A Context and User Aware Smart Notification System
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The document presents a prototype of a context and user aware smart notification system. The goal is to develop a system that can filter incoming notifications using machine learning based on notification information, environment status, user context, and user habits. The proposed architecture is modular and aware of these factors. Preliminary results show support vector machines and decision trees had the best performance at predicting the correct notification delivery method, with support vector machines being the most accurate but slowest. Future work includes collecting real notification data and evaluating additional machine learning algorithms.
![54
Prototype implementation
Prototype
94 people over 9 months
monitored through
smartphones in 2004:
• Sender
• Receiver
• Type of notification
• Timestamp of receipt
• User current location
Dataset
Synthetic data:
• User current
activity
• Available devices
for the user
• Target device.
Real + synthetic dataset:
165,289 samples, almost one per
each hour of the day
(the missing samples are related
to hours in which users turned
off their smartphones)
Information collected by Decision Maker in previous example
{
“notification“:{
“senderName“:“mySmartHome“,
“type“:“smart Home Notification“,
“receiptTimestamp“:“1447347600“
},
“userStatus“: {
“senderId“:“359“,
“currentActivity“:“STILL“,
“currentActivityConfidence“:“50%“,
“availableDevices”:[“deviceId”:”23”]
},
“deviceStatus“:{
“deviceId“:”23”,
“category“:”Smartphone”,
“currentStatus“:”On”,
“currentMode“:”Ring”,
“wifiStatus“:” Connected through MOBILE”,
“batteryLevel“:” 57%”,
“batteryStatus“:”BATTERY_STATUS_NOT_CHARGING”
}
}](https://image.slidesharecdn.com/2015-wfiot-smartnotifications-presentation-151217160436/85/A-Context-and-User-Aware-Smart-Notification-System-54-320.jpg)
A Context and User Aware Smart Notification System
- 1. A Context and User Aware Smart Notification System Fulvio Corno Luigi De Russis Teodoro Montanaro* http://jol.telecomitalia.com/j olswarm/ http://elite.polito.it/
- 2. 2 Outline 1. Context and Motivation 2. Goal 3. Architecture 4. Prototype 5. Preliminary results 6. Conclusion 7. Future work
- 3. 3 Context Context Infographic from "The Connectivist": growing of IoT connected devices (http://www.theconnectivist.com/2014/05/infographic-the-growth-of-the-internet-of-things/)
- 5. 5 Motivation Motivation IoT devices can generate, receive and show different kinds of notifications
- 6. 6 Motivation Motivation IoT devices can generate, receive and show different kinds of notifications
- 7. 7 Motivation Motivation IoT devices can generate, receive and show different kinds of notifications
- 8. 8 Motivation Motivation IoT devices can generate, receive and show different kinds of notifications The number of notifications is growing
- 9. 9 Motivation Motivation IoT devices can generate, receive and show different kinds of notifications The number of notifications is growing
- 10. 10 Motivation Motivation IoT devices can generate, receive and show different kinds of notifications Nowadays the same notification is replicated on all available devices The number of notifications is growing
- 11. 11 Motivation Motivation IoT devices can generate, receive and show different kinds of notifications Nowadays the same notification is replicated on all available devices The number of notifications is growing
- 12. 12 Motivation Motivation IoT devices can generate, receive and show different kinds of notifications Nowadays the same notification is replicated on all available devices The number of notifications is growing The benefit of displaying the same notification on all available devices could put user patience to a hard test
- 13. 13 Analyze how machine learning approach can improve IoT notification user experience Goal Goal
- 14. 14 Analyze how machine learning approach can improve IoT notification user experience Goal Goal
- 15. 15 Analyze how machine learning approach can improve IoT notification user experience Goal Goal Develop a system able to filter incoming notifications depending on: • Notification information • Environment status • User context • User habits
- 16. 16 Analyze how machine learning approach can improve IoT notification user experience Goal Goal Develop a system able to filter incoming notifications depending on: • Notification information • Environment status • User context • User habits
- 17. 17 Analyze how machine learning approach can improve IoT notification user experience Goal Goal Develop a system able to filter incoming notifications depending on: • Notification information • Environment status • User context • User habits Evaluate machine learning approach
- 19. 19 We propose: A modular architecture Architecture Architecture
- 20. 20 We propose: A modular architecture Architecture Architecture
- 21. 21 We propose: A modular architecture Architecture Architecture
- 22. 22 We propose: A modular architecture aware of Architecture Architecture
- 23. 23 We propose: Environment status (e.g., weather information, current date and time) A modular architecture aware of Architecture Architecture
- 24. 24 We propose: Environment status (e.g., weather information, current date and time) User context (e.g., location, status, current activity), A modular architecture aware of Architecture Architecture
- 25. 25 We propose: Environment status (e.g., weather information, current date and time) User context (e.g., location, status, current activity), User habits A modular architecture aware of Architecture Architecture
- 26. 26 We propose: A modular architecture Architecture Architecture
- 27. 27 We propose: Decision maker: Machine Learning algorithm makes decisions (best devices + best modes + best moment). Architecture Architecture
- 29. 29 Architecture: example Architecture Mario is in a meeting
- 30. 30 Architecture: example Architecture Mario is in a meeting
- 31. 31 Architecture: example Architecture Every meeting lasts at least 2 hours Mario is in a meeting
- 32. 32 Architecture: example Architecture Every meeting lasts at least 2 hours Mario is in a meeting
- 33. 33 Architecture: example Architecture Time: 17:00 Meeting started at 16:00 Wife is at home Every meeting lasts at least 2 hours Mario is in a meeting
- 34. 34 Architecture: example Architecture Time: 17:00 Meeting started at 16:00 Wife is at home Every meeting lasts at least 2 hours Mario is in a meeting
- 35. 35 Architecture: example Architecture Notification:the toilet paper has just finished Time: 17:00 Meeting started at 16:00 Wife is at home Every meeting lasts at least 2 hours Mario is in a meeting
- 36. 36 Architecture: example Architecture Notification:the toilet paper has just finished Time: 17:00 Meeting started at 16:00 Wife is at home Every meeting lasts at least 2 hours Mario is in a meeting
- 37. 37 Architecture: example Architecture Notification:the toilet paper has just finished Time: 17:00 Meeting started at 16:00 Wife is at home Every meeting lasts at least 2 hours Mario is in a meeting
- 38. 38 Architecture: example Architecture Notification:the toilet paper has just finished Notify: • At 18:10 • on his personal smartphone • Sound Time: 17:00 Meeting started at 16:00 Wife is at home Every meeting lasts at least 2 hours Mario is in a meeting
- 39. 39 Architecture: example Architecture Notification:the toilet paper has just finished Notify: • At 18:10 • on his personal smartphone • Sound Time: 17:00 Meeting started at 16:00 Wife is at home Every meeting lasts at least 2 hours Mario is in a meeting
- 40. 40 Architecture: example Architecture Notification:the toilet paper has just finished Notify: • At 18:10 • on his personal smartphone • Sound Time: 17:00 Meeting started at 16:00 Wife is at home Every meeting lasts at least 2 hours Mario is in a meeting
- 42. 42Prototype Prototype implementation Aim: evaluate machine learning approach to decide • who should receive an incoming notification; • the best moment to show the notification; • the best device(s) • the best mode to notify the incoming notification
- 43. 43Prototype Prototype implementation Aim: evaluate machine learning approach to decide • who should receive an incoming notification; • the best moment to show the notification; • the best device(s) • the best mode to notify the incoming notification
- 44. 44Prototype Prototype implementation Preliminary version of Aim: evaluate machine learning approach to decide • who should receive an incoming notification; • the best moment to show the notification; • the best device(s) • the best mode to notify the incoming notification
- 45. 45Prototype Prototype implementation Preliminary version of Aim: evaluate machine learning approach to decide • who should receive an incoming notification; • the best moment to show the notification; • the best device(s) • the best mode to notify the incoming notification
- 48. 48Prototype Prototype implementation Preliminary version of Dataset
- 49. 49Prototype Prototype implementation Preliminary version of Dataset Algorithms
- 51. 51 Prototype implementation Prototype 94 people over 9 months monitored through smartphones in 2004: • Sender • Receiver • Type of notification • Timestamp of receipt • User current location Dataset
- 52. 52 Prototype implementation Prototype 94 people over 9 months monitored through smartphones in 2004: • Sender • Receiver • Type of notification • Timestamp of receipt • User current location Dataset Synthetic data: • User current activity • Available devices for the user • Target device.
- 53. 53 Prototype implementation Prototype 94 people over 9 months monitored through smartphones in 2004: • Sender • Receiver • Type of notification • Timestamp of receipt • User current location Dataset Synthetic data: • User current activity • Available devices for the user • Target device. Real + synthetic dataset: 165,289 samples, almost one per each hour of the day (the missing samples are related to hours in which users turned off their smartphones)
- 54. 54 Prototype implementation Prototype 94 people over 9 months monitored through smartphones in 2004: • Sender • Receiver • Type of notification • Timestamp of receipt • User current location Dataset Synthetic data: • User current activity • Available devices for the user • Target device. Real + synthetic dataset: 165,289 samples, almost one per each hour of the day (the missing samples are related to hours in which users turned off their smartphones) Information collected by Decision Maker in previous example { “notification“:{ “senderName“:“mySmartHome“, “type“:“smart Home Notification“, “receiptTimestamp“:“1447347600“ }, “userStatus“: { “senderId“:“359“, “currentActivity“:“STILL“, “currentActivityConfidence“:“50%“, “availableDevices”:[“deviceId”:”23”] }, “deviceStatus“:{ “deviceId“:”23”, “category“:”Smartphone”, “currentStatus“:”On”, “currentMode“:”Ring”, “wifiStatus“:” Connected through MOBILE”, “batteryLevel“:” 57%”, “batteryStatus“:”BATTERY_STATUS_NOT_CHARGING” } }
- 55. 55 Prototype implementation Prototype Machine learning algorithms Dataset Real + synthetic data (165,289 samples)
- 56. 56 Prototype implementation Prototype Machine learning algorithms Dataset Real + synthetic data (165,289 samples)
- 57. 57 Prototype implementation Prototype Machine learning algorithms Dataset Real + synthetic data (165,289 samples)
- 58. 58 Prototype implementation Prototype Machine learning algorithms Dataset Real + synthetic data (165,289 samples) Training dataset: 80% of data Tests dataset: 20% of data
- 59. 59 Prototype implementation Prototype Simplified version of the Decision maker: • only one device as receiver; • only one available mode for each device; • no decision about the best time to deliver the notification; • not aware of environment context Machine learning algorithms Dataset Real + synthetic data (165,289 samples) Training dataset: 80% of data Tests dataset: 20% of data
- 60. 60 Prototype implementation Prototype Machine learning algorithms Dataset Real + synthetic data (165,289 samples) Training dataset: 80% of data Tests dataset: 20% of data
- 61. 61 Prototype implementation Prototype Machine learning algorithms Dataset Real + synthetic data (165,289 samples) Training dataset: 80% of data Tests dataset: 20% of data
- 62. 62 Prototype implementation Prototype Three machine learning algorithms: 1. SupportVector Machine 2. Gaussian NaĂŻve Bayes 3. Decision Trees. Machine learning algorithms Dataset Real + synthetic data (165,289 samples) Training dataset: 80% of data Tests dataset: 20% of data
- 64. 64 Preliminary results Preliminary results 96,10% 83,40% 93,90% 0,00% 20,00% 40,00% 60,00% 80,00% 100,00% Support Vector Machine Gaussian Naive Bayes Decision Trees Percentage of corrected predictions obtained with used algorithms
- 65. 65 Preliminary results Preliminary results CPU time (in seconds) for a training phase with 33058 samples 96,10% 83,40% 93,90% 0,00% 20,00% 40,00% 60,00% 80,00% 100,00% Support Vector Machine Gaussian Naive Bayes Decision Trees 5801,1 12,9 13,9 1 10 100 1000 10000 Support Vector Machine Gaussian Naive Bayes Decision Trees Percentage of corrected predictions obtained with used algorithms
- 66. 66 Preliminary results Preliminary results Average CPU time (in milliseconds) for each notification classification SupportVector Machine 40,22 ms Gaussian Naive Bayes 0,31 ms Decision Trees 0,001 ms 96,10% 83,40% 93,90% 0,00% 20,00% 40,00% 60,00% 80,00% 100,00% Support Vector Machine Gaussian Naive Bayes Decision Trees Percentage of corrected predictions obtained with used algorithms CPU time (in seconds) for a training phase with 33058 samples 5801,1 12,9 13,9 1 10 100 1000 10000 Support Vector Machine Gaussian Naive Bayes Decision Trees
- 67. 67 Conclusion Obtained results demonstrated that our system uses a promising technique to manage the problem of overwhelming notifications. Specifically, the machine learning approach was tested through 3 different algorithms and SVM and DT seem to be the most promising one. Conclusion Future work: Define a new dataset to include all the needed real information Development of a system to collect real data and real notifications Careful evaluation of the machine learning algorithms Enhancement of prototype to include unconsidered blocks
- 68. 68 Thank you Future work Notification Collector (beta): Android app to collect real data https://goo.gl/pLMWSG To contribute: download it! Requirement: Android 5 (Lollipop) We collect (anonymously): • Incoming notification info (no content) • User current activity • User current location • Device status • User feedback