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Adaptive and context-aware service composition for IoT-based smart cities
Affiliation:1. College of Management and Economics, Tianjin University, Tianjin, China;2. Department of Information Technology and Decision Sciences, Old Dominion University, Norfolk, VA 23529, USA;3. College of Pearl River, Tianjin University of Finance and Economics, Tianjin, China;4. State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, China
Abstract:Smart Cities are advancing towards an instrumented, integrated, and intelligent living space, where Internet of Things (IoT), mobile technologies and next generation networks are expected to play a key role. In smart cities, numerous IoT-based services are likely to be available and a key challenge is to allow mobile users perform their daily tasks dynamically, by integrating the services available in their vicinity. Semantic Service Oriented Architectures (SSOA) abstract the environment’s services and their functionalities as Semantic Web Services (SWS). However, existing service composition approaches based on SSOA do not support dynamic reasoning on user tasks and service behaviours to deal with the heterogeneity of IoT domains. In this paper, we present an adaptive service composition framework that supports such dynamic reasoning. The framework is based on wEASEL, an abstract service model representing services and user tasks in terms of their signature, specification (i.e., context-aware pre-conditions, post-conditions and effects) and conversation (i.e., behaviour with related data-flow and context-flow constraints). To evaluate our composition framework, we develop a novel OWLS-TC4-based testbed by combining simple and composite services. The evaluation shows that our wEASEL-based system performs more accurate composition and allows end-users to discover and investigate more composition opportunities than other approaches.
Keywords:Internet of Things  Smart environments  Smart cities  Smart services  Real-time and semantic web services  System design  Service modeling
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