WSEAS Transactions on Computers
Print ISSN: 1109-2750, E-ISSN: 2224-2872
Volume 20, 2021
A Segment-based Tree Traversal Algorithm for Enhancing Data Gathering in Wireless Sensor Networks
Authors: ,
Abstract: Wireless Sensor Networks (WSNs) are sink-based networks in which assigned sinks gather all data sensed by lightweight devices that are deployed in natural areas. The sensor devices are energy-scarce, therefore, energy-efficient protocols need to be designed for this kind of technology. Power-Efficient GAthering in Sensor Information Systems (PEGASIS) protocol is an energy-efficient data gathering protocol in which a chain is constructed using a greedy approach. This greedy approach has appeared to have unbalanced distances among the nodes which result in unfair energy consumption. Tree traversal algorithms have been used to improve the constructed chain to distribute the energy consumption fairly. In this research, however, a new segmentbased tree traversal approach is introduced to further improve the constructed chain. Our new proposed algorithm first constructs initial segments based on a list of nodes that are sorted according to post-order traversal. Afterwards, it groups these segments and concatenates them one by one according to their location; thus, our proposed approach uses location-awareness to construct a single balanced chain in order to use it for the data gathering process. This approach has been evaluated under various numbers of sensor devices in the network field with respect to various crucial performance metrics. It is shown in our conducted simulation results that our proposed segment-based chain construction approach produces shorter chains and shorter transmission ranges which as a result has improved the overall energy consumption per round, network lifetime, and end-to-end delay
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Keywords: Wireless Sensor Networks (WSNs), data aggregation, energy-efficient, greedy algorithm, PEGASIS protocol, tree traversal, Minimum Spanning Tree (MST), Segment-Based Tree Traversal (SBTT), cross elimination method,
Pages: 66-73
DOI: 10.37394/23205.2021.20.8