Improved sand cat swarm optimization algorithm for enhancing coverage of wireless sensor networks

Li, Ying; Zhao, Liqiang; Wang, Yunfeng; Wen, Qin

doi:10.1016/j.measurement.2024.114649

Measurement

2024

DOI: 10.1016/j.measurement.2024.114649

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Improved sand cat swarm optimization algorithm for enhancing coverage of wireless sensor networks

Ying Li,

Liqiang Zhao,

Yunfeng Wang

et al.

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Cited by 5 publications

References 35 publications

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A Fault-Tolerant Clustering Approach for Target Tracking in Wireless Sensor Networks

Tabatabaei

2024

Wireless Pers Commun

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Objective: using the cat swarm optimization algorithm to reduce energy consumption and data transmission delay in WSNs (wireless sensor networks) Materials and Methods: WSNs have emerged as a critical sensing technology with a wide range of military and civilian applications. In these networks, a large number of sensors are distributed throughout an area to track a speci c target. Each sensor is regarded as an operating tool that uses limited energy. They can sense and understand events within their sensing domains, as well as communicate with neighbouring sensor nodes. Because the battery determines the lifespan of a sensor, an e cient method of energy consumption must be used. The depletion of batteries' limited energy reserves may result in the network's demise. As a result, devising and developing a time plan for sensors' sleep and wake-up cycles is regarded as a critical goal in line with increasing network lifetime. We proposed a novel and different time planning method for enhancing the lifetimes of WSNs in this paper, using the cat swarm optimization algorithm, in which at least half of the full coverage is provided at all times. The simulation results showed that using a cat swarm optimization algorithm can reduce energy consumption and data transmission delay. It also improves load balance. Opnet simulator version 11.5 was used for the experiments and simulation.s

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A Fault-Tolerant Clustering Approach for Target Tracking in Wireless Sensor Networks

Tabatabaei

2024

Wireless Pers Commun

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CMPSO: A novel co-evolutionary multigroup particle swarm optimization for multi-mission UAVs path planning

Hu,

Cheng,

Houssein

et al. 2025

Advanced Engineering Informatics

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MBSCSO: Multi-Strategy Boosted Sand Cat Swarm Optimization for Engineering Applications

Li,

Hu,

et al. 2024

IEEE Access

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Improved sand cat swarm optimization algorithm for enhancing coverage of wireless sensor networks

Cited by 5 publications

References 35 publications

A Fault-Tolerant Clustering Approach for Target Tracking in Wireless Sensor Networks

A Fault-Tolerant Clustering Approach for Target Tracking in Wireless Sensor Networks

CMPSO: A novel co-evolutionary multigroup particle swarm optimization for multi-mission UAVs path planning

MBSCSO: Multi-Strategy Boosted Sand Cat Swarm Optimization for Engineering Applications

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