Hybrid WSN Node Deployment optimization Strategy Based on CS Algorithm

Xiang, Tingli; Wang, Hongiun; Shi, Yingchun

doi:10.1109/itnec.2019.8729481

Cited by 18 publications

(19 citation statements)

References 10 publications

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“…The second method deals with network connectivity and energy consumption. Paper [23] divides the deployment set of all sensors into disjoint subsets or sensor coverings so that each sensor covering can monitor the entire target at any time. Then, a cuckoo search algorithm is proposed to optimize the sensor coverage problem to achieve the goal of maximizing the coverage of target points.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Niche Immune Genetic Algorithm for Fault Detection Coverage in Industry Wireless Sensor Network

et al. 2021

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The industry wireless sensor network (IWSN) technology, which is used to monitor industrial equipment, has attracted more and more attention in recent years. Sensor nodes in IWSN can spontaneously complete distributed networking and carry out monitoring tasks under random deployment conditions. Therefore, a self-organized IWSN is particularly suitable for the fault detection and diagnosis of industrial equipment in complex environments. However, due to the detection, ability of a single sensor node is limited, and the monitoring distribution problem is a typical multidimensional discrete NP-hard combinatorial stochastic optimization problem, which is challenging to solve for the traditional mathematical methods. With the purpose of improving the target monitoring capability and prolonging lifetime of IWSN, a novel hybrid niche immune genetic algorithm (HNIGA) for optimizing the target coverage model of fault detection is proposed. It uses the genetic operation to evolve antibody groups and applies niche technology to maintain the diversity of antibody groups. As a result, HNIGA can effectively reduce the failure rate of detection targets. To verify the performance of HNIGA, a series of simulations under different simulation conditions are carried out. Specifically, HNIGA is compared with genetic algorithm (GA) and simulated annealing (SA). Simulation results show that HNIGA has a faster convergence speed and more robust global search capability than the other two algorithms.

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Section: Related Workmentioning

confidence: 99%

Hybrid Niche Immune Genetic Algorithm for Fault Detection Coverage in Industry Wireless Sensor Network

et al. 2021

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“…Amulya Anurag [3] proposed a negative speed PSO algorithm to improve the local search ability and obtain higher coverage efficiency. Tingli Xiang [19] proposed an optimization based on Cuckoo Search (CS) which divided the algorithm into two stages. The former is responsible for improving coverage rate, while the latter aims to reduce the average moving distance of mobile nodes and reduce energy consumption.…”

Section: Related Workmentioning

confidence: 99%

“…They have some inapplicability based on the current scene. For example, cuckoo search algorithm(CS) [19] uses Levy flight mechanism for birds, which is a random walk consisting of short-distance flight with small step length and occasional longdistance flight with large step length. So it is easy to appear in the area around the global optimal shock phenomenon, lower algorithm efficiency, etc.…”

Section: Algorithm Parameters In Wsn Deploymentmentioning

confidence: 99%

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WSN Coverage Optimization Based on Two-Stage PSO

Fan

et al. 2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Wireless Sensor Networks (WSN) coverage perception is an important basis for communication between the cyber world and the physical world in Cyber-Physical Systems (CPS). To address the coverage redundancy, hole caused by initial random deployment and the energy constraint in redeployment, this paper proposes a multi-objective two-stage particle swarm optimization algorithm (MTPSO) based on coverage rate and moving distance deviation to improve coverage efficiency. This algorithm establishes a multi-objective optimization model for above problems, and determines the candidate deployment scheme by reducing its local convergence probability through improved inertia weight, and then introduces virtual force mechanism to adjust the relative position between nodes. This paper mainly analyzes the influence of different initial deployment category and mobile nodes proportion on multi-objective optimization performance, and gives the corresponding algorithm implement. Simulation experiments show that compared with MVFA, SPSO and OPSO algorithms, MTPSO algorithm has a better redeployment coverage performance, which fully demonstrates its effectiveness.

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“…Xiang et al proposed a cuckoo search (CS) algorithm [39] to optimize the sensor node deployment strategy in a hybrid sensor network. The candidate target positions of mobile sensor nodes are determined by the CS algorithm, and then the position optimization scheme is used to reduce the number of mobile nodes and the average moving distance.…”

Section: B Lifetime Of Wsnsmentioning

confidence: 99%

A Novel Mobile-Coverage Scheme for Hybrid Sensor Networks

Huang

Liu

et al. 2020

IEEE Access

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In this paper, a novel Mobile-Coverage scheme is proposed to improve the coverage and lifetime of hybrid sensor networks, and thus the target region can be completely covered with minimum number of mobile sensor nodes and the energy consumption can be reduced in hybrid sensor networks at the same time. Our scheme is applied to a hybrid network comprising of both static and mobile sensor nodes, which are randomly deployed in a rectangle area. We derived the minimum number of sensor nodes as well as the mobile nodes required to cover the target region completely. Compared to the existing works, the minimum number of mobile sensor nodes required in this paper is significantly smaller, which implies that the movement distance and energy consumption of mobile nodes in our scheme is reduced. Moreover, we proved that the construction of our scheme based on the Virtual Force and Voronoi Graph (VFVG) is correct, and then derived the optimal trade-off between the coverage of the target region and energy consumption of mobile nodes. Our scheme can achieve the maximum coverage which is 98.7% by minimum number of mobile sensor nodes. Meanwhile, a connectivity rate of 76.84% can also be achieved by our connectivity evaluation method. Finally, a new topology construction algorithm-Topology Construction and Maintenance based on Trust (TCMT) is created to further increase the lifetime of the sensor network. We also evaluated the performance of our scheme in terms of coverage and lifetime, and carried out extensive experiments to compare the coverage and energy consumption in our scheme and several previous schemes. The experimental results showed that our scheme is more efficient than the previous schemes simulated. INDEX TERMS Topology construction and maintain, Minimum numbers of mobile sensor, Voronoi graph, Virtual force, TCMT.

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Hybrid WSN Node Deployment optimization Strategy Based on CS Algorithm

Cited by 18 publications

References 10 publications

Hybrid Niche Immune Genetic Algorithm for Fault Detection Coverage in Industry Wireless Sensor Network

Hybrid Niche Immune Genetic Algorithm for Fault Detection Coverage in Industry Wireless Sensor Network

WSN Coverage Optimization Based on Two-Stage PSO

A Novel Mobile-Coverage Scheme for Hybrid Sensor Networks

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