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Sensors 2017, 17(8), 1727; doi:10.3390/s17081727

IoT Service Clustering for Dynamic Service Matchmaking

1
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
2
China Mobile Information Security Center, Beijing 100033, China
*
Author to whom correspondence should be addressed.
Received: 30 June 2017 / Revised: 20 July 2017 / Accepted: 27 July 2017 / Published: 27 July 2017
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Abstract

As the adoption of service-oriented paradigms in the IoT (Internet of Things) environment, real-world devices will open their capabilities through service interfaces, which enable other functional entities to interact with them. In an IoT application, it is indispensable to find suitable services for satisfying users’ requirements or replacing the unavailable services. However, from the perspective of performance, it is inappropriate to find desired services from the service repository online directly. Instead, clustering services offline according to their similarity and matchmaking or discovering service online in limited clusters is necessary. This paper proposes a multidimensional model-based approach to measure the similarity between IoT services. Then, density-peaks-based clustering is employed to gather similar services together according to the result of similarity measurement. Based on the service clustering, the algorithms of dynamic service matchmaking, discovery, and replacement will be performed efficiently. Evaluating experiments are conducted to validate the performance of proposed approaches, and the results are promising. View Full-Text
Keywords: Internet of things; semantic similarity measurement; multidimensional model; service clustering Internet of things; semantic similarity measurement; multidimensional model; service clustering
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhao, S.; Yu, L.; Cheng, B.; Chen, J. IoT Service Clustering for Dynamic Service Matchmaking. Sensors 2017, 17, 1727.

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