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  • 1.
    Boytsov, Andrey
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Context reasoning, context prediction and proactive adaptation in pervasive computing systems2011Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    The paradigm of pervasive computing aims to integrate the computing technologies in a graceful and transparent manner, and make computing solutions available anywhere and at any time. Different aspects of pervasive computing, like smart homes, smart offices, social networks, micromarketing applications, PDAs, etc. are becoming a part of everyday life. Context of pervasive computing system is any piece of information that can be of possible interest to the system. Context often includes location, time, activity, surroundings, etc. One of the core features of pervasive computing systems is context awareness – the ability to use context information to the benefit of the system. The thesis proposes a set of context prediction and situation prediction methods on top of enhanced situation awareness mechanisms. Being aware of the future context enables a pervasive computing system to choose the most efficient strategies to achieve its stated objectives and therefore a timely response to the upcoming situation can be provided. This thesis focuses on the challenges of context prediction, but in order to become really efficient and useful, context prediction approaches need to be gracefully integrated with different other aspects of reasoning about the context. This thesis proposes a novel integrated approach for proactively working with context information. In order to become efficient, context prediction should be complemented with proper acting on predicted context, i.e. proactive adaptation. The majority of current approaches to proactive adaptation solves context prediction and proactive adaptation problems in sequence. This thesis identifies the shortcomings of that approach, and proposes an alternative solution based on reinforcement learning techniques. The concept of situation provides useful generalization of context data and allows eliciting the most important information from the context. The thesis proposes, justifies and evaluates improved situation modeling methods that allow covering broader range of real-life situations of interest and efficiently reason about situation relationships. The context model defines the pervasive computing system’s understanding of its internal and external environments, and determines the input for context prediction solutions. This thesis proposes novel methods for formal verification of context and situation models that can help to build more reliable and dependable pervasive computing systems and avoid the inconsistent context awareness, situation awareness and context prediction results. The architecture of pervasive computing system integrates all the aspects of context reasoning and governs the interaction and collaboration between different context processing mechanisms. This thesis proposes, justifies and evaluates the architectural support for context prediction methods. The novel architectural solutions allow encapsulating various practical issues and challenges of pervasive computing systems and handling them on low levels of context processing, therefore, supporting the efforts for efficient context prediction and proactive adaptation.

  • 2.
    Boytsov, Andrey
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Situation awareness in pervasive computing systems: reasoning, verification, prediction2013Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The paradigm of pervasive computing aims to integrate the computing technologies in a graceful and transparent manner, and make computing solutions available anywhere and at any time. Different aspects of pervasive computing, like smart homes, smart offices, social networks, micromarketing applications, PDAs are becoming a part of everyday life.Context can be defined as information that can be of possible interest to the system. Context often includes location, time, activity, surroundings among other attributes. One of the core features of pervasive computing systems is context awareness – the ability to use context to improve the performance of the system and make its behavior more intelligent.Situation awareness is related to context awareness, and can be viewed as the highest level of context generalization. Situations allow eliciting the most important information from context. For example, situations can correspond to locations of interest, actions and locomotion of the user, environmental conditions.The thesis proposes, justifies and evaluates situation modeling methods that allow covering broad range of real-life situations of interest and reasoning efficiently about situation relationships. The thesis also addresses and contributes to learning the situations out of unlabeled data. One of the main challenges of that approach is understanding the meaning of a newly acquired situation and assigning a proper label to it. This thesis proposes methods to infer situations from unlabeled context history, as well as methods to assign proper labels to the inferred situations. This thesis proposes and evaluates novel methods for formal verification of context and situation models. Proposed formal verification significantly reduces misinterpretation and misdetection errors in situation aware systems. The proper use of verification can help building more reliable and dependable pervasive computing systems and avoid the inconsistent context awareness and situation awareness results. The thesis also proposes a set of context prediction and situation prediction methods on top of enhanced situation awareness mechanisms. Being aware of the future situations enables a pervasive computing system to choose the most efficient strategies to achieve its stated objectives and therefore a timely response to the upcoming situation can be provided. In order to become efficient, situation prediction should be complemented with proper acting on prediction results, i.e. proactive adaptation. This thesis proposes proactive adaptation solutions based on reinforcement learning techniques, in contrast to the majority of current approaches that solve situation prediction and proactive adaptation problems sequentially. This thesis contributes to situation awareness field and addresses multiple aspects of situation awareness.The proposed methods were implemented as parts of ECSTRA (Enhanced Context Spaces Theory-based Reasoning Architecture) framework. ECSTRA framework has proven to be efficient and feasible solution for real life pervasive computing systems

  • 3. Boytsov, Andrey
    et al.
    Zaslavsky, Arkady
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Context prediction in pervasive computing systems: achievements and challenges2010In: Supporting real time decision-making: the role of context in decision support on the move, New York: Encyclopedia of Global Archaeology/Springer Verlag, 2010, p. 35-64Chapter in book (Other academic)
  • 4.
    Boytsov, Andrey
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Zaslavsky, Arkady
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Correctness Analysis and Verification of Fuzzy Situations in Situation Aware Pervasive Computing Systems2013Report (Other academic)
    Abstract [en]

    Context awareness is one of the central features of pervasive computing systems. From pervasive computing perspective a situation can be defined as external semantic interpretation of context. Situation awareness aims to infer situations out of context. Developing situation awareness is a challenging task, which can be significantly hampered by errors during design stage. In this article we propose a novel method for verification of fuzzy situation definitions. Fuzzy logic is a powerful mechanism for reasoning in pervasive computing systems and verification of situation models is a new method of formally ensuring correctness of context awareness and situation awareness. Verification is applied at the design time to check that definitions of situations are error-free. Verification approach allows developers to rigorously specify expected relationships between situations and then formally check that definitions of situations comply with expected relationships. If an error is found, then additional task is to find counterexamples - particular context attribute values, which can cause situation awareness inconsistency. Counterexamples provide additional insight into the cause of error and help repairing situation definitions. We also discuss a method to formalize requirements, as well as propose and formally prove the novel verification algorithm for fuzzy situation models. Last, but not least, we analyze theoretical and practical complexity of the proposed solution.

  • 5.
    Boytsov, Andrey
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Zaslavsky, Arkady
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    ECSTRA: Distributed context reasoning framework for pervasive computing systems2011In: Smart Spaces and Next Generation Wired/Wireless Networking: 11th International Conference, NEW2AN 2011, and 4th Conference on Smart Spaces, ruSMART 2011, St. Petersburg, Russia, August 22-25, 2011. Proceedings / [ed] Sergey Balandin; Yevgeni Koucheryavy; Honglin Hu, Springer Science+Business Media B.V., 2011, p. 1-13Conference paper (Refereed)
    Abstract [en]

    Pervasive computing solutions are now being integrated into everyday life. Pervasive computing systems are deployed in homes, offices, hospitals, universities. In this work we present ECSTRA – Enhanced Context Spaces Theory-based Reasoning Architecture. ECSTRA is a context awareness and situation awareness framework that aims to provide a comprehensive solution to reason about the context from the level of sensor data to the high level situation awareness. Also ECSTRA aims to fully take into account the massively multiagent distributed nature of pervasive computing systems. In this work we discuss the architectural features of ECSTRA, situation awareness approach and collaborative context reasoning. We also address the questions of multi-agent coordination and efficient sharing of reasoning information. ECSTRA enhancements related to those problems are discussed. Evaluation of proposed features is also discussed.

  • 6.
    Boytsov, Andrey
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Zaslavsky, Arkady
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Extending context spaces theory by proactive adaptation2010In: Smart spaces and next generation Wired/Wireless networking: third Conference on Smart Spaces, ruSMART 2010, and 10th international conference, NEW2AN 2010, St. Petersburg, Russia, August 23-25, 2010 ; proceedings / [ed] Sergey Balandin; Roman Dunaytsev; Yevgeni Koucheryavy, Encyclopedia of Global Archaeology/Springer Verlag, 2010, p. 1-12Conference paper (Refereed)
    Abstract [en]

    Context awareness is one of the core features of pervasive computing systems. Pervasive systems can also be improved by smart application of context prediction. This paper addresses subsequent challenge of how to act according to predicted context in order to strengthen the system. Novel reinforcement learning based architecture is proposed to overcome the drawbacks of existing approaches to proactive adaptation. Context spaces theory is used as an example of how existing context awareness systems can be enhanced to achieve proactive adaptation. This recently developed theory addresses problems related to sensors uncertainty and high-level situation reasoning and it can be enhanced to achieve efficient proactive adaptation as well. This article also discusses implementation options and possible testbed to evaluate the solutions

  • 7.
    Boytsov, Andrey
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Zaslavsky, Arkady
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Formal verification of context and situation models in pervasive computing2013In: Pervasive and Mobile Computing, ISSN 1574-1192, E-ISSN 1873-1589, Vol. 9, no 1, p. 98-117Article in journal (Refereed)
    Abstract [en]

    Pervasive computing is a paradigm that focuses on availability of computer resources anytime anywhere for any application and supports non-intrusive integration of computing services into everyday life. Context awareness is the core feature of pervasive computing. High-level context awareness can be enhanced by situation awareness that represents the ability to detect and reason about the real-life situations. In this article we propose, analyze and validate the formal verification method for situation definitions and demonstrate its feasibility and efficiency. Situations are often defined manually by domain experts and are, therefore, susceptible to definition inconsistencies and possible errors, which in turn can cause situation reasoning problems. The proposed method takes as an input properties of situations and dependencies among them as well as situation definitions in terms of low-level context features, and then either formally proves that the definitions do comply with the expected properties, or provides a complete set of counterexamples — context parameters that prove situation inconsistency. Evaluation and complexity analysis of the proposed approach are also presented and discussed. Examples and evaluation results demonstrate that the proposed approach can be used to verify real-life situation definitions, and detect non-obvious errors in situation specifications.

  • 8.
    Boytsov, Andrey
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Zaslavsky, Arkady
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Formal Verification of the Context Model: Enhanced Context Spaces Theory Approach2011Report (Other academic)
    Abstract [en]

    Pervasive computing is a paradigm that focuses on availability and non-intrusive integration of computing services into everyday life. Context awareness is the basic principle of pervasive computing. The important part of high-level context awareness is situation awareness – the ability to detect and reason about the real-life situations. The specifications of situations are often carried out manually by the experts. Therefore, the specification errors can be introduced. The specification errors cause the situation reasoning problems and context model inconsistency. In this article we propose and analyze the approach for formal verification of the situation definitions. Our solution uses as an input the situation specification in terms of low-level context features and the properties under verification, and then either formally proves that the specifications do comply with the expected property, or provide all possible counterexamples – the context conditions that will lead to situation awareness inconsistency. Evaluation and the complexity analysis of the proposed approach are also discussed.

  • 9.
    Boytsov, Andrey
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Zaslavsky, Arkady
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    From sensory data to situation awareness: enhanced context spaces theory approach2011In: Ninth International Conference on Dependable, Autonomic and Secure Computing (DASC), Piscataway, NJ: IEEE Communications Society, 2011, p. 207-214Conference paper (Refereed)
    Abstract [en]

    High-level context awareness can be significantly improved by the recognition of real-life situations. The theory of context spaces is a context awareness approach that uses spatial metaphors to provide integrated mechanisms for both low-level and high-level context awareness and situation awareness. Taking context spaces theory situation awareness as a baseline, we propose and analyze the enhanced situation awareness techniques, which allow us to reason about broad class of real-life situations. We also improve reasoning about the relationships between situations, and discuss how it relates to newly proposed situation awareness approaches. Practical evaluation of the results is also discussed.

  • 10.
    Boytsov, Andrey
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Zaslavsky, Arkady
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Abdallah, Zahraa
    Caulfield School of Information Technology, Monash University.
    Where have you been?: Using location clustering and context awareness to understand places of interest2012In: Internet of things, smart spaces, and next generation networking: 12th international conference, NEW2AN 2012, and 5th Conference on Smart Spaces, ruSMART 2012, St. Petersburg, Russia, August 27-29, 2012 : proceedings / [ed] Sergey Andreev; Yevgeni Koucheryavy ; Sergey Balandin, Heidelberg: Encyclopedia of Global Archaeology/Springer Verlag, 2012, p. 51-62Conference paper (Refereed)
    Abstract [en]

    Mobile devices have access to multiple sources of location data, but at any particular time often only a fraction of the location information sources is available. Fusion of location information can provide reliable real-time location awareness on the mobile phone. In this paper we propose and evaluate a novel approach to detecting the places of interest based on density-based clustering. We address both extracting the information about relevant places from the combined location information, and detecting the visits to known places in the real time. In this paper we also propose and evaluate ContReMAR application - an application for mobile context and location awareness. We use Nokia MDC dataset to evaluate our findings, find the proper configuration of clustering algorithm and refine various aspects of place detection

  • 11.
    Boytsov, Andrey
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Zaslavsky, Arkady
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Synnes, Kåre
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Extending context spaces theory by predicting run-time context2009In: Smart Spaces and Next Generation Wired/Wireless Networking: 9th International Conference, NEW2AN 2009 and Second Conference on Smart Spaces, ruSMART 2009, St. Petersburg, Russia, September 15-18, 2009. Proceedings / [ed] Sergey Balandin; Dmitri Moltchanev; Yevgeni Koucheryavy, Berlin: Encyclopedia of Global Archaeology/Springer Verlag, 2009, p. 8-21Conference paper (Refereed)
    Abstract [en]

    Context awareness and prediction are important for pervasive computing systems. The recently developed theory of context spaces addresses problems related to sensor data uncertainty and high-level situation reasoning. This paper proposes and discusses componentized context prediction algorithms and thus extends the context spaces theory. This paper focuses on two questions: how to plug-in appropriate context prediction techniques, including Markov chains, Bayesian reasoning and sequence predictors, to the context spaces theory and how to estimate the efficiency of those techniques. The paper also proposes and presents a testbed for testing a variety of context prediction methods. The results and ongoing implementation are also discussed.

  • 12.
    Cleland, Ian
    et al.
    University of Ulster. School of Computing and Mathematics.
    Kikhia, Basel
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Nugent, Chris
    University of Ulster. School of Computing and Mathematics.
    Boytsov, Andrey
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hallberg, Josef
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Synnes, Kåre
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    McClean, Sally
    Computing and Information Engineering, University of Ulster.
    Finlay, Dewar
    University of Ulster. School of Computing and Mathematics.
    Optimal Placement of Accelerometers for the Detection of Everyday Activities2013In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 13, no 7, p. 9183-9200Article in journal (Refereed)
    Abstract [en]

    This article describes an investigation to determine the optimal placement of accelerometers for the purpose of detecting a range of everyday activities. The paper investigates the effect of combining data from accelerometers placed at various bodily locations on the accuracy of activity detection. Eight healthy males participated within the study. Data were collected from six wireless tri-axial accelerometers placed at the chest, wrist, lower back, hip, thigh and foot. Activities included walking, running on a motorized treadmill, sitting, lying, standing and walking up and down stairs. The Support Vector Machine provided the most accurate detection of activities of all the machine learning algorithms investigated. Although data from all locations provided similar levels of accuracy, the hip was the best single location to record data for activity detection using a Support Vector Machine, providing small but significantly better accuracy than the other investigated locations. Increasing the number of sensing locations from one to two or more statistically increased the accuracy of classification. There was no significant difference in accuracy when using two or more sensors. It was noted, however, that the difference in activity detection using single or multiple accelerometers may be more pronounced when trying to detect finer grain activities. Future work shall therefore investigate the effects of accelerometer placement on a larger range of these activities

  • 13.
    Kikhia, Basel
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Boytsov, Andrey
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hallberg, Josef
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Sani, Zaheer ul Hussain
    Luleå University of Technology, Department of Health Sciences.
    Jonsson, Håkan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Synnes, Kåre
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Structuring and Presenting Lifelogs based on Location Data2014In: Pervasive Computing Paradigms for Mental Health: 4th International Symposium, MindCare 2014, Tokyo, Japan, May 8-9, 2014, Revised Selected Papers / [ed] Pietro Cipresso; Alaksandar Matic; Guillaume Lopez, Cham: Encyclopedia of Global Archaeology/Springer Verlag, 2014, p. 133-144Conference paper (Refereed)
    Abstract [en]

    Lifelogging techniques help individuals to log their life and retrieve important events, memories and experiences. Structuring lifelogs is a major challenge in lifelogging systems since the system should present the logs in a concise and meaningful way to the user. In this paper the authors present an approach for structuring lifelogs as places and activities based on location data. The structured lifelogs are achieved using a combination of density-based clustering algorithms and convex hull construction to identify the places of interest. The periods of time where the user lingers at the same place are then identified as possible activities. In addition to structuring lifelogs the authors present an application in which images are associated to the structuring results and presented to the user for reviewing. The system is evaluated through a user study consisting of 12 users, who used the system for 1 day and then answered a survey. The proposed approach in this paper allows automatic inference of information about significant places and activities, which generates structured image-annotated logs of everyday life.

  • 14.
    Kikhia, Basel
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Boytsov, Andrey
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hallberg, Josef
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Sani, Zaheer ul Hussain
    Jonsson, Håkan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Synnes, Kåre
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Structuring and presenting lifelogs based on location data2012Report (Other academic)
    Abstract [en]

    Lifelogging techniques help individuals to log their life and retrieve important events, memories and experiences. Structuring lifelogs is a major challenge in lifelogging systems since the system should present the logs in a concise and meaningful way to the user. In this article the authors present a novel approach for structuring lifelogs as places and activities based on location data. The structured lifelogs are achieved using a combination of density-based clustering algorithms and convex hull construction to identify the places of interest. The periods of time where the user lingers at the same place are then identified as possible activities. In addition to structuring lifelogs the authors present an application in which images are associated to the structuring results and presented to the user for reviewing. The proposed approach allows automatic inference of information about significant places and activities, which generates structured image-annotated logs of everyday life.

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