Differential Evolution Based Regional Coverage-Enhancing Algorithm for Directional 3D Wireless Sensor Networks

Zhang, Lei; Liu, Tingting; Wen, Fangqing; Hu, Lin; Hei, Chuang; Wang, Ke

doi:10.1109/access.2019.2927805

Cited by 16 publications

(15 citation statements)

References 36 publications

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“…We can see that w (t) is an adaptive manner in equation (13). In the early evolution, w (t) is relatively larger and the difference of particles increases.…”

Section: Adaptive Particle Swarm Optimization (Apso)mentioning

confidence: 82%

“…step.3 Calculate the fitness value of each individual through equation (10). step.4 Update the particles according to equation (13) and equation (12). step.5 Determine whether the maximum number of iterations is reached.…”

Section: A Node Location Optimizationmentioning

confidence: 99%

“…Generally speaking, coverage control in WMSNs mainly refers to coverage model and coverage control method [13], [14]. The essence of coverage model is to establish the mathematical model for simulating the actual perception of sensors.…”

Section: Introductionmentioning

confidence: 99%

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Coverage Control Algorithm-Based Adaptive Particle Swarm Optimization and Node Sleeping in Wireless Multimedia Sensor Networks

Jiao

Zhang

et al. 2019

IEEE Access

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Wireless multimedia sensor networks (WMSNs) are widely used in various fields where coverage control is a critical difficulty because multiple requirements need to be considered such as service quality and energy consumption. In this paper, we focus on the issues existing in coverage model and coverage control method, and propose an adaptive particle swarm optimization algorithm for solving the coverage control problem in the WMSNs. First of all, a 3D coverage model is developed with introducing the perceptual radius of sensors, while the optimal projection area of a single sensor is deduced. Hereafter, an adaptive particle swarm optimization is suggested to optimize the location information of sensors for reducing both perceptual overlapping areas and perceptual blind areas in the monitoring area. At last, a redundant node sleeping strategy is presented to reduce the number of working sensors. Simulation results have show that we can ensure better coverage as well as fewer sensors compared to the state-of-the-art frameworks.

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“…We can see that w (t) is an adaptive manner in equation (13). In the early evolution, w (t) is relatively larger and the difference of particles increases.…”

Section: Adaptive Particle Swarm Optimization (Apso)mentioning

confidence: 82%

Section: A Node Location Optimizationmentioning

confidence: 99%

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Coverage Control Algorithm-Based Adaptive Particle Swarm Optimization and Node Sleeping in Wireless Multimedia Sensor Networks

Jiao

Zhang

et al. 2019

IEEE Access

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“…They mainly studied the coverage of two-dimensional scenes under specific circumstances, but the number of parameters used for optimization was not large, and the scale of joint optimization was small. As in literature [9], differential evolution algorithm was adopted to solve the coverage problem of directional sensor network in three-dimensional environment. Literature [10] proposed a multi-objective optimization scheme of comprehensive three-dimensional uncertain coverage model based on fuzzy ring concept.…”

Section: Related Workmentioning

confidence: 99%

Joint Optimization Strategy of Coverage Planning and Energy Scheduling for Wireless Rechargeable Sensor Networks

Gong¹,

Guo²,

Xu³

et al. 2020

Preprint

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Wireless Sensor Networks (WSNs) has the characteristics of large-scale deployment, flexible networking, and wide application. It is an important part of the wireless communication networks. However, due to limited energy supply, the development of WSN is greatly restricted. Wireless Rechargeable Sensor Networks (WRSNs) transform the distributed energy around the environment into usable electricity through energy collection technology. In this work, a joint optimization strategy is proposed to improve the energy management efficiency for WRSNs. The joint optimization strategy is divided into two phases. In the first phase, we design an Annulus Virtual Force based Particle Swarm Optimization (AVFPSO) algorithm for area coverage planing. It adopts the multi-parameter joint optimization method to improve the efficiency of the algorithm. In the second phase, a Queuing Game-based Energy Supply (QGES) algorithm is designed for energy scheduling. It converts energy supply and consumption into network service. By solving the game equilibrium of the model, the optimal energy distribution strategy can be obtained. The simulation results show that our scheme improves the efficiency of coverage and energy, and extends the lifetime of WSN.

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“…Differential Evolution (DE) has been well accepted as a easily-used but effective method in the family of Evolutionary Algorithm (EA) [32]. Over the past two decades, many variants [33]- [41] of DE have been developed to solve various engineering problems, such as optimal power flow [42], parameters identification [43], feature selection [44], railway line planning [45], wireless sensor network [46], optimal location of battery swapping stations [47], mild depressive detection [48], path planning of mobile robots [49] and DC motor controller [50]. For DE algorithm, it is crucial to generate a new mutant vector for the final performance of the designed DE.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Economic Scheduling of a Shipboard Microgrid Based on Self-Adaptive Collective Intelligence DE Algorithm

et al. 2020

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With the growing demand for emission reductions and fuel efficiency improvements, alternative energy sources and energy storage technologies are becoming popular in a ship microgrid. In order to balance the two non-compatible objectives, a new differential evolution variant, which is named as SaCIDE-r, was proposed to solve the optimization problem. In this algorithm, a Collective Intelligence (CI) based mutation operator was proposed by mixing some promising donor vectors in the current population. Besides, a self-adaptive mechanism which was developed to avoid introducing extra control parameters. Further, to avoid being trapped in local optima, a re-initialization mechanism was developed. Then, we have evaluated the performances of the proposed SaCIDE-r approach by studying some numerical optimization problems of Congress on Evolutionary Computation (CEC) 2013 with D = 30, compared with seven stateof-the-art DE algorithms. Moreover, the proposed SaCIDE-r method was applied for economic scheduling of a shipboard microgrid under different cases compared with other multi-objective optimizing methods, resulting in very competitive performances. The comprehensive experimental results have demonstrated that the presented SaCIDE-r method might be a feasible solution for such a kind of optimization problem. INDEX TERMS Shipboard microgrid, global optimization, collective intelligence (CI), differential evolution (DE).

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Differential Evolution Based Regional Coverage-Enhancing Algorithm for Directional 3D Wireless Sensor Networks

Cited by 16 publications

References 36 publications

Coverage Control Algorithm-Based Adaptive Particle Swarm Optimization and Node Sleeping in Wireless Multimedia Sensor Networks

Coverage Control Algorithm-Based Adaptive Particle Swarm Optimization and Node Sleeping in Wireless Multimedia Sensor Networks

Joint Optimization Strategy of Coverage Planning and Energy Scheduling for Wireless Rechargeable Sensor Networks

Multi-Objective Economic Scheduling of a Shipboard Microgrid Based on Self-Adaptive Collective Intelligence DE Algorithm

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