Mobile Multi-Sink Nodes Path Planning Algorithm Concerned With Energy Balance in Wireless Sensor Networks

Sun, Supeng; Zhao, Jianping; Feng, Xia‐Ting; Zhang, Jing; Luo, Jiaqi

doi:10.1109/access.2019.2929583

Cited by 9 publications

(2 citation statements)

References 22 publications

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“…Sun et al [23] presented hybrid positive and negative particle swarm optimization algorithm (HPNPSOA) to determine the optimal path for multiple mobile sinks and stop positions in hexagonal grids. The authors concluded that their proposed approach eliminated the energy hole problem, reduced network latency, and prolonged the network lifetime.…”

Section: A Optimized Mobile Sink Path Without Clusteringmentioning

confidence: 99%

Optimized Energy – Efficient Path Planning Strategy in WSN With Multiple Mobile Sinks

et al. 2021

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Recently, wireless sensor networks (WSNs) were perceived as the foundation infrastructure that paved the way to the emergence of the Internet of Things (IoT). However, a challenging issue exists when WSNs are integrated into the IoT because of high energy consumption in their nodes and poor network lifespan. Therefore, the elementary discussions in WSN are energy scarcity in sensor nodes, sensors' data exchange, and routing protocols. To address the aforesaid shortcomings, this paper develops an optimized energy-efficient path planning strategy that prolongs the network lifetime and enhances its connectivity. The proposed approach has four successive procedures: initially, the sensing field is partitioned into equal regions depending on the number of deployed mobile sinks that eliminate the energyhole problem. A new heuristic clustering approach called stable election algorithm (SEA) is introduced to minimize the message exchange between sensor nodes and prevent frequent cluster heads rotation. A sojourn location determination algorithm is proposed based on the minimum weighted vertex cover problem (MWVCP) to find the best position for the sinks to stop and collect the data from cluster heads. Finally, three optimization techniques are utilized to evaluate the optimized mobile sinks' trajectories using multi-objective evolutionary algorithms (MOEAs). Whilst the performance of the developed work was evaluated in terms of cluster heads number, network lifetime, the execution time of the sinks' sojourn locations determination algorithm, the convergence rate of optimization techniques, and data delivery. The simulation scenarios conducted in MATLAB and the obtained results showed that the introduced approach outperformed comparable existing schemes. It succeeded in prolonging the network lifetime up to 66% compared to existing routing protocols.

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Section: A Optimized Mobile Sink Path Without Clusteringmentioning

confidence: 99%

Optimized Energy – Efficient Path Planning Strategy in WSN With Multiple Mobile Sinks

et al. 2021

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“…Intelligent manufacturing has emerged with the rapid development of information technology. IWSNs are one of the key technologies of intelligent manufacturing, and they are a combination of IWSNs and the Internet of Things [1][2][3]. Compared with traditional wired industrial sensor networks, it has the characteristics of fast deployment, high flexibility, and self-organization.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Immune Ant Colony Optimization for Reducing Energy Consumption of Automatic Inspection Path Planning in Industrial Wireless Sensor Networks

Xiao

Liu

et al. 2021

Journal of Sensors

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Industrial wireless sensor networks (IWSNs) are usually fixedly deployed in industrial environments, and various sensor nodes cooperate with each other to complete industrial production tasks. The efficient work of each sensor node of IWSNs will improve the efficiency of the entire network. Automated robots need to perform timely inspection and maintenance of IWSNs in an industrial environment. Excessive inspection distance will increase inspection costs and increase energy consumption. Therefore, shortening the inspection distance can reduce production energy consumption, which is very important for the efficient operation of the entire system. However, the optimal detection path planning of IWSNs is an N-P problem, which can usually only be solved by heuristic mathematical methods. This paper proposes a new adaptive immune ant colony optimization (AIACO) for optimizing automated inspection path planning. Moreover, novel adaptive operator and immune operator are designed to prevent the algorithm from falling into the local optimum and increase the optimization ability. In order to verify the performance of the algorithm, the algorithm is compared with genetic algorithm (GA) and immune clone algorithm (ICA). The simulation results show that the inspection distance of IWSNs using AIACO is lower than that of GA and ICA. In addition, the convergence speed of AIACO is faster than that of GA and ICA. Therefore, the AIACO proposed in this paper can effectively reduce the inspection energy consumption of the entire IWSN system.

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Energy-balanced path optimization of UAV-assisted wireless power and information system

Guo

Yang

et al. 2022

Wireless Netw

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Mobile Multi-Sink Nodes Path Planning Algorithm Concerned With Energy Balance in Wireless Sensor Networks

Cited by 9 publications

References 22 publications

Optimized Energy – Efficient Path Planning Strategy in WSN With Multiple Mobile Sinks

Optimized Energy – Efficient Path Planning Strategy in WSN With Multiple Mobile Sinks

An Adaptive Immune Ant Colony Optimization for Reducing Energy Consumption of Automatic Inspection Path Planning in Industrial Wireless Sensor Networks

Energy-balanced path optimization of UAV-assisted wireless power and information system

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