Machine Learning Applications to IoT
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Internet of Things (IoT) is growing rapidly in decades, various applications came out from academia and industry. IoT is an amazing future to the Internet, but there remain some challenges to IoT for human have never dealt with so many devices and so much amount of data. Machine Learning (ML) is the technique that allows computers to learn from data without being explicitly programmed. Generally, the aim is to make predictions after learning and the process operates by building a model from the given (training) data and then makes predictions based on that model. Machine learning is closely related to artificial intelligence, pattern recognition and computational statistics and has strong relationship with mathematical optimization. In this talk, we focus on ML applications to IoT. Specially, we focus on the existing ML techniques that are suitable for IoT. We also consider the issues and challenges for solving the IoT problems using ML techniques.
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Machine Learning Applications to IoT
- 1. Introduction ML and IoT Our Work UbiMedia Lab Machine Learning Applications to IoT Dr. Hari Prabhat Gupta and Dr. Tanima Dutta Assistant Professor(s), Department of Computer Science and Engineering Indian Institute of Technology (BHU) Varanasi, India Email: {hariprabhat.cse,tanima.cse}@iitbhu.ac.in 1/55
- 2. Introduction ML and IoT Our Work UbiMedia Lab Outline 1 Introduction Internet of Things (IoT) Machine Learning (ML) 2 ML and IoT ML and IoT: An Example Scenario Major Components of IoT 3 Our Work Smart Connectivity Smart Education Smart Life Smart Environment 4 UbiMedia Lab About UbiMedia Lab Ongoing Projects 2/55
- 3. Introduction ML and IoT Our Work UbiMedia Lab Internet of Things (IoT) Machine Learning (ML) Types of Internet of Things (IoT) Security and Privacy Smart (Cities and Homes) Devices (Wearable and Connecting) BlockChains Industrial automation 3/55
- 4. Introduction ML and IoT Our Work UbiMedia Lab Internet of Things (IoT) Machine Learning (ML) Security and Privacy Highly security-centric “life-and-death” applications Intermediate security uses that include: smart home Lower security casual uses such as: games All images source: https://www.google.com/ 4/55
- 5. Introduction ML and IoT Our Work UbiMedia Lab Internet of Things (IoT) Machine Learning (ML) Smart Home Area Network (Zigbee, BLE, WiFi): Smart Homes Wide Area Network: Smart Cities Field Area Networks: Smart End-to-End 5/55
- 6. Introduction ML and IoT Our Work UbiMedia Lab Internet of Things (IoT) Machine Learning (ML) Smart 6/55
- 7. Introduction ML and IoT Our Work UbiMedia Lab Internet of Things (IoT) Machine Learning (ML) Govt. of India 7/55
- 8. Introduction ML and IoT Our Work UbiMedia Lab Internet of Things (IoT) Machine Learning (ML) Devices:Wearable and Connecting 8/55
- 9. Introduction ML and IoT Our Work UbiMedia Lab Internet of Things (IoT) Machine Learning (ML) BlockChains 9/55
- 10. Introduction ML and IoT Our Work UbiMedia Lab Internet of Things (IoT) Machine Learning (ML) Machine Learning Tom M. Mitchell: A computer program is said to learn from Experience E with respect to some Task T and some Performance measure P, if its performance on T, as measured by P, improves with experience E. Learning means Improving with Experience at some Task Example: Smart Homes T: Estimate the desired temperature E: Learning from temperature dataset P: Accuracy of the desired temperature 10/55
- 11. Introduction ML and IoT Our Work UbiMedia Lab Internet of Things (IoT) Machine Learning (ML) Traditional vs Machine Learning algorithms (Walk/Run) Accelerometer Data (Rules Learned) Walk vs Run Machine Learning for many different people) (Accelerometer data collected Algorithm (Walk/Run) Accelerometer Data (Programmed) (Hard−coded Instruction) Computer Program Walk/Run Traditional Programming: Machine Learning: 11/55
- 12. Introduction ML and IoT Our Work UbiMedia Lab Internet of Things (IoT) Machine Learning (ML) Traditional vs Machine Learning algorithms 12/55
- 13. Introduction ML and IoT Our Work UbiMedia Lab Internet of Things (IoT) Machine Learning (ML) ML algorithms 13/55
- 14. Introduction ML and IoT Our Work UbiMedia Lab Internet of Things (IoT) Machine Learning (ML) ML Algorithms: Regression How to estimate the price of a fruit if freshness (in days) is given? 14/55
- 15. Introduction ML and IoT Our Work UbiMedia Lab Internet of Things (IoT) Machine Learning (ML) ML Algorithms: Regression Freshness of fruits (in days, 100−0) Price(inINR0−100) 15/55
- 16. Introduction ML and IoT Our Work UbiMedia Lab Internet of Things (IoT) Machine Learning (ML) ML Algorithms: Regression How to estimate the price of a fruit if freshness (in days) is given? Input Training Data (labeled instances (x1, y1), (x2, y2), . . . , (xn, yn)) e.g., freshness of fruits and prices Objective Develop a relation (or rule f : x → y) to predict y for given x e.g., a new fruit xnew with given fresh or not Output Discrete-valued y e.g., Fresh or not ynew of the fruit xnew 16/55
- 17. Introduction ML and IoT Our Work UbiMedia Lab Internet of Things (IoT) Machine Learning (ML) ML Algorithms: Regression Price(inINR0−100) Freshness of fruits (in days, 100−0) 17/55
- 18. Introduction ML and IoT Our Work UbiMedia Lab Internet of Things (IoT) Machine Learning (ML) ML Algorithms: Classification How to identify where a given fruit is fresh (if freshness is not more than 50 days) or not? 18/55
- 19. Introduction ML and IoT Our Work UbiMedia Lab Internet of Things (IoT) Machine Learning (ML) ML Algorithms: Classification 19/55
- 20. Introduction ML and IoT Our Work UbiMedia Lab Internet of Things (IoT) Machine Learning (ML) ML Algorithms: Classification How to identify where a given fruit is fresh (if freshness is not more than 50 days) or not? Input Training Data (labeled instances (x1, y1), (x2, y2), . . . , (xn, yn)) e.g., freshness of fruits and prices Objective Develop a relation (or rule f : x → y) to predict y for given x e.g., a new fruit xnew with given freshness (in days) Output Real-valued y e.g., Price ynew of the fruit xnew 20/55
- 21. Introduction ML and IoT Our Work UbiMedia Lab Internet of Things (IoT) Machine Learning (ML) ML Algorithms: Classification 21/55
- 22. Introduction ML and IoT Our Work UbiMedia Lab Internet of Things (IoT) Machine Learning (ML) ML Algorithms: Clustering How to put the fruits in two buckets? 22/55
- 23. Introduction ML and IoT Our Work UbiMedia Lab Internet of Things (IoT) Machine Learning (ML) Input Training Data (Unlabeled instances x1, x2, . . . , xn) Objective Learn more about the data distribution Output Discover the inherent groupings in data. 23/55
- 24. Introduction ML and IoT Our Work UbiMedia Lab Internet of Things (IoT) Machine Learning (ML) Supervised & Unsupervised ML Algorithms 1 How to estimate the price of a fruit if freshness (in days) is given? 2 How to identify where a given fruit is fresh (if freshness is not more than 50 days) or not? 3 How to put the fruits in two buckets? Supervised ML algorithms. 1 Regression: Estimate the real-value. 2 Classification: Estimate the Discrete-value. Unsupervised ML algorithms 1 Clustering: Grouping the given items. 24/55
- 25. Introduction ML and IoT Our Work UbiMedia Lab Internet of Things (IoT) Machine Learning (ML) Example of ML Algorithms 1 Supervised: Decision trees, Nearest-neighbors methods, regression and classification 2 Unsupervised: Clustering, Dimensionality reduction, Association In this workshop, we focus on: Linear Regression Logical Regression K-NN Decision trees Support Vector Machine K-mean Clustering 25/55
- 26. Introduction ML and IoT Our Work UbiMedia Lab ML and IoT: An Example Scenario Major Components of IoT Internet 26/55
- 27. Introduction ML and IoT Our Work UbiMedia Lab ML and IoT: An Example Scenario Major Components of IoT Major Components of IoT Sensor technology Communication technology Machine Learning Human-machine interface (UI/UX) 27/55
- 28. Introduction ML and IoT Our Work UbiMedia Lab ML and IoT: An Example Scenario Major Components of IoT Sensor technology: Smartphone, Arduino, Communication technology: BLE, WIFI Machine Learning: 10 ML algorithms Human-machine interface (UI/UX): Android 28/55
- 29. Introduction ML and IoT Our Work UbiMedia Lab ML and IoT: An Example Scenario Major Components of IoT Traditional vs Machine Learning algorithms 29/55
- 30. Introduction ML and IoT Our Work UbiMedia Lab ML and IoT: An Example Scenario Major Components of IoT Abstract View of ML 30/55
- 31. Introduction ML and IoT Our Work UbiMedia Lab Smart Connectivity Smart Education Smart Life Smart Environment ML → Internet and ML → Things Machine Learning to Internet Machine Learning to Things 31/55
- 32. Introduction ML and IoT Our Work UbiMedia Lab Smart Connectivity Smart Education Smart Life Smart Environment Introduction Smart Connectivity in IoT Smart Education, Home, and Environment Deployment SDN and BlockChains 32/55
- 33. Introduction ML and IoT Our Work UbiMedia Lab Smart Connectivity Smart Education Smart Life Smart Environment Connectivity in IoT for smart home BLE Mesh Network Samsung R&D Bangalore, “A Mesh Network for Mobile Devices using Bluetooth Low Energy”, In IEEE Sensors 2015, South Korea, pp. 1-4, 2015. 33/55
- 34. Introduction ML and IoT Our Work UbiMedia Lab Smart Connectivity Smart Education Smart Life Smart Environment BLE Mesh Network at Samsung R&D Bangalore 34/55
- 35. Introduction ML and IoT Our Work UbiMedia Lab Smart Connectivity Smart Education Smart Life Smart Environment BLE Mesh Network at Samsung R&D Bangalore 35/55
- 36. Introduction ML and IoT Our Work UbiMedia Lab Smart Connectivity Smart Education Smart Life Smart Environment BLE Mesh Network at Samsung R&D Bangalore 36/55
- 37. Introduction ML and IoT Our Work UbiMedia Lab Smart Connectivity Smart Education Smart Life Smart Environment Connectivity in IoT for smart home Edge-Fog-Cloud Network Surbhi Saraswat, Hari Prabhat Gupta, and Tanima Dutta, “Fog based Energy Efficient Ubiquitous System”. 37/55
- 38. Introduction ML and IoT Our Work UbiMedia Lab Smart Connectivity Smart Education Smart Life Smart Environment Edge-Fog-Cloud Network Edge Management Framework Fog Layer Cloud Layer Fog Management Framework Edge Layer Sensor Actuator Edge Relay Fog Device Gateway Device Edge Device Sensors Fog Relay 38/55
- 39. Introduction ML and IoT Our Work UbiMedia Lab Smart Connectivity Smart Education Smart Life Smart Environment Connectivity in IoT for smart home SDN Network Vishal Agarwal, Surbhi Saraswat, Hari Prabhat Gupta, and Tanima Dutta, “A Priority based Traffic Engineering Framework in Software Defined Networks”. 39/55
- 40. Introduction ML and IoT Our Work UbiMedia Lab Smart Connectivity Smart Education Smart Life Smart Environment SDN Network Switch Switch Switch Switch Switch Network Topology Network Constraints Control Admission Active Flows Load Monitor Policy Pricing Path Assignment h1 1 2 3 4 5 h2 Controller App 2 App 3App 1 4 40/55
- 41. Introduction ML and IoT Our Work UbiMedia Lab Smart Connectivity Smart Education Smart Life Smart Environment Smart Pencil Smart Pencil “S-Pencil: A Smart Pencil Grip Monitoring System for Kids using Sensors”, in proc. of the IEEE Globecom 2017, Singapore, December 04 - 08, 2017. 41/55
- 42. Introduction ML and IoT Our Work UbiMedia Lab Smart Connectivity Smart Education Smart Life Smart Environment A Smart Pencil Grip Monitoring System using Sensors (f) (a) (b) (c) (e)(d) 42/55
- 43. Introduction ML and IoT Our Work UbiMedia Lab Smart Connectivity Smart Education Smart Life Smart Environment A Smart Pencil Grip Monitoring System using Sensors (a) (b) Bluetooth PressureSensor Pressure Sensor Accelerometer 43/55
- 44. Introduction ML and IoT Our Work UbiMedia Lab Smart Connectivity Smart Education Smart Life Smart Environment A Smart Pencil Grip Monitoring System using Sensors Z−axis X−axis Y−axis Accelerometer 44/55
- 45. Introduction ML and IoT Our Work UbiMedia Lab Smart Connectivity Smart Education Smart Life Smart Environment A Smart Pencil Grip Monitoring System using Sensors Writing Activity Set Ë Ð Ø ÓÒ Ë Ò×ÓÖ Ø ÈÖ ÔÖÓ ×× Ò Ø ÜØÖ Ø ÓÒ ØÙÖ Ø × Ø Ú ØÝ Ê Ó Ò Ø ÓÒ ÕÙ × Ø ÓÒ ÏÖ Ø Ò Ø Ú ØÝ 45/55
- 46. Introduction ML and IoT Our Work UbiMedia Lab Smart Connectivity Smart Education Smart Life Smart Environment Smart Life Smart Life “A Continuous Hand Gestures Recognition Technique for Human-Machine Interaction using Accelerometer and Gyroscope sensors”, IEEE Sensors Journal, Vol. 16, no. 16, pp. 6425-6432, 2016. 46/55
- 47. Introduction ML and IoT Our Work UbiMedia Lab Smart Connectivity Smart Education Smart Life Smart Environment A Continuous Hand Gestures Recognition Technique g1 g3g2 g4 g5 g6 47/55
- 48. Introduction ML and IoT Our Work UbiMedia Lab Smart Connectivity Smart Education Smart Life Smart Environment A Continuous Hand Gestures Recognition Technique Accelerometer sensor Gyroscope sensor Gesture EventSensor Data Action Command mapping module Gesture −> Action AllShare −> Triggering action on appliance CHG Technique Appliance 48/55
- 49. Introduction ML and IoT Our Work UbiMedia Lab Smart Connectivity Smart Education Smart Life Smart Environment A Continuous Hand Gestures Recognition Technique 0 20 40 60 80 100 Variencesofthesensorydata Coded sensory data Gesture Gesture Gesture Gesture Coded sensory data of x-axis Coded sensory data of y-axis Coded sensory data of z-axis Mean value of the coded sensory data 49/55
- 50. Introduction ML and IoT Our Work UbiMedia Lab Smart Connectivity Smart Education Smart Life Smart Environment Smart Environment Smart Environment “Leveraging Smart Devices for Automatic Mood-transferring in Real-time Oil Painting”, IEEE Transactions on Industrial Electronics, Volume 64, Issue 2, pp. 1581-1588, 2017. 50/55
- 51. Introduction ML and IoT Our Work UbiMedia Lab Smart Connectivity Smart Education Smart Life Smart Environment Automatic Mood-transferring in Real-time Oil Painting Romantic Refreshing Displeased Calm Energetic Nervous Pleasant Digital Oil Painting Mood Selection Color Selection Light Selection 51/55
- 52. Introduction ML and IoT Our Work UbiMedia Lab Smart Connectivity Smart Education Smart Life Smart Environment Automatic Mood-transferring in Real-time Oil Painting (c)(b)(a) 52/55
- 53. Introduction ML and IoT Our Work UbiMedia Lab About UbiMedia Lab Ongoing Projects Ubiquitous and Multimedia Lab Lab Space: 1000m2 Sponsor agencies: SERB, University of Chicago, Tata Centre for Development Chicago, Samsung R&D Terminals: 50+ High End Machines: Servers (Unix, Windows, and Solaris) and High Performance Computing (HPC) Sensors and Smart Devices: Texas Instruments, Libelium kits, Gopro Cameras, Hikvision PTZ cameras setups and other different 200+ type of Sensors and Smart Devices Current Students Doctoral Student: 10+ Post Graduate Students: 2 Junior Research Fellow: 2 Undergraduate (in projects): 10+ Ongoing short term courses, workshops, seminars: 5+ 53/55
- 54. Introduction ML and IoT Our Work UbiMedia Lab About UbiMedia Lab Ongoing Projects Running Projects An Energy-efficient WSN for Precision Agriculture Agency: SERB, Govt. of India, Duration: 2017-2020. Smart Life Samsung R&D Bangalore, Duration: May-July 2017. Water Monitoring using Sensors with Prof. Supratik Guha (University of Chicago), Agency: TCD Chicago, Duration: 2018-2020. A Robust Medical Image Forensics System for Smart Healthcare Agency: SERB, Govt. of India, Duration: 2018-2020. 54/55
- 55. Introduction ML and IoT Our Work UbiMedia Lab About UbiMedia Lab Ongoing Projects Thank You 55/55