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EE‐hHHSS: Energy‐efficient wireless sensor network with mobile sink strategy using hybrid Harris hawk‐salp swarm optimization algorithm
Author(s) -
Srinivas Madana,
Amgoth Tarachand
Publication year - 2020
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.4569
Subject(s) - computer science , wireless sensor network , network packet , efficient energy use , base station , energy consumption , computer network , real time computing , end to end delay , mobile wireless sensor network , key distribution in wireless sensor networks , wireless , wireless network , engineering , telecommunications , electrical engineering
Summary Wireless sensor network (WSN) includes power‐efficient sensor nodes to convey information to the base station (BS). This network comprises a number of sensor nodes that perform sensing, processing, and wireless communication abilities to monitor a specified sensing field. Therefore, it is necessary to prolong WSN lifetime using energy efficient optimization methods, because the sensor nodes are operated by battery. Also, it is difficult to replace the battery of the nodes located in harsh environments. Thus, energy‐efficient routing is a crucial phenomenon in WSN. In this article, energy‐efficient WSN with mobile sink (MS) strategy is proposed using hybrid Harris hawk and salp swarm (Hybrid HH‐SS) optimization algorithm. In order to achieve energy efficiency, finding an optimal route for MS is a critical task. The MS discovers an optimal path to interconnect with the cluster heads (CHs) by adaptive ant colony optimization (AACO) algorithm. Hence, the proposed hybrid algorithm minimizes the energy consumption (EC), packet loss rate (PLR), and end‐to‐end (E2E) delay and enhances the lifetime of the network. The proposed work is implemented in JAVA platform, and simulation outcomes show that the proposed approach enhances the wireless sensor network performances. Simulation results show enhancement in energy efficiency in terms of network lifetime, packet delivery rate, average throughput, packet loss rate, energy efficiency, and end‐to‐end delay.