4.7 Article

Adaptive and context-aware service composition for IoT-based smart cities

Publisher

ELSEVIER
DOI: 10.1016/j.future.2016.12.038

Keywords

Internet of Things; Smart environments; Smart cities; Smart services; Real-time and semantic web services; System design; Service modeling

Funding

  1. Commission of the European Union within CREMA H2020-RIA [637066]
  2. Basque Government's Elkartek program within LANA II project [KK-2016/00052]
  3. H2020 Societal Challenges Programme [637066] Funding Source: H2020 Societal Challenges Programme

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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 loT-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 loT 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 preconditions, 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. (C) 2017 Elsevier B.V. All rights reserved.

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