Next Article in Journal
Curvature and Temperature Measurement Based on a Few-Mode PCF Formed M-Z-I and an Embedded FBG
Next Article in Special Issue
Context- and Template-Based Compression for Efficient Management of Data Models in Resource-Constrained Systems
Previous Article in Journal
Establishment of a Site-Specific Tropospheric Model Based on Ground Meteorological Parameters over the China Region
Previous Article in Special Issue
A Novel Dual Separate Paths (DSP) Algorithm Providing Fault-Tolerant Communication for Wireless Sensor Networks
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(8), 1727;

IoT Service Clustering for Dynamic Service Matchmaking

State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
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
Full-Text   |   PDF [3292 KB, uploaded 28 July 2017]   |  


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

Figure 1a

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).

Share & Cite This Article

MDPI and ACS Style

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

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top