Coverage enhancing of 3D underwater sensor networks based on improved fruit fly optimization algorithm

Zhang, Ying; Wang, Mingxing; Liang, Junbin; Zhang, Haiyang; Chen, Wei; Jiang, Shengming

doi:10.1007/s00500-017-2667-7

Cited by 20 publications

(11 citation statements)

References 17 publications

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“…Wang et al 16 proposed a distributed hybrid fish swarm optimization algorithm based on the impact of water flow and the activity of an artificial fish swarm system to increase the coverage efficacy of event collection and prevent blind sensor node movements. Zhang et al 17 also proposed a new optimal coverage enhancing deployment approach for 3D underwater sensor networks based on an improved fruit fly optimization algorithm, which aimed to determine the optimization strategy with the fewest iterations and the most significant objective function. However, even if the algorithm has the power to escape from the local optimum, it also may collapse into it.…”

Section: Related Workmentioning

confidence: 99%

Hybrid multi-objective node deployment for energy-coverage problem in mobile underwater wireless sensor networks

Fattah

Ahmedy

Idris

et al. 2022

International Journal of Distributed Sensor Networks

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Underwater wireless sensor networks have grown considerably in recent years and now contribute substantially to ocean surveillance applications, marine monitoring and target detection. However, the existing deployment solutions struggle to address the deployment of mobile underwater sensor nodes as a stochastic system. The system faces internal and external environment problems that must be addressed for maximum coverage in the deployment region while minimizing energy consumption. In addition, the existing traditional approaches have limitations of improving simultaneously the objective function of network coverage and the dissipated energy in mobility, sensing and redundant coverage. The proposed solution introduced a hybrid adaptive multi-parent crossover genetic algorithm and fuzzy dominance-based decomposition approach by adapting the original non-dominated sorting genetic algorithm II. This study evaluated the solution to substantiate its efficacy, particularly regarding the nodes’ coverage rate, energy consumption and the system’s Pareto optimal metrics and execution time. The results and comparative analysis indicate that the Multi-Objective Optimisation Genetic Algorithm based on Adaptive Multi-Parent Crossover and Fuzzy Dominance (MOGA-AMPazy) is a better solution to the multi-objective sensor node deployment problem, outperforming the non-dominated sorting genetic algorithm II, SPEA2 and MOEA/D algorithms. Moreover, MOGA-AMPazy ensures maximum global convergence and has less computational complexity. Ultimately, the proposed solution enables the decision-maker or mission planners to monitor effectively the region of interest.

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Section: Related Workmentioning

confidence: 99%

Hybrid multi-objective node deployment for energy-coverage problem in mobile underwater wireless sensor networks

Fattah

Ahmedy

Idris

et al. 2022

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

show abstract

“…The network interface chip AX88180 reset [ 3 ] adopts the combination of power on automatic reset and manual reset, and the frequency of external crystal oscillation circuit is 12 MHz. P1 port line, INT0 and VSE are used for network data acquisition.…”

Section: Hardware Designmentioning

confidence: 99%

Virtual Force Coverage Control System of Wireless Sensor Network in the Background of Big Data

Guo

2020

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

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In view of the low virtual force coverage of traditional wireless sensor networks. A virtual force coverage control system based on wireless sensor network is designed. Hardware design mainly includes network interface, processor, control chip and network coordinator. In the software part of the system, firstly, the virtual force coverage control node is selected. On this basis. The optimal control of virtual force and coverage of wireless sensor networks. The virtual force coverage control system of wireless sensor network is designed under the background of big data. The experimental results show that. Under the background of big data, the coverage of virtual force control system of wireless sensor network is higher than that of traditional system. It has a certain practical significance.

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“…The method can maximize the coverage and protection of high-value assets in military applications. In [24], the authors proposed an improved fruit fly optimization algorithm to solve the 3D underwater sensor network coverage optimization problem and designed a 3D space-based network coverage method. The algorithm uses the behavior of fruit flies' preying to optimize global optimal monitoring.…”

Section: Introductionmentioning

confidence: 99%

A Chaotic Parallel Artificial Fish Swarm Algorithm for Water Quality Monitoring Sensor Networks 3D Coverage Optimization

Zhou

Liu

2021

Journal of Sensors

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In recent years, the increasingly severe water pollution problem encouraged researchers to optimize water quality monitoring sensor networks (WQMSNs) by creating new underwater sensor coverage algorithms. Since the sensor is limited by the monitoring range and the number of targets, optimizing the 3D target coverage of heterogeneous multisensors is essential to maximize the 3D target coverage rate of the monitored waters. To enhance the target coverage rate, the target allocation needs to be searched in all possible combinations. To optimize the 3D coverage of underwater targets, this research proposes a chaotic parallel artificial fish swarm algorithm (CPAFSA). CPAFSA uses chaotic selection to initialize parameters and integrates the global search capabilities of parallel operators. It also applies the elite selection which effectively avoiding local optimization and solving the problem of 3D target coverage. Ultimately, CPAFSA is compared with genetic algorithm (GA) and particle swarm optimization (PSO). The results of the simulation experiment demonstrated the excellent performance of CPAFSA in achieving underwater 3D target coverage.

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Coverage enhancing of 3D underwater sensor networks based on improved fruit fly optimization algorithm

Cited by 20 publications

References 17 publications

Hybrid multi-objective node deployment for energy-coverage problem in mobile underwater wireless sensor networks

Hybrid multi-objective node deployment for energy-coverage problem in mobile underwater wireless sensor networks

Virtual Force Coverage Control System of Wireless Sensor Network in the Background of Big Data

A Chaotic Parallel Artificial Fish Swarm Algorithm for Water Quality Monitoring Sensor Networks 3D Coverage Optimization

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