Network energy optimization and intelligent routing in WSN applicable for IoT using self‐adaptive coyote optimization algorithm

Summary The Internet of Things (IoT) is a recent wireless telecommunications platform, which contains a set of sensor nodes linked by wireless sensor networks (WSNs). These approaches split the sensor nodes into clusters, in which each cluster consists of an exclusive cluster head (CH) node. The maj...

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Vydáno v:International journal of communication systems Ročník 36; číslo 9
Hlavní autoři: Naveen, G., Prathap, P. M. Joe
Médium: Journal Article
Jazyk:angličtina
Vydáno: Chichester Wiley Subscription Services, Inc 01.06.2023
Témata:
Adaptive algorithms
clone
Clusters
Heuristic methods
intelligent routing
Internet of Things
Krill
multi‐objective routing
network energy optimization
Nodes
Optimization
Optimization algorithms
Residual energy
Routing (telecommunications)
self‐adaptive coyote optimization algorithm
Sensors
Wireless sensor networks
ISSN:1074-5351, 1099-1131
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Shrnutí:Summary The Internet of Things (IoT) is a recent wireless telecommunications platform, which contains a set of sensor nodes linked by wireless sensor networks (WSNs). These approaches split the sensor nodes into clusters, in which each cluster consists of an exclusive cluster head (CH) node. The major scope of this task is to introduce a novel CH selection in WSN applicable to IoT using the self‐adaptive meta‐heuristic algorithm. This paper aids in providing the optimal routing in the network based on direct node (DN) selection, CH selection, and clone cluster head (CCH) selection. DNs are located near the base station, and it is chosen to avoid the load of CH. The adoption of the novel self‐adaptive coyote optimization algorithm (SA‐COA) is used for the DN selection and CCH selection. When the nodes are assigned in the network, DN and CCH selection is performed by the proposed SA‐COA. Then, the computation of residual energy helps to select the CH, by correlating with the threshold energy. CCH is proposed to copy the data from the CH to avoid the loss of data in transmitting. By forming the CCH, the next CH can be easily elected with the optimal CCH using SA‐COA. From the simulation findings, the best value of the designed SA‐COA‐LEACH model is secured at 1.14%, 3.17%, 1.18%, and 7.33% progressed than self‐adaptive whale optimization algorithm (SAWOA), cyclic rider optimization algorithm (C‐ROA), krill herd algorithm (KHA), and COA while taking several nodes 50. The proposed routing of sensor networks specifies better performance than the existing methods. A cluster selection in WSN using the self‐adaptive meta‐heuristic algorithm is used for energy‐efficient routing in WSNs. The adoption of the novel self‐adaptive coyote optimization algorithm (SA‐COA) is used for the DN selection and CCH selection. The optimal routing is performed by SA‐COA to attain the best routing in WSN for IoT with the aim of network reliability and achieve high network performance.
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ISSN:1074-5351
1099-1131
DOI:10.1002/dac.5464