QoS Oriented and Energy Efficient Routing Protocol for Cooperative MIMO Based Mobile WSN: Q-E2RPC

Reddy, Kummathi Chenna; Devanagavi, Geetha D.; Thippeswamy, M N

doi:10.25103/jestr.121.25

Cited by 6 publications

(1 citation statement)

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“…In this section, the recent research studies in denoising, efficient data collection, clustering, and routing in WSN have been presented. Reddy et al [15] have introduced an energy‐effective routing protocol for WSN. Each WSN node involves transferring the gathered information to the sink node and leads to unnecessary redundancy due to similar data.…”

Section: Related Workmentioning

confidence: 99%

An energy‐efficient clustering and cross‐layer‐based opportunistic routing protocol (CORP) for wireless sensor network

Shanmugam

Baskaran

2021

Int J Communication

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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.

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Section: Related Workmentioning

confidence: 99%