A Hybrid Genetic Algorithm With Bidirectional Mutation for Maximizing Lifetime of Heterogeneous Wireless Sensor Networks

Li, Jingjing; Luo, Zhongzhen; Xiao, Jing

doi:10.1109/access.2020.2988368

Cited by 27 publications

(27 citation statements)

References 41 publications

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“…In order to reduce energy consumption in the network, a multi-objective energy-aware routing protocol is introduced to achieve the best path selection, namely, multi-objective fractional particle lion algorithm (MOFPL) [ 32 ]. In this paper, to increase the lifetime of the network the author developed a hybrid genetic algorithm which is the combination of greedy initialization and bidirectional mutation [ 33 , 34 ].…”

Section: Related Workmentioning

confidence: 99%

MWCSGA—Multi Weight Chicken Swarm Based Genetic Algorithm for Energy Efficient Clustered Wireless Sensor Network

Ajmi

Helali

Lorenz

et al. 2021

Sensors

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Nowadays due to smart environment creation there is a rapid growth in wireless sensor network (WSN) technology real time applications. The most critical resource in in WSN is battery power. One of the familiar methods which mainly concentrate in increasing the power factor in WSN is clustering. In this research work, a novel concept for clustering is introduced which is multi weight chicken swarm based genetic algorithm for energy efficient clustering (MWCSGA). It mainly consists of six sections. They are system model, chicken swarm optimization, genetic algorithm, CCSO-GA cluster head selection, multi weight clustering model, inter cluster, and intra cluster communication. In the performance evaluation the proposed model is compared with few earlier methods such as Genetic Algorithm-Based Energy-Efficient Adaptive Clustering Protocol For Wireless Sensor Networks (GA-LEACH), Low energy adaptive Clustering hierarchy approach for WSN (MW-LEACH) and Chicken Swarm Optimization based Genetic Algorithm (CSOGA). During the comparison it is proved that our proposed method performed well in terms of energy efficiency, end to end delay, packet drop, packet delivery ratio and network throughput.

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Section: Related Workmentioning

confidence: 99%

MWCSGA—Multi Weight Chicken Swarm Based Genetic Algorithm for Energy Efficient Clustered Wireless Sensor Network

Ajmi

Helali

Lorenz

et al. 2021

Sensors

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“…Based on the performance of the routing scenario used, computing and processing power is minimal considering the limited battery power [18]. For this reason, in paper [19], the author uses the genetic algorithm (GA) for simulation experiments in multihop QoS routing wireless networks, and the performance of the algorithm is analyzed from the aspects of scalability, energy consumption, and HDWSN life cycle. It can maximize the activity of the sensor by saving energy, thereby extending the service life of the network.…”

Section: Related Workmentioning

confidence: 99%

“…In equation (19), Δτ de ðg, g + 1Þ represents the pheromone content that the ant remaining on the link ðd, eÞ during ðg, g + 1Þ round. ρ represents the volatility factor of pheromone, which is used to reduce the accumulated pheromone on the link.…”

Section: Pheromone Updatementioning

confidence: 99%

Minimization of Energy Consumption for Routing in High‐Density Wireless Sensor Networks Based on Adaptive Elite Ant Colony Optimization

Xiao

Zhou

2021

Journal of Sensors

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High-density wireless sensor networks (HDWSNs) are usually deployed randomly, and each node of the network collects data from complex environments. Because the energy of sensor nodes is powered by batteries, it is basically impossible to replace batteries or charge in the complex surroundings. In this paper, a QoS routing energy consumption model is designed, and an improved adaptive elite ant colony optimization (AEACO) is proposed to reduce HDWSN routing energy consumption. This algorithm uses the adaptive operator and the elite operator to accelerate the convergence speed. So, as to validate the efficiency of AEACO, the AEACO is contrast with particle swarm optimization (PSO) and genetic algorithm (GA). The simulation outcomes show that the convergence speed of AEACO is sooner than PSO and GA. Moreover, the energy consumption of HDWSNs using AEACO is reduced by 30.7% compared with GA and 22.5% compared with PSO. Therefore, AEACO can successfully decrease energy consumption of the whole HDWSNs.

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“…Perform data simulation to judge the performance of EASA. Simulation results indicate the proposed algorithm can achieve a higher working life of LSWSNs over GA and PSO [8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 97%

Elite Adaptive Simulated Annealing Algorithm for Maximizing the Lifespan in LSWSNs

et al. 2021

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Large-scale wireless sensor networks (LSWSNs) are currently one of the most influential technologies and have been widely used in industry, medical, and environmental monitoring fields. The LSWSNs are composed of many tiny sensor nodes. These nodes are arbitrarily distributed in a certain area for data collection, and they have limited energy consumption, storage capabilities, and communication capabilities. Due to limited sensor resources, traditional network protocols cannot be directly applied to LSWSNs. Therefore, the issue of maximizing the LSWSNs’ lifetime by working with duty cycle design algorithm has been extensively studied in this paper. Encouraged by annealing algorithm, this work provides a new elite adaptive simulated annealing (EASA) algorithm to prolong LSWSNs’ lifetime. We then present a sensor duty cycle models, which can make sure the full coverage of the monitoring targets and prolong the network lifetime as much as possible. Simulation results indicate that the network lifetime of EASA algorithm is 21.95% longer than that of genetic algorithm (GA) and 28.33% longer than that of particle swarm algorithm (PSO).

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A Hybrid Genetic Algorithm With Bidirectional Mutation for Maximizing Lifetime of Heterogeneous Wireless Sensor Networks

Cited by 27 publications

References 41 publications

MWCSGA—Multi Weight Chicken Swarm Based Genetic Algorithm for Energy Efficient Clustered Wireless Sensor Network

MWCSGA—Multi Weight Chicken Swarm Based Genetic Algorithm for Energy Efficient Clustered Wireless Sensor Network

Minimization of Energy Consumption for Routing in High‐Density Wireless Sensor Networks Based on Adaptive Elite Ant Colony Optimization

Elite Adaptive Simulated Annealing Algorithm for Maximizing the Lifespan in LSWSNs

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