Hybrid Metaheuristic Algorithm for Cluster Head Selection in WSN

Rathod, Sankul

doi:10.46253/jnacs.v3i4.a1

Cited by 5 publications

(1 citation statement)

References 14 publications

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“…Multihop data transmission is generally used in the larger scale network [9,12]. Each of the cluster groups in the network comprises a single header termed CH that efficiently interacts with other members of CH in the network [13][14][15]. As the nodes need a higher quantity of energy to transfer data to BS directly, it is necessary to utilize routing protocols in the clustered WSN for identifying the finest route among CH to BS for minimizing energy consumption [9,[16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous wireless information and power transmission‐based power transfer and energy prediction for efficient communication with golden Taylor sea lion optimization in wireless sensor network

Sureshkumar

Vimala

2023

Int J Communication

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Summary Wireless Sensor Network (WSN) is an emerging lower cost and resourceful solution, which enables controlled observation of the environment. The high amount of energy is required in wireless networks during the transmission of data. Here, Golden Ant Lion Whale Optimization (GALWO) and Golden Taylor Sea Lion Optimization (GTSLnO) techniques are presented for cluster head (CH) selection and prediction of neighbor nodes' age. The six stages performed in this work are setup, steady‐state, prediction, power transfer, communication or route discovery, and route maintenance stages. In the setup level, CH selection is carried out by GALWO, which is the combination of Ant Lion Whale Optimization (ALWO) with Golden Search Optimization (GSO). Moreover, ALWO is an integration of Ant Lion Optimizer (ALO) with the Whale Optimization Algorithm (WOA). In the steady state, the distance, energy, delay, throughput, and trust update are considered as objective functions. In the prediction stage, the Deep Convolutional Neural Network (Deep CNN) is utilized for age prediction of neighbor nodes, wherein Deep CNN is tuned by employing GTSLnO. The GTSLnO is an incorporation of GSO and Taylor series with Sea Lion Optimization (SLnO). Then, the power transfer stage is done utilizing simultaneous wireless information and power transmission (SWIPT). Thereafter, the communication/route discovery stage is conducted for path selection through neighbor node, and lastly, the route maintenance stage is carried out. The GTSLnO–Deep CNN achieved a minimal delay of 0.089 s, maximal residual energy, throughput, and trust of 0.500 J, 98,843 kbps, and 0.452 for DoS attack.

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