Benefits of using particle swarm optimization and Voronoi diagram for coverage in wireless sensor networks

Kumar, Manish; Gupta, Vrinda

doi:10.1109/icetcct.2017.8280300

Cited by 11 publications

(10 citation statements)

References 11 publications

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“…Merging Voronoi-based approaches with other nature-inspired algorithms such as particle swarm optimization (PSO) [52,[56][57][58][59][60][61][62], genetic algorithms (GAs) [63][64][65][66][67][68][69][70][71][72][73][74][75][76][77] and ant colony optimization (ACO) [78][79][80][81] is not uncommon (cf. Table 3).…”

Section: Motivation Objectives and Goalsmentioning

confidence: 99%

“…Table 3). There are many examples where this has been shown to potentially provide stability in the network with coverage optimization, distributed sensor nodes more uniformly, and ultimately, enhance the lifetime of the network [58,59].…”

Section: Motivation Objectives and Goalsmentioning

confidence: 99%

“…Merging PSO with Voronoi tessellations is considered among the major processes of maximizing the coverage and prolonging the lifetime of WSN [52,53,59,61,66]. The authors in [53,61] used PSO to determine the next position of the sensor nodes, while Voronoi tessellations are generated to detect the coverage holes and evaluate the fitness function.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

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A Hybrid Voronoi Tessellation/Genetic Algorithm Approach for the Deployment of Drone-Based Nodes of a Self-Organizing Wireless Sensor Network (WSN) in Unknown and GPS Denied Environments

Eledlebi

Hildmann

Ruta

et al. 2020

Drones

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Using autonomously operating mobile sensor nodes to form adaptive wireless sensor networks has great potential for monitoring applications in the real world. Especially in, e.g., disaster response scenarios—that is, when the environment is potentially unsafe and unknown—drones can offer fast access and provide crucial intelligence to rescue forces due the fact that they—unlike humans—are expendable and can operate in 3D space, often allowing them to ignore rubble and blocked passages. Among the practical issues faced are the optimizing of device–device communication, the deployment process and the limited power supply for the devices and the hardware they carry. To address these challenges a host of literature is available, proposing, e.g., the use of nature-inspired approaches. In this field, our own work (bio-inspired self-organizing network, BISON, which uses Voronoi tessellations) achieved promising results. In our previous approach the wireless sensors network (WSN) nodes were using knowledge about their coverage areas center of gravity, something which a drone would not automatically know. To address this, we augment BISON with a genetic algorithm (GA), which has the benefit of further improving network deployment time and overall coverage. Our evaluations show, unsurprisingly, an increase in energy cost. Two variations of our proposed GA-BISON deployment strategies are presented and compared, along with the impact of the GA. Counter-intuitively, performance and robustness increase in the presence of noise.

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Section: Motivation Objectives and Goalsmentioning

confidence: 99%

Section: Motivation Objectives and Goalsmentioning

confidence: 99%

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

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A Hybrid Voronoi Tessellation/Genetic Algorithm Approach for the Deployment of Drone-Based Nodes of a Self-Organizing Wireless Sensor Network (WSN) in Unknown and GPS Denied Environments

Eledlebi

Hildmann

Ruta

et al. 2020

Drones

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show abstract

“…The PCD value drops from 0.91 quickly and finally decrease to 0, which means that the VFA-SF has very good convergence rate and the final distribution should be a perfect hexagon structure. In order to visually compare the difference between the final deployment and the perfect hexagon topology, the Voronoi diagram [19,20] and the Delaunay triangulation [21,22] for the final network configuration were employed, which are illustrated in Figure 2d. The green lines clear show the triangular tessellation generated by Delaunay triangulation, and the blue lines generated by Voronoi diagram present the network structure.…”

Section: Simulations and Statistical Analysis For Vfa-sfmentioning

confidence: 99%

An Optimized Node Deployment Solution Based on a Virtual Spring Force Algorithm for Wireless Sensor Network Applications

Deng

Tang

et al. 2019

Sensors

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How to effectively deploy all wireless sensors and save a system’s energy consumption is a key issue in current wireless sensor network (WSN) applications. Theoretical analysis has proven that a hexagonal structure is the best topology in the two-dimensional network, which can provide the maximum coverage area with the minimum number of sensor nodes and minimum energy consumption. Recently, many scientists presented their self-deployment strategies based on different virtual forces and discussed the corresponding efficiency via several case studies. However, according to our statistical analysis, some virtual force algorithms, e.g., virtual spring force, can still cause holes or twisted structure in a small region of the final network distribution, which cannot achieve the ideal network topology and will waste the system energy in real applications. In this paper, we first statistically analyzed the convergence and deployment effect of the virtual spring force algorithm to derive our question. Then we presented an optimized strategy that sensor deployment begins from the center of the target region by adding an external central force. At the early stage, the external force will be added to the most peripheral nodes to promote the formation of hexagonal topology and avoid covering holes or unusual structure. Finally, a series of independent simulation experiments and corresponding statistical results proved that our optimized deployment solution is very stable and effective, which can improve the energy consumption of the whole sensor network and be used in the application of a large scale WSN.

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“…Therefore, many researchers have studied this and achieved rich results. [2][3][4][5] In general, there are two types of network coverage optimization methods. Firstly, the coverage optimization based on geometric methods.…”

Section: Introductionmentioning

confidence: 99%

Node coverage optimization algorithm for wireless sensor networks based on improved grey wolf optimizer

Wang

Xie

et al. 2019

Journal of Algorithms & Computational Technology

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Aiming at the problem of wireless sensor network node coverage optimization with obstacles in the monitoring area, based on the grey wolf optimizer algorithm, this paper proposes an improved grey wolf optimizer (IGWO) algorithm to improve the shortcomings of slow convergence, low search precision, and easy to fall into local optimum. Firstly, the nonlinear convergence factor is designed to balance the relationship between global search and local search. The elite strategy is introduced to protect the excellent individuals from being destroyed as the iteration proceeds. The original weighting strategy is improved, so that the leading wolf can guide the remaining grey wolves to prey in a more reasonable way. The design of the grey wolf's boundary position strategy and the introduction of dynamic variation strategy enrich the population diversity and enhance the ability of the algorithm to jump out of local optimum. Then, the benchmark function is used to test the convergence performance of genetic algorithm, particle swarm optimization, grey wolf optimizer, and IGWO algorithm, which proves that the convergence performance of IGWO algorithm is better than the other three algorithms. Finally, the IGWO algorithm is applied to the deployment of wireless sensor networks with obstacles (rectangular obstacle, trapezoidal obstacle and triangular obstacles). Simulation results show that compared with GWO algorithm, IGWO algorithm can effectively improve the coverage of wireless sensor network nodes and obtain higher coverage rate with fewer nodes, thereby reducing the cost of deploying the network.

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Benefits of using particle swarm optimization and Voronoi diagram for coverage in wireless sensor networks

Cited by 11 publications

References 11 publications

A Hybrid Voronoi Tessellation/Genetic Algorithm Approach for the Deployment of Drone-Based Nodes of a Self-Organizing Wireless Sensor Network (WSN) in Unknown and GPS Denied Environments

A Hybrid Voronoi Tessellation/Genetic Algorithm Approach for the Deployment of Drone-Based Nodes of a Self-Organizing Wireless Sensor Network (WSN) in Unknown and GPS Denied Environments

An Optimized Node Deployment Solution Based on a Virtual Spring Force Algorithm for Wireless Sensor Network Applications

Node coverage optimization algorithm for wireless sensor networks based on improved grey wolf optimizer

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