a context and user aware smart notification system

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

Outline

1. Context and Motivation

2. Goal

3. Architecture

4. Prototype

5. Preliminary results

6. Conclusion

7. Future work

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/)

4

Motivation

Motivation

5

Motivation

Motivation

IoT devices can generate, receive and show different kinds of notifications

6

Motivation

Motivation


7

Motivation

Motivation


8

Motivation

Motivation


The number of notifications is growing

9

Motivation

Motivation



10

Motivation

Motivation


Nowadays the same notification is replicated on all available devices


11

Motivation

Motivation




12

Motivation

Motivation




The benefit of displaying the same

notification on all available devices

could put user patience to a hard test

13

Analyze how machine learning approach can improve

IoT notification user experience

Goal

Goal

14



Goal

Goal

15



Goal

Goal

Develop a system able to filter incoming notifications

depending on:

• Notification information

• Environment status

• User context

• User habits

16



Goal

Goal


depending on:



• User context

• User habits

17



Goal

Goal


depending on:



• User context

• User habits

Evaluate machine learning approach

18

We propose:

Architecture

Architecture

19

We propose: A modular architecture

Architecture

Architecture

20


Architecture

Architecture

21


Architecture

Architecture

22

We propose: A modular architecture aware of

Architecture

Architecture

23

We propose:

Environment status

(e.g., weather information,

current date and time)

A modular architecture aware of

Architecture

Architecture

24

We propose:

Environment status



User context (e.g.,

location, status, current

activity),


Architecture

Architecture

25

We propose:

Environment status



User context (e.g.,

location, status, current

activity),

User habits


Architecture

Architecture

26


Architecture

Architecture

27

We propose:

Decision maker: Machine Learning

algorithm makes decisions (best devices

+ best modes + best moment).

Architecture

Architecture

28

Architecture: example

Architecture

29


Architecture

Mario is in a

meeting

30


Architecture

Mario is in a

meeting

31


Architecture

Every meeting lasts at least 2

hours

Mario is in a

meeting

32


Architecture


hours

Mario is in a

meeting

33


Architecture

Time: 17:00

Meeting started at

16:00

Wife is at home


hours

Mario is in a

meeting

34


Architecture

Time: 17:00

Meeting started at

16:00

Wife is at home


hours

Mario is in a

meeting

35


Architecture

Notification: the toilet

paper has just finished

Time: 17:00

Meeting started at

16:00

Wife is at home


hours

Mario is in a

meeting

36


Architecture



Time: 17:00

Meeting started at

16:00

Wife is at home


hours

Mario is in a

meeting

37


Architecture



Time: 17:00

Meeting started at

16:00

Wife is at home


hours

Mario is in a

meeting

38


Architecture



Notify:

• At 18:10

• on his personal

smartphone

• Sound

Time: 17:00

Meeting started at

16:00

Wife is at home


hours

Mario is in a

meeting

39


Architecture



Notify:

• At 18:10

• on his personal

smartphone

• Sound

Time: 17:00

Meeting started at

16:00

Wife is at home


hours

Mario is in a

meeting

40


Architecture



Notify:

• At 18:10

• on his personal

smartphone

• Sound

Time: 17:00

Meeting started at

16:00

Wife is at home


hours

Mario is in a

meeting

41 Prototype

Prototype implementation

42 Prototype

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 Prototype

Prototype implementation Aim: evaluate machine learning approach to decide





44 Prototype


Preliminary version of

Aim: evaluate machine learning approach to decide





45 Prototype



Aim: evaluate machine learning approach to decide





46 Prototype


47 Prototype



48 Prototype



Dataset

49 Prototype



Dataset Algorithms

50


Prototype

Dataset

51


Prototype

94 people over 9 months

monitored through

smartphones in 2004:

• Sender

• Receiver

• Type of notification

• Timestamp of receipt

• User current location

Dataset

52


Prototype


monitored through


• Sender

• Receiver




Dataset

Synthetic data:

• User current

activity

• Available devices

for the user

• Target device.

53


Prototype


monitored through


• Sender

• Receiver




Dataset

Synthetic data:

• User current

activity


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


Prototype


monitored through


• Sender

• Receiver




Dataset

Synthetic data:

• User current

activity


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


Prototype

Machine learning algorithms

Dataset

Real + synthetic data (165,289

samples)

56


Prototype


Dataset


samples)

57


Prototype


Dataset


samples)

58


Prototype


Dataset


samples)

Training dataset: 80% of data

Tests dataset: 20% of data

59


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


Dataset


samples)



60


Prototype


Dataset


samples)



61


Prototype


Dataset


samples)



62


Prototype

Three machine learning

algorithms:

1. Support Vector Machine

2. Gaussian Naïve Bayes

3. Decision Trees.


Dataset


samples)



63

Preliminary results

Preliminary results

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

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



66

Preliminary results

Preliminary results

Average CPU time (in milliseconds) for each

notification classification

Support Vector 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



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

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

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


• Device status

• User feedback

a context and user aware smart notification system

Technology