Node Deployment Optimization for Wireless Sensor Networks Based on Virtual Force-Directed Particle Swarm Optimization Algorithm and Evidence Theory

Wu, Liangshun; Qu, Junsuo; Shi, Haonan; Li, Pengfei

doi:10.3390/e24111637

Cited by 7 publications

(3 citation statements)

References 33 publications

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“…In the field of swarm intelligence algorithms, Opoku et al [ 9 ] combined an ant colony optimization algorithm with iterative conditional patterns for computing estimates of neural source activity. To optimize wireless sensor node deployment, Wu et al [ 10 ] proposed a virtual force-directed particle swarm optimization approach, where the optimization objective is to maximize network coverage. Dai et al [ 11 ] solved the problem of gravity anomaly matching using an artificial bee colony algorithm based on a radiation transformation.…”

Section: Introductionmentioning

confidence: 99%

Remora Optimization Algorithm with Enhanced Randomness for Large-Scale Measurement Field Deployment Technology

Yan

Liu

et al. 2023

Entropy

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In the large-scale measurement field, deployment planning usually uses the Monte Carlo method for simulation analysis, which has high algorithm complexity. At the same time, traditional station planning is inefficient and unable to calculate overall accessibility due to the occlusion of tooling. To solve this problem, in this study, we first introduced a Poisson-like randomness strategy and an enhanced randomness strategy to improve the remora optimization algorithm (ROA), i.e., the PROA. Simultaneously, its convergence speed and robustness were verified in different dimensions using the CEC benchmark function. The convergence speed of 67.5–74% of the results is better than the ROA, and the robustness results of 66.67–75% are better than those of the ROA. Second, a deployment model was established for the large-scale measurement field to obtain the maximum visible area of the target to be measured. Finally, the PROA was used as the optimizer to solve optimal deployment planning; the performance of the PROA was verified by simulation analysis. In the case of six stations, the maximum visible area of the PROA reaches 83.02%, which is 18.07% higher than that of the ROA. Compared with the traditional method, this model shortens the deployment time and calculates the overall accessibility, which is of practical significance for improving assembly efficiency in large-size measurement field environments.

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Section: Introductionmentioning

confidence: 99%

Remora Optimization Algorithm with Enhanced Randomness for Large-Scale Measurement Field Deployment Technology

Yan

Liu

et al. 2023

Entropy

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“…Early circuit partitioning algorithms mainly include KL [ 5 , 6 , 7 ] and FM [ 8 , 9 ]. With the development of machine learning theory, some heuristic algorithms, such as the genetic algorithm [ 10 , 11 , 12 , 13 , 14 ], the particle swarm optimization algorithm [ 15 , 16 ], the bird flock algorithm [ 17 ], etc., have also emerged. In order to further improve the calculation speed, multi-level partition algorithms [ 18 , 19 , 20 ], such as Metis [ 21 ] and /hMetis [ 22 ], etc., have received extensive attention in recent years.…”

Section: Introductionmentioning

confidence: 99%

Gate-Level Circuit Partitioning Algorithm Based on Clustering and an Improved Genetic Algorithm

Cheng

Yin

Jiang

et al. 2023

Entropy

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Gate-level circuit partitioning is an important development trend for improving the efficiency of simulation in EDA software. In this paper, a gate-level circuit partitioning algorithm, based on clustering and an improved genetic algorithm, is proposed for the gate-level simulation task. First, a clustering algorithm based on betweenness centrality is proposed to quickly identify clusters in the original circuit and achieve the circuit coarse. Next, a constraint-based genetic algorithm is proposed which provides absolute and probabilistic genetic strategies for clustered circuits and other circuits, respectively. This new genetic strategy guarantees the integrity of clusters and is effective for realizing the fine partitioning of gate-level circuits. The experimental results using 12 ISCAS ‘89 and ISCAS ‘85 benchmark circuits show that the proposed algorithm is 5% better than Metis, 80% better than KL, and 61% better than traditional genetic algorithms for finding the minimum number of connections between subsets.

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“…Usually, the smaller the fitness function value of the particle, the closer it is to the optimal solution. In WSNs, PSO has been used for clustering [14], routing [15], clustering and routing [16], localization [17], sleep scheduling [18], intrusion detection [19], congestion control [20], and so on [21][22][23]. In existing clustering and routing protocols based on PSO, they use PSO to reach two objectives, i.e.…”

Section: Introductionmentioning

confidence: 99%

A Novel Particle Swarm Optimization-Based Clustering and Routing Protocol for Wireless Sensor Networks

Huangshui,

Xinji,

Chuhang

et al. 2023

Wireless Pers Commun

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Extending the network lifetime as long as possible is one of the critical issues for wireless sensor networks (WSNs), which is usually resolved by using clustering and routing protocols. The clustering and routing processes are considered as an NP-hard problem popularly solved by swarm intelligence optimization algorithm. In this paper, a novel particle swarm optimization based clustering and routing protocol called NPSOP is proposed to maximize the network lifetime considering not only energy efficiency but also energy and load balance. In NPSOP, the particle swarm optimization (PSO) technique is used to select the cluster heads (CHs) and find the routing paths for each CH by encoding them into a single particle simultaneously. Moreover, the components of a particle is constrained by parameters residual energy, centrality, distance to the BS so as to improve the convergence speed. In addition, the fitness function considering network energy consumption and load balancing is derived to evaluate the quality of particles. And an adaptive inertial weight is used to update the status of each particle in order to escape from trapping into local optima. Iteratively, the global optimal solution can be reached in the end. The performance of NPSOP is evaluated by extensive experiments compared with existing approaches in terms of energy consumption, throughput, network lifetime, standard deviation of residual energy and load. The results show that NPSOP outperforms its comparatives.

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Node Deployment Optimization for Wireless Sensor Networks Based on Virtual Force-Directed Particle Swarm Optimization Algorithm and Evidence Theory

Cited by 7 publications

References 33 publications

Remora Optimization Algorithm with Enhanced Randomness for Large-Scale Measurement Field Deployment Technology

Remora Optimization Algorithm with Enhanced Randomness for Large-Scale Measurement Field Deployment Technology

Gate-Level Circuit Partitioning Algorithm Based on Clustering and an Improved Genetic Algorithm

A Novel Particle Swarm Optimization-Based Clustering and Routing Protocol for Wireless Sensor Networks

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