Formal verification of context and situation models in pervasive computing

Pervasive computing is a paradigm that focuses on the 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.

[1]  Hans W. Guesgen,et al.  Spatiotemporal Reasoning for Smart Homes , 2006, Designing Smart Homes.

[2]  Vasile-Marian Scuturici,et al.  An Ontology-Based Approach to Context Modeling and Reasoning in Pervasive Computing , 2007, Fifth Annual IEEE International Conference on Pervasive Computing and Communications Workshops (PerComW'07).

[3]  Giuseppe De Pietro,et al.  Formal specification of wireless and pervasive healthcare applications , 2010, TECS.

[4]  Kent Larson,et al.  Activity Recognition in the Home Using Simple and Ubiquitous Sensors , 2004, Pervasive.

[5]  Ling Bao,et al.  Activity Recognition from User-Annotated Acceleration Data , 2004, Pervasive.

[6]  Giuseppe De Pietro,et al.  Formal Specification and Verification of Ubiquitous and Pervasive Systems , 2011, TAAS.

[7]  Ben Kröse,et al.  Care: context awareness in residences for elderly , 2008 .

[8]  Arkady B. Zaslavsky,et al.  ECSTRA - Distributed Context Reasoning Framework for Pervasive Computing Systems , 2011, NEW2AN.

[9]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[10]  Juan Carlos Augusto,et al.  Management of Uncertainty and Spatio-Temporal Aspects for Monitoring and Diagnosis in a Smart Home , 2008 .

[11]  John W. Eaton,et al.  Gnu Octave Manual , 2002 .

[12]  Stathes Hadjiefthymiades,et al.  Situational computing: An innovative architecture with imprecise reasoning , 2007, J. Syst. Softw..

[13]  B. Kröse,et al.  Bayesian Activity Recognition in Residence for Elders , 2007 .

[14]  Arkady B. Zaslavsky,et al.  The ECORA framework: A hybrid architecture for context-oriented pervasive computing , 2008, Pervasive Mob. Comput..

[15]  Mohamed Medhat Gaber,et al.  Reasoning about Context in Uncertain Pervasive Computing Environments , 2008, EuroSSC.

[16]  Luca Cardelli,et al.  Mobile Ambients , 1998, FoSSaCS.

[17]  Oliver Brdiczka,et al.  Learning to detect user activity and availability from a variety of sensor data , 2004, Second IEEE Annual Conference on Pervasive Computing and Communications, 2004. Proceedings of the.

[18]  Arkady B. Zaslavsky,et al.  Multiple-Agent Perspectives in Reasoning About Situations for Context-Aware Pervasive Computing Systems , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[19]  Stathes Hadjiefthymiades,et al.  Prediction intelligence in context-aware applications , 2005, MDM '05.

[20]  John Soldatos,et al.  Ontology-based Management of Pervasive Systems , 2007, Emerging Artificial Intelligence Applications in Computer Engineering.

[21]  Tao Gu,et al.  Ontology based context modeling and reasoning using OWL , 2004, IEEE Annual Conference on Pervasive Computing and Communications Workshops, 2004. Proceedings of the Second.

[22]  Roy H. Campbell,et al.  Reasoning about Uncertain Contexts in Pervasive Computing Environments , 2004, IEEE Pervasive Comput..

[23]  Simon A. Dobson,et al.  Situation identification techniques in pervasive computing: A review , 2012, Pervasive Mob. Comput..

[24]  Fuyuki Ishikawa,et al.  Physical interaction in pervasive computing: formal modeling, analysis and verification , 2009, ICPS '09.

[25]  Andrey Boytsov,et al.  Formal Verification of the Context Model : Enhanced Context Spaces Theory Approach , 2011 .