tata Consultancy Services en Research and Innovation, Tata Consultancy Services
17 de May de 2015

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Io t platform-infotech_arpanpal

  1. 1Copyright © 2014 Tata Consultancy Services Limited A ubiquitous platform for IoT - bridging the gap between the sensor world and the application world Dr. Arpan Pal Principal Scientist, Innovation Lab, Tata Consultancy Services Ltd., India 17 May 2015
  2. 2 Tata Consultancy Services (TCS) at a Glance Bangalore, India1 Chennai, India2 Cincinnati, USA3 Delhi, India4 Hyderabad, India5 Kolkata, India6 Mumbai, India7 Peterborough, UK8 Pune, India9 2000+ Associates in Research, Development and Asset Creation 1 2 3 4 597 6 8 10 Singapore10 Innovation @ 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  300,000+ associates  USD 15Billion+ annual revenue  Global presence – 55+ countries, 119 nationalities  First Software R&D Center in India
  3. 3 Internet-of-Things – at the peak of the Hype?
  4. 4 Internet-of-Things – what does it really mean? M2M Communication Sensing the human – quantified self Embedded software and Hardware Cloud, Mobile, Big Data and Analytics Wireless Sensor Networks, Pervasive Computing Sensors and Actuators
  5. 5 The Internet of Everything Humans Physical Objects and Infrastructure Computing Infrastructure Physical Context Discovery INTERNET OF EVERYTHING Physical Context Discovery What is happening, where and when People Context Discovery Who is doing what, where and when, who is thinking what Internet of Digital Internet of Things Internet of Humans ABI Research. May 7, 2014 • New Business / Pricing Models • Customer becomes the focus, not the product or service – key is understanding the Customer
  6. 6 Platform Requirements for IoT TCS Connected Universe Platform (TCUP) A horizontal platform for addressing the IoT Software and Services market Applications need support for Visibility Capture & store data from sensors Insights Patterns, relationships and models Control Optimize and actuate TCUP Platform
  7. 7 TCUP Architecture Sensor Services Sensor Registration / Describe Sensor Get Sensor Capabilities Insert Observation Get Observation Device Management Edit configuration Update device firmware Download software and remote status monitoring Alarms and notification Analytics Framework Register Job Deploy / Undeploy Job Get Job Status Start / Restart / Stop Job Search Job Flexible Interfaces for easy application development and integration Adopt Open-source and Open Standards TCUP API Classes APPhonics Develop– Test - Publish - Manage People Things TCUPKnome DevelopersDevelopers
  8. 8 Challenges for IoT Platform Scalability Privacy Affordability Context-awareness Ease-of-Development Security S A E C P S Analytics is the Key
  9. 9 IoT Analytics – what does it really mean?
  10. 10 Analytics-as-a-Service Algorithm Recommendation: Ease-of- Development and Fusion Analytics Libraries: Affordability via Reuse and Knowledge Modeling Base TCUP Platform (Sensor Data Transport, Storage and Analysis) Scalability and Affordability: Utilize Edge Devices – seamless distributed computing (Fog) Prescriptive DescriptiveDescriptiveDescriptivePredictive Diagnostic
  11. 11 Sensor-agnostic Anomaly Detection – Remote Health Monitoring Sensed data – PPG, ECG, HR, BP, Heart Sound, Smart-Meter ….. Outlier Detection Information Measure Generate Alerts based on critical information Preventive Healthcare Promote WellnessSensor agnostic outlier analysis library Refer to Doctors Being Tested on ECG, PPG and EEG Data • Anomaly within same source, same time • Anomaly within same source, different time • Anomaly between different sources • Knowledge model – sensor data type dependency for outlier algorithms
  12. 12 Behavior Sensing – Crowd sourcing of people context using mobile phones Indoor Localization – Bldg, Mall • Entry-Exit and Zoning • Fine-grained positioning Activity Detection - Wellness • Walking / Brisk Walking / Jogging / Running • Calorie Burnt Traffic Sensing – City Authority • Congestion Modeling • Honk Detection • Road Condition Monitoring Driving Behavior - Insurance • Hard Cornering / Breaking Magnetometer – Entry/Exit WiFi -Zoning Bluetooth - Proximity RFID Fusion 98% 97% 96% 99.7% (Accuracy ~2m) (Accuracy ~ 98%) Mobile phone sensors – Magnetometer, Wi-Fi, Bluetooth, Accelerometer, Microphone, GPS Knowledge – Sensor Noise Models, physical world models is form of building plan, road maps, driver- vehicle interaction models
  13. 13 Measurement – using Camera Vision for Physical World Metrics eGarment Fitting – Online Retail • Web cam based affordable system at home • Real-time 3D reconstruction is a challenge Accident Damage Assessment - Insurance • Mobile phone camera based Insurance Application • Template based damage assessment Postal Packaging Automation - Online Resellers • Mobile Camera based System • Camera vision based approach • 3D reconstruction from 2D images • Affordable, quick to deploy systems Sensors - Mobile Phone Camera, Webcams Knowledge – Physical Object 3D Models (Human, Car, Box)
  14. 14 Vision: Democratizing IoT App Development I only know the business logic, I do not know how to code, nor do I understand analytics algorithms… I know how to code, but I do not know algorithms, nor do I know about the business logic… Oh, I know algorithms, but I can’t code for your mobile devices… I have all these cloud and edge nodes which you can use to deploy the app… Need of the Day - Knowledge-driven Framework for IoT App Development
  15. 15 Model-driven-development for IoT – Separation of Concerns through Knowledge Modeling • Knowledge models include rules, ontologies, Information flow graphs, physical models • Ratified / Augmented by experts (domain, sensor, algorithm and infrastructure)
  16. 16 Publication List Anomaly Detection and Compression 1. A Ukil, et. al., “Adaptive sensor data compression in IoT systems: sensor data analytics based Approach”, ICASSP 2015 2. One more Crowd-sensing via Mobile Phones 1. Nasimuddim Ahmed et. al., ""SmartEvacTrak: A People Counting and Coarse-Level Localization Solution for Efficient Evacuation of Large Buildings“, CASPER'15 workshop of IEEE Percom 2013 2. Sourjya Sarkar et. al. “Improving the Error Drift of Inertial Navigation based Indoor Location Tracking” , IPSN 2015 3. Vivek Chandel, "AcTrak - Unobtrusive Activity Detection and Step Counting using Smartphones“, Mobiquitous 2013 4. Ghose, Avik et. al., "Road condition monitoring and alert application: Using in-vehicle smartphone as internet-connected sensor.“, Percom Workshops 2012. 5. Tapas Chakravarthy et. al., “MobiDriveScore — A system for mobile sensor based driving analysis: A risk assessment model for improving one's driving”, ICST 2013 6. Maiti, Santa, et al. "Historical data based real time prediction of vehicle arrival time." ITSC 2014 3D Vision based Measurements 1. Saha, Arindam et. al.,"A System for Near Real-Time 3D Reconstruction from Multi-view Using 4G Enabled Mobile." IEEE Mobile Services (MS), 2014 2. Brojeshwar Bhowmick et. al., “Mobiscan3D: A low cost framework for real time dense 3D reconstruction on mobile devices”, IEEE UIC 2014 Model-driven Development 1. A. Pal et al., “Model-Driven Development for Internet of Things: Towards Easing the Concerns of Application Developers,” IoT as a Service (IoTaaS), 2014 2. S. Dey et al., “Challenges of Using Edge Devices in IoT Computation Grids,” ICPADS 2013 IoT Platform 1. P. Balamuralidhara et al., “Software Platforms for Internet of Things and M2M,” Journal of. Indian Inst. of Science 2. - TCUP Platform Page
  17. 17 Thank You