Lossless address data compression using quadtree clustering of the sensors in a grid based WSN

Thomas, Seemon; Mathew, Thomaskutty

doi:10.1016/j.adhoc.2016.11.015

Cited by 3 publications

(1 citation statement)

References 29 publications

(40 reference statements)

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“…EC-based improved GRs have been developed for many specific sensor networks in recent years [27,28,29,30,31]. However, we only reviewed studies that are relevant to our proposed work, that is, those that address energy efficiency [31,32,33,34,35,36], load balancing [37,38,39], and spatial query issues [40,41,42,43]. We organized this review by discussing energy-efficiency and load-balancing EC algorithms, followed by several spatial query efficiency GR issues.…”

Section: Related Workmentioning

confidence: 99%

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

Wang

Liu

Wang

et al. 2019

Sensors

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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.

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