Model of a Context-Aware Middleware for Mobile Workers

With the development of Internet of things and Web of things, computing becomes more pervasive, invisible and present everywhere. In fact, in our environment, we are surrounded by multiple devices that deliver (web) services which meet the needs of the users. However, the mobility of these devices as the users has important repercussions that challenge software design of these applications because the variability of the environment cannot be anticipated at the design time. Thus, it will be interesting to dynamically discover the environment and adapt the application during its execution to the new contextual conditions. We therefore, propose a model of a context-aware middleware that can address this issue through a monitoring service which is capable of reasoning and observation channels capable of calculating the context during the runtime. The monitoring service evaluates the pre-defined X-Query predicates in the context manager and uses Prolog to deduce the services needed to respond back. An independent observation channel for each different predicate is then dynamically generated by the monitoring service depending on the current state of the environment. Each channel sends its result directly to the context manager which consequently calculates the context based on all the predicates’ results while preserving the reactivity of the self-adaptive system.

[1]  Jens Rasmussen,et al.  Skills, rules, and knowledge; signals, signs, and symbols, and other distinctions in human performance models , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[2]  Mark Weiser The computer for the 21st century , 1991 .

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

[4]  M. Robiul Hoque,et al.  Development of middleware architecture to realize context-aware service in smart home environment , 2016, Comput. Sci. Inf. Syst..

[5]  Bhagwan Sahay Meena,et al.  A Heterogeneous Middleware Architecture for Wireless Sensor Network , 2013 .

[6]  Shonali Krishnaswamy,et al.  An Evaluation of Query Languages for Context-Aware Computing , 2006, 17th International Workshop on Database and Expert Systems Applications (DEXA'06).

[7]  Jeffrey Heer,et al.  liquid: Context-Aware Distributed Queries , 2003, UbiComp.

[8]  Xin Li,et al.  Context Aware Middleware Architectures: Survey and Challenges , 2015, Sensors.

[9]  Paraskevas Evripidou,et al.  CONTEXT-AWARE QUERIES USING QUERY BY BROWSING AND CHIROMANCER , 2004 .

[10]  Agnar Aamodt,et al.  Contextualised Ambient Intelligence Through Case-Based Reasoning , 2006, ECCBR.

[11]  Gaëtan Rey Contexte en Interaction Homme-Machine : le contexteur , 2005 .

[12]  Richard P. Martin,et al.  Poster: Smart buildings, sensor networks, and the Internet of Things , 2011, SenSys.

[13]  Jing Wang,et al.  Towards Future Situation-Awareness: A Conceptual Middleware Framework for Opportunistic Situation Identification , 2016, Q2SWinet@MSWiM.

[14]  Mohammed Elkoutbi,et al.  A policy-based middleware for context-aware pervasive computing , 2015, Int. J. Pervasive Comput. Commun..

[15]  M. Weiser The Computer for the Twenty-First Century , 1991 .

[16]  Roy H. Campbell,et al.  A Middleware for Context-Aware Agents in Ubiquitous Computing Environments , 2003, Middleware.

[17]  Arkady B. Zaslavsky,et al.  CA4IOT: Context Awareness for Internet of Things , 2012, 2012 IEEE International Conference on Green Computing and Communications.