A Deployment and Coverage Optimization Algorithm for Self-Powered Wireless Sensor Networks Based on Hybrid Swarm Intelligence

Zhang, Lingli

doi:10.1109/jsen.2022.3230955

Cited by 5 publications

(3 citation statements)

References 35 publications

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“…Finally, the solution is updated according to formula (1). The specific iteration diagram of equilibrium optimization algorithm is shown as follows (Figure 1).…”

Section: Algorithm Optimizationmentioning

confidence: 99%

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Optimization of 3D WSN coverage based on equilibrium optimization algorithm

2023

jaip

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Coverage optimization is one of the basic problems in wireless sensor networks. Coverage reflects the service quality provided by wireless sensor networks. Swarm intelligence algorithm is an optimization method inspired by natural organisms. Node coverage optimization is also an optimization problem. Swarm intelligence algorithm can solve the coverage problem of wireless sensor networks. Therefore, this paper focuses on the application of swarm intelligence algorithm in coverage optimization of wireless sensor networks, and proposes a coverage optimization strategy based on swarm intelligence algorithm: coverage optimization of three-dimensional wireless sensor networks based on equilibrium optimization algorithm. In this algorithm, the principle is to control the volume and mass balance model, the particle concentration update according to the equilibrium candidate solution, and finally reach the equilibrium state, which mainly consists of three stages: population initialization, equilibrium pool and concentration update. In the simulation, the equilibrium optimization algorithm has higher effective coverage than the particle swarm optimization algorithm.

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“…Finally, the solution is updated according to formula (1). The specific iteration diagram of equilibrium optimization algorithm is shown as follows (Figure 1).…”

Section: Algorithm Optimizationmentioning

confidence: 99%

“…It is this wireless sensor network that integrates the logical information world with the objective physical world and changes the way human interacts with nature. [1].…”

Section: Introductionmentioning

confidence: 99%

Optimization of 3D WSN coverage based on equilibrium optimization algorithm

2023

jaip

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“…Simulation results show that AFSA increases the sensors’ coverage in WSN [ 27 ]. In 2015, Zhang proposed a hybrid algorithm of particle swarm and firefly, with particle swarm as the main body and firefly for local search, thus improving the sensor coverage [ 28 ]. In 2016, Wu et al suggested an improved adaptive PSO-based coverage optimization.…”

Section: Introductionmentioning

confidence: 99%

Coverage Optimization of Heterogeneous Wireless Sensor Network Based on Improved Wild Horse Optimizer

et al. 2023

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One of the most important challenges for heterogeneous wireless sensor networks (HWSNs) is adequate network coverage and connectivity. Aiming at this problem, this paper proposes an improved wild horse optimizer algorithm (IWHO). Firstly, the population’s variety is increased by using the SPM chaotic mapping at initialization; secondly, the WHO and Golden Sine Algorithm (Golden-SA) are hybridized to improve the WHO’s accuracy and arrive at faster convergence; Thirdly, the IWHO can escape from a local optimum and broaden the search space by using opposition-based learning and the Cauchy variation strategy. The results indicate that the IWHO has the best capacity for optimization by contrasting the simulation tests with seven algorithms on 23 test functions. Finally, three sets of coverage optimization experiments in different simulated environments are designed to test the effectiveness of this algorithm. The validation results demonstrate that the IWHO can achieve better and more effective sensor connectivity and coverage ratio compared to that of several algorithms. After optimization, the HWSN’s coverage and connectivity ratio attained 98.51% and 20.04%, and after adding obstacles, 97.79% and 17.44%, respectively.

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