An Energy aware Routing Mechanism in WSNs using PSO and GSO Algorithm

Asha, G.; Gowrishankar,

doi:10.1109/spin.2018.8474140

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…But, there is need of research that could consider more parameters such as size of network, node count, size of packet, initial energy of sensor nodes, communication radius, alive node, area, weight of nodes, data delivery cost, network lifetime, normalized nodes, packet delivery ratio. It has been observed that there is need of research that should be capable of providing energy efficient, cost effective, optimized path having highly reliable network with better data delivery ratio (Vinodini and Gomathi, 2019; Qasim et al , 2018; Asha and Gowrishankar, 2018; Reddy and Gayathri, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

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Hybrid artificial bee colony and glow worm algorithm for energy efficient cluster head selection in wireless sensor networks

Kaur

Rani

Dahiya³

2021

WJE

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Purpose This paper aim to find optimal cluster head and minimize energy wastage in WSNs. Wireless sensor networks (WSNs) have low power sensor nodes that quickly lose energy. Energy efficiency is most important factor in WSNs, as they incorporate limited sized batteries that would not be recharged or replaced. The energy possessed by the sensor nodes must be optimally used so as to increase the lifespan. The research is proposing hybrid artificial bee colony and glowworm swarm optimization [Hybrid artificial bee colony and glowworm swarm optimization (HABC-GSO)] algorithm to select the cluster heads. Previous research has considered fitness-based glowworm swarm with Fruitfly (FGF) algorithm, but existing research was limited to maximizing network lifetime and energy efficiency. Design/methodology/approach The proposed HABC-GSO algorithm selects global optima and improves convergence ratio. It also performs optimal cluster head selection by balancing between exploitation and exploration phases. The simulation is performed in MATLAB. Findings The HABC-GSO performance is evaluated with existing algorithms such as particle swarm optimization, GSO, Cuckoo Search, Group Search Ant Lion with Levy Flight, Fruitfly Optimization algorithm and grasshopper optimization algorithm, a new FGF in the terms of alive nodes, normalized energy, cluster head distance and delay. Originality/value This research work is original.

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Section: Literature Reviewmentioning

confidence: 99%

“…Hybrid approach that made use of PSO and GSO algorithms were used of cost effective routing and routing mechanism that is aware of energy in WSNs, came in existence. DV-hop node location algorithm depending on GSO was proposed for wireless sensor network (Asha and Gowrishankar, 2018; Song et al , 2019) (Table 1).…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid artificial bee colony and glow worm algorithm for energy efficient cluster head selection in wireless sensor networks

Kaur

Rani

Dahiya³

2021

WJE

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“…In WSNs, PSO has been used to address several problems such as energy conservation [39], coverage maximization [40], optimal deployment of sensors [41], clustering, clustering head selection, data aggregation [42], and node localization [43].…”

Section: A Tdma Scheduling Optimizationmentioning

confidence: 99%

A Modified Particle Swarm Optimization Approach for Latency of Wireless Sensor Networks

Elrefaei¹,

Yahya²,

Shaat³

et al. 2021

IJACSA

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In time-sensitive applications, such as detecting environmental and individual nuclear radiation exposure, wireless sensor networks are employed.. Such application requires timely detection of radiation levels so that appropriate emergency measures are applied to protect people and the environment from radiation hazards. In these networks, collision and interference in communication between sensor nodes cause more end-to-end delay and reduce the network's performance. A time-division multiple-access (TDMA) media access control protocol guarantees minimum latency and low power consumption. It also overcomes the problem of interference. TDMA scheduling problem determines the minimum length conflict-free assignment of slots in a TDMA frame where each node or link is activated at least once. This paper proposes a meta-heuristic centralized contention-free approach based on TDMA, a modified particle swarm optimization. This approach realizes the TDMA scheduling more efficiently compared with other existing algorithms. Extensive simulations were performed to evaluate the modified approach. The simulation results prove that the proposed scheduling algorithm has a better performance in wireless sensor networks than the interference degree leaves order algorithm and interference degree remaining leaves order algorithm. The results demonstrate also that integrating the proposed algorithm in TDMA protocols significantly optimizes the communication latency reduction and increases the network reliability.

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An Efficient Clustering and Routing Algorithm for Wireless Sensor Networks Using GSO and KGMO Techniques

Asha

Gowrishankar

2019

Advances in Intelligent Systems and Computing

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An Energy aware Routing Mechanism in WSNs using PSO and GSO Algorithm

Cited by 5 publications

References 20 publications

Hybrid artificial bee colony and glow worm algorithm for energy efficient cluster head selection in wireless sensor networks

Hybrid artificial bee colony and glow worm algorithm for energy efficient cluster head selection in wireless sensor networks

A Modified Particle Swarm Optimization Approach for Latency of Wireless Sensor Networks

An Efficient Clustering and Routing Algorithm for Wireless Sensor Networks Using GSO and KGMO Techniques

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