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A new meta-heuristic approach for efficient search in the Internet of Things

Citation

Ebrahimi, M and ShafieiBavani, E and Wong, RK and Chi, C-H, A new meta-heuristic approach for efficient search in the Internet of Things, Proceedings of the 12th IEEE International Conference on Services Computing, 27 June - 2 July 2015, New York City, NY, USA, pp. 264-270. ISBN 9781467372817 (2015) [Refereed Conference Paper]


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Copyright Statement

Copyright 2015 IEEE

DOI: doi:10.1109/SCC.2015.44

Abstract

The number of sensors deployed around the world is growing at a rapid pace when we are moving towards the Internet of Things (IoT). The widespread deployment of these sensors represents significant financial investment and technical achievement. These sensors continuously generate enormous amounts of data which is capable of supporting an almost unlimited set of high value proposition applications for users. Given that, effectively and efficiently searching and selecting the most related sensors of a userís interest has recently become a crucial challenge. In this paper, inspired by ant clustering algorithm, we propose an effective context-aware method to cluster sensors in the form of Sensor Semantic Overlay Networks (SSONs) in which sensors with similar context information gathered into one cluster. Firstly, sensors are grouped based on their types to create SSONs. Then, our meta-heuristic algorithm called AntClust has been performed to cluster sensors using their context information. Finally, a few useful adjustments have been applied to reduce the cost of sensor search process. Experiments show the scalability of AntClust in clustering sensors and significantly faster runtime on sensor search, when compared with existing systems.

Item Details

Item Type:Refereed Conference Paper
Keywords:Internet of Things, context-aware sensor search, ant-based clustering
Research Division:Information and Computing Sciences
Research Group:Computation Theory and Mathematics
Research Field:Analysis of Algorithms and Complexity
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in the Information and Computing Sciences
Author:Chi, C-H (Dr Chi-Hung Chi)
ID Code:110677
Year Published:2015
Web of Science® Times Cited:3
Deposited By:Computing and Information Systems
Deposited On:2016-08-09
Last Modified:2016-09-05
Downloads:0

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