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An energy‐efficient clustering and cross‐layer‐based opportunistic routing protocol (CORP) for wireless sensor network
Author(s) -
Shanmugam Ramalingam,
Kaliaperumal Baskaran
Publication year - 2021
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.4752
Subject(s) - computer science , wireless sensor network , cluster analysis , energy consumption , network packet , efficient energy use , computer network , quality of service , routing protocol , real time computing , network layer , distributed computing , layer (electronics) , ecology , chemistry , organic chemistry , machine learning , electrical engineering , biology , engineering
Summary Energy‐efficient data collection is a significant research challenge in wireless sensor networks (WSNs). One of the essential approaches to improve WSN performance is clustering, routing, and denoising technique. Cluster head (CH) selection is an essential issue in WSN. The present work proposes cross‐layer‐based opportunistic routing protocol (CORP) for WSN. The proposed CORP approach has been used to find an optimal traversal path, which reduces computation time and energy consumption as well as improves data delivery reliability. For optimal clustering, K‐medoid with adaptive Harris hawk optimization algorithm (AHHO) has been utilized for clustering the sensor nodes. Quality of service (QoS) impact, energy status criteria, distance, and sensor nodes position have been considered as key factors. These factors can influence the selection of CH in WSN. Moreover, the CH selection method can minimize traffic and energy saving. For efficient data collection, a hybrid Variable Weighted stacked Autoencoder with adaptive sunflower optimization (VWAE‐ASFO) denoising technique has been intended for reducing the data error rate. Initially, in the data collection phase, the variable weighted stacked autoencoder senses the data and collects all the data from the network. Adaptive sunflower optimization algorithm (ASFO) updates the weight of VWAE to minimize the data error and model complexity. The simulation results demonstrate that the proposed CORP approach enhances QoS performance metrics such as energy consumption, packet delivery ratio, packet delay, network lifetime, throughput, jitter, buffer occupancy, and packet loss ratio. When the performance of the proposed approach is compared with the existing algorithms such as HEED, TCBDGA, FRLDG, MOBFO‐EER, and FEEC‐IIR, the proposed approach outperforms them.