Lifetime improved WSN using enhanced-LEACH and angle sector-based energy-aware TDMA scheduling. Issue 1 (1st January 2020)

• Record Type: Journal Article
• Authors: Sinde, Ramadhani; Begum, Feroza; Njau, Karoli; Kaijage, Shubi
• Editors: Shih, Kuei-Ping
• Is Part Of: Cogent engineering. Volume 7:Issue 1(2020)
• Publication Date: 2020-01-01
• Subjects: clustering -- network lifetime -- optimization -- sensor nodes -- sleep scheduling; Engineering -- Periodicals; Technology -- Periodicals; Engineering; Technology; Periodicals; 620
• DOI: 10.1080/23311916.2020.1795049
• Languages: English
• ISSNs: 2331-1916

Abstract

Abstract: Network lifetime remains as a significant requirement in Wireless Sensor Network (WSN) exploited to prolong network processing. Deployment of low power sensor nodes in WSN is essential to utilize the energy efficiently. Clustering and sleep scheduling are the two major processes involved in improving network lifetime. However, abrupt and energy unaware selection of cluster head (CH) is non-optimal in WSN which reflects in the drop of energy among sensor nodes. This paper addresses the twofold as utilization of sensor nodes to prolong the node's energy and network lifetime by LEACH-based cluster formation and Time Division Multiple Access scheduling (TDMA). Clusters are constructed by the design of an Enhanced-Low-Energy adaptive Clustering Hierarchy protocol (E-LEACH) that uses parallel operating optimization (Grey Wolf Optimization (GWO) and Discrete Particle Swarm Optimization (D-PSO)) for selecting an optimal CH and helper CH. The fitness values estimation from GWO and D-PSO is concatenated to prefer the best optimal CH. E-LEACH also manages the cluster size which is one of the conventional disadvantages in LEACH. CHs are responsible to perform energy-aware TDMA scheduling which segregates the coverage area into 24 sectors. Alternate sectors are assigned to be operated on any one of the three states as sense, transmit and sleep. Lastly, for mitigating packet loss, a channel is chosen between CH and sink node using the dynamic fuzzy algorithm. The extensive performances are evaluated in terms of energy consumption, throughput, delay, packet loss and network lifetime.