DEDC: Joint Density-Aware and Energy-Limited Path Construction for Data Collection Using Mobile Sink in WSNs

doi:10.1109/ACCESS.2020.2989763

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Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/118874

Title:	DEDC: Joint Density-Aware and Energy-Limited Path Construction for Data Collection Using Mobile Sink in WSNs
Authors:	Wen, Weimin;Shang, Cuijuan;Chang, Chih-Yung;Roy, Diptendu Sinha
Keywords:	Sensors;Wireless sensor networks;Data collection;Energy consumption;Monitoring;Heuristic algorithms;Computer science
Date:	2020-04-23
Issue Date:	2020-07-07 12:10:29 (UTC+8)
Abstract:	Data collection is one of the most important issues in Wireless Sensor Networks (WSNs). Many data collection algorithms have been proposed for collecting data using a mobile sink. However, the appropriateness and the number of the selected anchors, which significantly impact the network lifetime of the given WSNs, still can be improved. This paper proposes a Joint Density-Aware and Energy-Limited Path Construction algorithm for Data Collection, called DEDC, aiming to select as more as possible appropriate anchors under the path length constraint for prolonging the network lifetime. Initially, the proposed DEDC determines the grid size according to the path length constraint, partitions the monitoring region into several grids and identifies the grids to be balance or unbalance grids. Based on the partitioned grids, the proposed DEDC constructs a regular path and then further adjusts the path segments for these unbalanced grids. The regular path construction and path adjustment aim to construct a path passing through as more as possible anchors for balancing the forwarding loads and prolonging the network lifetime. Performance evaluations reveal that the proposed DEDC outperforms existing data collection mechanisms in terms of energy consumption, network lifetime, and SD energy consumptions.
Relation:	IEEE Access 8, p.78942-78955
DOI:	10.1109/ACCESS.2020.2989763
Appears in Collections:	[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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