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研究生:蔡博倫
研究生(外文):Bo-Lung Tsai
論文名稱:於示範中萃取事件-狀態-行動規則以編程物聯網之研究
論文名稱(外文):Programming Internet of Things by Mining of Event-Condition-Action Rules from Demonstration
指導教授:許永真許永真引用關係
口試委員:林桂傑施吉昇紀婉容
口試日期:2015-07-28
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:資訊工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:57
中文關鍵詞:物聯網示範編程事件-狀態-行動規則探勘模式狀態機
外文關鍵詞:Internet of ThingsProgramming by DemonstrationEvent- Condition-Action rulePattern DiscoveryFinite State Machine
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物聯網將生活中的物品連上網路,帶來了資訊分享與操控的便利性。然而,現有的編程方法仍然阻礙終端使用者利用這些好處。如何能讓使用者不用程式語言或是訓練學習模型,我提出示範物品之間的實際運作,以及背後探勘隱藏的模式來編程物聯網。本論文描述一個系統從感測器和控制器收集之資料萃取出事件-狀態-行動規則為主的程式,以及與狀態機計算模型的關聯。最後本論文透過比較實際案例來證實系統的實用性與適用範圍。

The Internet of Things (IoT) connects massive daily things to conventional Internet networks, bringing the benefit to get information from or control those things with ease. However, current programming solutions still hinder the end-users to take advantage of the emerging technology. Instead of asking them to write programs or to train models, I proposed to program IoT by demonstrating how the things should work explicitly and combine the frequent patterns discovered implicitly. This paper presents a system to generate Event-Condition-Action rules from the data collected from the sensors and the actuators and relate the mining to the computation model, Finite State Machine. The results and comparison study are presented to show the feasibility and the limitation of the system.

口試委員審定書 i
Acknowledgments ii
中文摘要 iii
Abstract iv
Contents v
List of Figures vii
List of Tables x
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . .1
1.2 Internet of things . . . . . . . . .2
1.2.1 Machine-to-machine . . .3
1.2.2 Wireless Sensor Network .4
1.2.3 Ubiquitous Computing . .5
1.2.4 Event Processing . . . . .5
1.3 Thesis Organization . . . . . . . . .6
2 Related Work 7
2.1 Rule-based programming . . . .7
2.1.1 Active Database . . . .7
2.1.2 iCap . . . . . . . . . . .9
2.1.3 SmartThings . . . . . .9
2.1.4 IFTTT . . . . . . . . . .12
2.2 Learning-based programming . .12
2.2.1 Blackbos systems . . . .14
2.2.2 Glassbox systems . . . .14
2.2.3 Frequent patterns mining....15
2.2.4 Nest . . . . . . . . . . .16
2.3 Programming by Demonstration 18
2.3.1 Direct Manipulation . .18
2.3.2 Gamut . . . . . . . . . .19
2.3.3 CAPpella . . . . . . . .20
2.3.4 Gesture Coder . . . . .20
2.3.5 Exemplar . . . . . . . .22
2.3.6 GALLAG Strip . . . . .23
3 Formulation 25
3.1 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . .25
3.2 Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .26
3.3 Finite State Machine and Event-Condition-Action Modular Finite
Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .26
3.4 Programming by Demonstration Problem . . . . . . . . . . . . .30
3.5 Prosposed Solution . . . . . . . . . . . . . . . . . . . . . . .30
4 Demonstration Recording and Rule Extraction 33
4.1 Explicit demonstration . . . . . . . . . . .33
4.1.1 Event extraction . . . . . . . . . .33
4.1.2 State machine derivation . . . . . .35
4.1.3 ECA-rule transformation . . . . . .36
4.2 Implicit discovery . . . . . . . . . . 37
4.2.1 Pattern learning . . . . . . . . . . .37
5 Evaluation 38
5.1 Feasibility study of explicit demonstration via SmartThings platform and
Compare to practical SmartApps . . . . . . . . . . . . . . . . . . . . . .38
5.2 Applying to the R324 dataset . . . . . . . . . . . . . . . . . . . . . . . . 39
6 Conclusion 43
A SmartThings applications 44
B Room 324 Environment 47
Bibliography 50

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