Semantic-based dynamic service adaptation in context-aware mobile cloud learning

Self-adaptable system concerns on service adaptation whenever errors persist within the system. Changes in contextual information such as networks or sensors will affect the system's effectiveness because the service adaptation process is not comprehensively handled in those contexts. Besides, the correctness to get the most equivalence services to be substituted is limitedly being addressed from previous works. A dynamic service adaptation framework is introduced to monitor and run a reasoning control to solve these issues. Hence, this paper presents a case study to proof the dynamic service adaptation framework that leverages on semantic-based approach in a context-aware environment. The evaluation of the case study resulted in a significant difference for the effectiveness at a 95% confidence level, which can be interpreted to confirm that the framework is promising to be used in operating dynamic adaptation process in a pervasive environment.

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