Recommending Trigger-Action Rules for the IoT PDF

Recommending Trigger-Action Rules for the IoT

the ontology by which the application is supported (EUPont) and some tools and libraries of Android (e.g. Room Database Data bindings)

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Android SQLite database and content provider - Tutorial

SQLiteQueryBuilder is a convenience class that helps to build SQL queries. 2.4. rawQuery() Example. The following gives an example of a rawQuery() call. Cursor

[Master Thesis] Cross-platform mobile application generation with a

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The android application will be having its own local database (SQLite database) which will Fig 1: Example for creating SQLiteOpenHelper Class [4].

AND-801: Android Application Development Exam Sample

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Recommending Trigger-Action Rules for the IoT

POLITECNICO DI TORINO

Master degree in Computer Engineering

Master Degree Thesis

Recommending Trigger-Action

Rules for the IoTSupervisors:

Fulvio CORNO

Luigi DE RUSSIS

Alberto MONGE ROFFARELLO

Candidate:

Alessia MANTOVANI

December 2018

1 Introduction1

1.1 Problem definition . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1.2 Thesis goal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1.3 Thesis structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2 Background7

2.1 EUPont ontology . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2.2 Android . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3 Analysis11

3.1 Research stage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3.2 Android features . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3.3 Facebook service . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4 Design17

4.1 System architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4.2 Rule creation processes . . . . . . . . . . . . . . . . . . . . . . . . . .

4.3 Application structure . . . . . . . . . . . . . . . . . . . . . . . . . . .

5 Implementation25

5.1 Android application . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5.1.1 User interface . . . . . . . . . . . . . . . . . . . . . . . . . . .

5.1.2 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5.1.3 Data association . . . . . . . . . . . . . . . . . . . . . . . . .

5.2 Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5.3 Webhook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6 In-the-wild evaluation37

6.1 Study procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6.2 Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

38
iii

7 Results39

7.1 Data discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

8 Conclusions45

8.1 Future works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

A Appendix47

A.1 User information document . . . . . . . . . . . . . . . . . . . . . . . 47
A.2 Initial questionnaire . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
A.3 Final questionnaire . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

B Tables53

B.1 Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
B.1.1 Dictionary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
B.1.2 EventListened . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

Bibliography77

List of Tables

3.1 Android Actions not supported . . . . . . . . . . . . . . . . . . . . .

3.2 Android Actions supported . . . . . . . . . . . . . . . . . . . . . . . .

3.3 Android Triggers supported . . . . . . . . . . . . . . . . . . . . . . .

3.4 Facebook Triggers supported . . . . . . . . . . . . . . . . . . . . . . .

3.5 Facebook Actions supported . . . . . . . . . . . . . . . . . . . . . . .

4.1 Local Database Tables . . . . . . . . . . . . . . . . . . . . . . . . . .

7.1 End-user evaluation results - part 1 . . . . . . . . . . . . . . . . . . .

7.2 End-user evaluation results - part 2 . . . . . . . . . . . . . . . . . . .

44
B.1 Triggers first list - part 1 . . . . . . . . . . . . . . . . . . . . . . . . . 54
B.2 Triggers first list - part 2 . . . . . . . . . . . . . . . . . . . . . . . . . 55
B.3 Triggers first list - part 3 . . . . . . . . . . . . . . . . . . . . . . . . . 56
B.4 Triggers first list - part 4 . . . . . . . . . . . . . . . . . . . . . . . . . 57
B.5 Actions first list - part 1 . . . . . . . . . . . . . . . . . . . . . . . . . 58
B.6 Actions first list - part 2 . . . . . . . . . . . . . . . . . . . . . . . . . 59
B.7 Actions first list - part 3 . . . . . . . . . . . . . . . . . . . . . . . . . 60
v

List of Figures

2.1 EUPont structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4.1 General architecture . . . . . . . . . . . . . . . . . . . . . . . . . . .

4.2 Android features process . . . . . . . . . . . . . . . . . . . . . . . . .

4.3 Facebook service architecture . . . . . . . . . . . . . . . . . . . . . .

4.4 Application structure . . . . . . . . . . . . . . . . . . . . . . . . . . .

5.1 Application login . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5.2 Application places list . . . . . . . . . . . . . . . . . . . . . . . . . .

5.3 Application rule details . . . . . . . . . . . . . . . . . . . . . . . . . .

5.4 Application home . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5.5 EventDB Room table . . . . . . . . . . . . . . . . . . . . . . . . . . .

5.6 TriggerActionDB Room table . . . . . . . . . . . . . . . . . . . . . .

5.7 Registration/Login process . . . . . . . . . . . . . . . . . . . . . . . .

7.1 Distribution total rules . . . . . . . . . . . . . . . . . . . . . . . . . .

7.2 Distribution rules per user . . . . . . . . . . . . . . . . . . . . . . . .

7.3 Distribution features: data form the evaluation . . . . . . . . . . . . .

7.4 Distribution features: data from initial questionnaire . . . . . . . . .

7.5 Application utility (1 = not useful, 5 = very useful) . . . . . . . . . .

42
A.1 Application rule details . . . . . . . . . . . . . . . . . . . . . . . . . . 49
A.2 Application home . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
vi

Chapter 1 Introduction

The Internet of Things (IoT) is the Internet extended into to the physical world. Its function is to collect data from IoT objects and transform them into useful information. These objects can be standard devices, such as computers, smartphones and tablets, but also online services or any kind of non-internet-enabled physical device and everyday objects. Although IoT objects do most of the work without any human intervention, people can interact with them for example to set them up, to give them instructions or simply to access the data. For empowering users to program their IoT objects, a promising approach has been adopted (i.e. EUD). End-User Development (EUD) refers to activities and tools that allow end users (i.e., people who are not professional software developers) to program computers. One of the most used format in EUD is Trigger-Action programming paradigm. It is a paradigm where the user must define rules by means of "if-then" constructs, to create event-action rules. In the last years, an evolution of this area was the use of mobile devices to support end users development activities. Although EUD in the IoT has recently gained interest, interoperability and scalability challenges remain. With the spread of new smart "things", the amount of information may become too high and cluttered [1]. Therefore the most important concepts of the just mentioned paradigm are: •Trigger: an event that, when detected, causes the execution of an Action; •Action: an operation that is executed as a consequence of a Trigger; •Rule: the association of a Trigger with an Action (an example of a rule could be: IF I go into my home, THEN turn on smartphone Wi-fi); This thesis wants to use the EUD approach and the Trigger-Action program- ming paradigm, adding a system of automatic recommendations instead of having 1quotesdbs_dbs7.pdfusesText_5

[PDF] Recommending Trigger-Action Rules for the IoT