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Open AccessArticle

Energy-Efficient Spatial Query-Centric Geographic Routing Protocol in Wireless Sensor Networks

by Xing Wang 1,2,3, Xuejun Liu 1,2,3,*, Meizhen Wang 1,2,3, Yunfeng Nie 4 and Yuxia Bian 5
Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China
State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
School of Information and Engineering, Nanchang Hang Kong University, Nanchang 330000, China
College of Resources and Environment, Chengdu University of Information Technology, Chengdu 610225, China
Author to whom correspondence should be addressed.
Sensors 2019, 19(10), 2363;
Received: 5 April 2019 / Revised: 19 May 2019 / Accepted: 20 May 2019 / Published: 22 May 2019
(This article belongs to the Special Issue Sensors In Target Detection)
In data-centric wireless sensor networks (WSNs), sensing data have a high time–space correlation. Most queries are spatial and used to obtain data in a defined region. Geographic routing (GR) protocols are the optimal choice for routing spatial queries. However, several drawbacks still exist in GRs, and these the include premature death of nodes and communication latency, which result in reduced network life and query efficiency. A new clustering GR protocol called quadtree grid (QTGrid) was proposed in this study to save energy and improve spatial query efficiency. First, the monitoring area was logically divided into clusters by a quadtree structure, and each grid’s location was encoded to reduce the memory overhead. Second, cluster head (CH) nodes were selected based on several metrics, such as distance from the candidate node to the grid center and adjacent CHs and residual energy. Third, the next-hop routing node was selected depending on the residual energy of the candidate node and its distance to the sink node. Lastly, a lossless data aggregation algorithm and a flexible spatial query algorithm were adopted to reduce the transmission of redundant data and meet the application requirements, respectively. Simulation results showed that compared with three related protocols, QTGrid has lower energy consumption and higher spatial query efficiency and is more suitable for large-scale WSN spatial query application scenarios. View Full-Text
Keywords: wireless sensor network; geographic routing protocol; spatial query; energy balance wireless sensor network; geographic routing protocol; spatial query; energy balance
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MDPI and ACS Style

Wang, X.; Liu, X.; Wang, M.; Nie, Y.; Bian, Y. Energy-Efficient Spatial Query-Centric Geographic Routing Protocol in Wireless Sensor Networks. Sensors 2019, 19, 2363.

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