Dissertation Towards Ambient Intelligent Applications Using

171 Pages·2016·4.52 MB·English

PhD-FSTC-2016-02
The Faculty of Science, Technology and Communication
Dissertation
Defense held on the 11th January 2016 in Luxembourg
to obtain the degree of
Docteur de l’Université du Luxembourg
en Informatique
by
Assaad Moawad
Born on 11th March 1988 in Bayet El Chaar (Lebanon)
Towards Ambient Intelligent Applications
Using [email protected] And Machine
Learning For Context-Awareness
Dissertation Defense Committee
Prof. Nicolas Navet, chairman
Professor, University of Luxembourg, Luxembourg
Dr. Francois Fouquet, co-chairman
Research Associate, University of Luxembourg, Luxembourg
Prof. Yves Le Traon, supervisor
Professor, University of Luxembourg, Luxembourg
Prof. Romain Rouvoy, member
Professor, University Lille 1, France
Prof. Houari Sahraoui, member
Professor, University of Montreal, Montreal, Canada
Dr. Patrice Caire, expert
Research Associate, University of Luxembourg, Luxembourg

Abstract
A
mbient Intelligence (AmI) constitutes a new paradigm of interaction among
humans, smart objects and devices. AmI systems are expected to support humans
in their every day tasks and activities. In order to achieve this goal, these systems
requireaugmentingtheenvironmentwithsensing,computing,communicating,andreasoning
capabilities. Due to advances in technology, sensors are getting more powerful, cheaper
and smaller, which stimulated large scale development and production. These sensors will
generate a big amount of data and can easily lead to millions of values in a short amount
of time, which can quickly reach the computation and storage limits when it comes to
structuring and processing the data. For this problem, we propose a concept of continuous
models that can handle highly-volatile data, and represent the continuous nature of sensor
data in an efficient and compact way. We show on various AmI datasets that this can
significantly improve storage and efficiency.
One important goal of AmI systems is to transform living and working environments into
intelligent spaces able to adapt to their users’ needs and desires in real-time. In this
sense, we call AmI applications context-aware, meaning that they use environmental
information to adaptively provide more relevant and better services to the user. However,
AmI systems are composed from heterogeneous components, operating in an open and
dynamicenvironment. Eachofthesecomponentscanhavedifferentstorageandcomputation
capabilities. They might not have all the information needed to derive context information,
and they might not be reachable all the time for various reasons. In this thesis, we present
a contextual reasoning solution adapted for component based platforms. Our solution can
derive contextual information in a distributed way and can handle inconsistencies when
contradictory information is received from several components.
Other than the storage and computation efficiency, several qualities need to be satisfied
according to the different contexts. Privacy is one of these qualities. AmI services will rely
more and more on personal data that is vastly collected, stored, and exchanged with other
third parties in order to provide added-value services. Such data are sensitive and often
related to personal activities and therefore can lead to privacy risks, especially when data is
shared with high precision and frequency. However, this privacy quality can be relaxed in
somecontexts, forexampleinanemergencysituationinordertoincreaseutilityorefficiency.
This leads to the need of developing an adaptive solution that is able to react to context
changes in real-time and involve optimizing conflicting objectives. For this challenge, we
propose to use blurring components as our main privacy preservation elements. The
idea behind this approach is that, by gradually decreasing the data quality, a blurring
component is able to hide some of the personal data delivered by sensors while still keeping
the necessary information for the services to work. In order to find a good trade-off between
these different conflicting objectives, we adapt a multi-objective evolutionary algorithm to
run directly on top of domain specific models. We then apply it as our main optimization
i
engine on [email protected] to keep adapting the different qualities, when the context
change.
Finally, AmI services are expected to be tailored for different users’ needs in a seamless
and unobtrusive way. Meaning that they should be able to detect contexts and learn
habits automatically with the least possible intervention of users. In order to achieve this,
machine learning (ML) techniques need to be merged at the core of reasoning models.
These techniques offer powerful tools to automatically detect patterns, categorize contexts,
build usage profiles, represent data with compact mathematical hypothesis and provide
statistical information vital for the intelligent aspect of AmI. This dissertation ends up by
opening new directions on how to model and adapt machine learning techniques to fit for
AmI platforms.
Overall, this thesis provides solutions for the next leap of technology, where sensors become
ubiquitous in order to empower smart systems. Our solutions, implemented in an open
source framework KMF, allow to create efficient and distributed, data and component
models for IoT, adaptable at runtime leveraging multi-objective optimization to find a
good tradeoff between qualities for the current context, and machine learning techniques to
derive contextual rules, profile and learn habits automatically.
Keywords: Distributed, Context-aware, MOEA, Machine Learning, Ambient Intelligence,
Internet of Things, [email protected], Privacy, Blurring.
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Acknowledgements
T
he PhD experience goes beyond research, experimentations and papers writing.
It is indeed a challenging life experience. It is the result of a long process that
began mid-January 2012 and which outcome owes much to the support and help of
several people. It is a pleasure for me to express my gratitude to them.
First of all, the accomplishments of this challenging experience would have never been
possible without the support of my supervisor Prof. Yves Le Traon. I would like to express
my deepest gratitude for never stopping in believing in me and encouraging me, especially
in the hardest moments of my thesis. He helped me finding new solutions when I only saw
closed roads.
Of all the people involved in my thesis, I am particularly grateful to my daily supervisor,
Dr. François Fouquet, for his patience, advice and flawless guidance. He showed me how to
performresearch,andtaughtmehowtoconductrigorousexperiments. Mostimportantly,he
helped me to improve my programming skills, and thought me how to work and collaborate
in an efficient way with the open source community. I am very happy as well about the
friendship we have built up during these years.
My special thanks goes also to the members of my dissertation committee: Prof. Romain
Rouvoy and Prof. Houari Sahraoui for investing time to review my work and for providing
interesting and valuable feedback. I am equally thankful to my co-supervisor Dr. Jacques
Klein. I would like to express my warm thanks as well to all the group members of the
SerVal team for the plenty discussions we had. More specifically, the people who helped me
during my PhD: Dr. Gregory Nain for the advice he gave me while working with Kevoree
and KMF, Thomas Hartmann and Dr. Tejeddine Mouelhi for their continuous feedbacks
and reviews. I would also like to extend my thanks to my external co-authors, especially
Dr. Nicola Zannone, Vasilis Efthymiou and Dr. Antonis Bikakis.
My PhD experience was a great enriching cultural experience. I enjoyed my stay in the
Grand Duchy of Luxembourg, a great place to live, to work and to meet international
people. I am thankful to Luxembourgish institutions and particularly to the University of
Luxembourg and the city of Luxembourg for all the facilities that made my stay pleasant
and joyful. Finally, and more personally, I would like to express my deepest thanks to my
family and my friends for their support, especially my photography friends in Luxembourg
(Johann Heckel, Bogdan and Mioara) who helped me overcoming the work stress, and my
good neighbors in Noertzange (Dimitrios Kampas and Amir Houshang Mahmoudi) for their
continuous support.
Assaad Moawad
Luxembourg, Luxembourg, January 2016
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Contents
List of abbreviations ix
List of figures xi
List of tables xiii
List of algorithms xv
1 Introduction 1
1.1 Application Domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1.1 Internet of Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1.2 Ambient Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1.3 Ambient Assisted Living . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2.1 Big data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2.2 Continuous aspect of physical measurements . . . . . . . . . . . . . 4
1.2.3 Inaccuracies and loss of values . . . . . . . . . . . . . . . . . . . . . 4
1.2.4 Distributed, dynamic and heterogeneous . . . . . . . . . . . . . . . . 5
1.2.5 Context awareness . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2.6 Adaptability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2.7 Unobtrusiveness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
I Background and state of the art 11
2 Contextual reasoning 13
2.1 Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2 Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3 Contextual reasoning challenges in AmI . . . . . . . . . . . . . . . . . . . . 15
2.4 Multi-Context System MCS . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.5 Contextual Defeasible Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.6 Contextual Representation Model . . . . . . . . . . . . . . . . . . . . . . . . 18
3 Modeling frameworks 21
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.2 Model-driven engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.3 [email protected] for context representation . . . . . . . . . . . . . . . . . . 23
3.3.1 Requirements of [email protected] . . . . . . . . . . . . . . . . . . . 24
3.3.2 Native Independent Versioning . . . . . . . . . . . . . . . . . . . . . 24
3.3.3 Time management . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.3.4 Eclipse Modeling Framework (EMF) . . . . . . . . . . . . . . . . . . 26
3.3.5 Kevoree Modeling Framework (KMF) . . . . . . . . . . . . . . . . . 26
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3.4 Kevoree - A component based software platform . . . . . . . . . . . . . . . 27
3.5 Kevoree Critical Features for AmI . . . . . . . . . . . . . . . . . . . . . . . 28
4 Qualities in AmI 31
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4.2 Privacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4.2.1 Definitions of privacy . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4.2.2 Privacy issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.2.3 Privacy preservation techniques . . . . . . . . . . . . . . . . . . . . . 38
4.2.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.3 Utility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.4 Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
5 Reasoning tools and techniques 43
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5.2 Optimization problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5.2.1 Solution encoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5.3 Evolutionary algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
5.4 Multi-objective optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
5.4.1 Pareto front . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
5.4.2 Selecting a solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
5.4.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
5.5 Machine learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
5.5.1 Categorizations of ML . . . . . . . . . . . . . . . . . . . . . . . . . . 49
5.5.2 Machine learning for the AmI domain . . . . . . . . . . . . . . . . . 52
II Contributions 55
6 A Continuous and Efficient Model for IoT 57
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
6.2 Time Series and Signal Segmentation Algorithms . . . . . . . . . . . . . . . 59
6.3 A Continuous and Efficient Model for IoT . . . . . . . . . . . . . . . . . . . 61
6.3.1 Continuous Models Structure . . . . . . . . . . . . . . . . . . . . . . 62
6.3.2 Live Model Segmentation Driven by Tolerated Error . . . . . . . . . 63
6.3.3 Online Segment Construction Based on Live Machine Learning . . . 65
6.4 Experimental Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
6.5 Discussion: Data-Driven Models or Model-Driven Data? . . . . . . . . . . . 74
6.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
7 R-Core: a rule-based contextual reasoning platform for AmI 77
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
7.2 An Ambient Assisted Living Example . . . . . . . . . . . . . . . . . . . . . 79
7.2.1 Distributed Query evaluation . . . . . . . . . . . . . . . . . . . . . . 80
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7.3 R-CoRe Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
7.3.1 Java Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
7.3.2 Query Component . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
7.3.3 Query Servant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
7.3.4 Query Interceptor . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
7.3.5 Query class and loop detection mechanism. . . . . . . . . . . . . . . 84
7.4 Demonstrating R-Core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
7.4.1 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
7.4.2 Execution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
7.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
8 Polymer - A model-driven approach for MOEA 91
8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
8.2 Real-World Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
8.3 Model-based MOO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
8.3.1 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
8.3.2 Polymer Implementation . . . . . . . . . . . . . . . . . . . . . . . . . 97
8.3.3 Partial Model Cloning . . . . . . . . . . . . . . . . . . . . . . . . . . 99
8.4 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
8.4.1 Complexity to Implement . . . . . . . . . . . . . . . . . . . . . . . . 99
8.4.2 Evolutive Refactoring Robustness. . . . . . . . . . . . . . . . . . . . 100
8.4.3 Performance and Effectiveness . . . . . . . . . . . . . . . . . . . . . 101
8.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
9 Adaptive blurring to balance privacy and utility 105
9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
9.2 Proportional Data Access . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
9.3 Adaptive Blurring Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
9.3.1 Blurring Components . . . . . . . . . . . . . . . . . . . . . . . . . . 110
9.3.2 Risk and Counter-Measure Model . . . . . . . . . . . . . . . . . . . 112
9.3.3 Reasoning Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
9.4 Results and Evaluation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
9.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
9.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
10 Model-based machine learning 121
10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
10.2 Model-based machine learning . . . . . . . . . . . . . . . . . . . . . . . . . . 123
10.2.1 Main principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
10.3 Use case: profiling in the smart grid . . . . . . . . . . . . . . . . . . . . . . 124
10.4 Suspicious consumption value detection . . . . . . . . . . . . . . . . . . . . 125
10.4.1 Overview: Towards Contextual Learning and Detection . . . . . . . 125
10.4.2 Gaussian Mixture Model . . . . . . . . . . . . . . . . . . . . . . . . . 126
10.4.3 Profiling Power Consumption . . . . . . . . . . . . . . . . . . . . . . 126
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Contents
10.5 Modeling language for machine learning . . . . . . . . . . . . . . . . . . . . 128
10.5.1 Live meta-learning with [email protected]. . . . . . . . . . . 130
10.6 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
10.6.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
10.6.2 Efficiency: Can We Meet Near Real-Time Expectations? . . . . . . . 131
10.6.3 Effectiveness: Can We Better Detect Suspicious Values? . . . . . . . 132
10.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
III Conclusion 135
11 Conclusions and Future Work 137
11.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
11.2 Next steps and future research axis . . . . . . . . . . . . . . . . . . . . . . . 139
List of papers, tools & services 141
Bibliography 143
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