Title: An efficient spatial query processing algorithm in multi-sink wireless sensor networks

Authors: Zheng Ma; Jin Zheng; Weijia Jia; Guojun Wang

Addresses: School of Information Science and Engineering, Central South University, Changsha, Hunan, 410083, China; School of Information Science and Engineering, Hunan University, Changsha, Hunan, 410082, China ' School of Information Science and Engineering, Central South University, Changsha, Hunan, 410083, China ' Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China ' School of Information Science and Engineering, Central South University, Changsha, Hunan, 410083, China; School of Computer Science and Educational Software, Guangzhou University, Guangzhou, Guangdong, 510006, China

Abstract: In order to address the problem of energy and time-efficient execution of spatial queries in multi-sink wireless sensor networks, an efficient hybrid spatial query processing algorithm (EHSQP) is proposed in this paper. In EHSQP, sink nodes perform the sector selection for the query region and seek the query route to a query region using the known infrastructure information. Sensor nodes can adjust the query dissemination and reversing route for query results based on the available local information. To minimise the energy consumption and the response time, the proposed EHSQP algorithm ensures that only the relevant nodes are involved in the query execution. Experimental results show that the proposed algorithm reduces communication cost significantly, and saves energy and time very effectively for the connected sensors in the given region. The proposed technique has an advantage over other techniques in terms of energy and time-efficient query cover with lower communication cost.

Keywords: multi-sink WSNs; wireless sensor networks; spatial queries; parallel processing; energy consumption; response time; energy efficiency.

DOI: 10.1504/IJSNET.2016.080372

International Journal of Sensor Networks, 2016 Vol.22 No.4, pp.274 - 282

Available online: 18 Nov 2016 *

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