Differential evolution algorithm applied to wireless sensor distribution on different geometric shapes with area and energy optimization

Céspedes-Mota, Armando; Castañón, Gerardo; Martínez-Herrera, Alberto F.; Cárdenas–Barrón, Leopoldo Eduardo; Sarmiento, Aguasanta Miguel

doi:10.1016/j.jnca.2018.06.006

Cited by 24 publications

(18 citation statements)

References 25 publications

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“…et al in [25], suggested that the DE algorithm was presented for the situation in which the communication range value was unknown to deal with the optimization problem. Cespedes-Mota et al in [26] utilised the multiobjective DE algorithm to improve the sensor distribution over the multiple areas within the geolocation and to expand the coverage area and to minimize the network energy consumption at the same time. The PSO algorithm is a nature inspired algorithm by the social behavior of birds, has been one of the most popular optimization algorithms that is widely applied to solve complex optimization problems in WSNs.…”

Section: Literature Reviewmentioning

confidence: 99%

An Innovative Hyperheuristic, Gaussian Clustering Scheme for Energy‐Efficient Optimization in Wireless Sensor Networks

2021

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Energy stability on sensor nodes in wireless sensor networks (WSNs) is always an important challenge, especially during data capturing and transmission of packets. The recent advancement in distributed clustering algorithms in the extant literature proposed for energy efficiency showed refinements in deployment of sensor nodes, network duration stability, and throughput of information data that are channelled to the base station. However, much scope still exists for energy improvements in a heterogeneous WSN environment. This research study uses the Gaussian elimination method merged with distributed energy efficient clustering (referred to as DEEC-Gauss) to ensure energy efficient optimization in the wireless environment. The rationale behind the use of the novel DEEC-Gauss clustering algorithm is that it fills the gap in the literature as researchers have not been able to use this scheme before to carry out energy-efficient optimization in WSNs with 100 nodes, between 1,000 and 5000 rounds and still achieve a fast time output. In this study, using simulation, the performance of highly developed clustering algorithms, namely, DEEC, EDEEC_E, and DDEEC, was compared to the proposed Gaussian Elimination Clustering Algorithm (DEEC-Gauss). The results show that the proposed DEEC-Gauss Algorithm gives an average percentage of 4.2% improvement for the first node dead (FND), a further 2.8% improvement for the tenth node dead (TND), and the overall time of delivery was increased and optimized when compared with other contemporary algorithms.

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Section: Literature Reviewmentioning

confidence: 99%

An Innovative Hyperheuristic, Gaussian Clustering Scheme for Energy‐Efficient Optimization in Wireless Sensor Networks

2021

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“…Aimed at guaranteeing the task completion instantaneously and extending the life-cycle of network, the authors in [30] presented an energy-efficiency node scheduling algorithm based on game theory for WSNs, and the payoff function includes both the residual energy and local task load of the sensor nodes. A Hungarian algorithm (HA) [31] is used to solve the NP-hard problem of deterministic coverage enhancement with small time complexity [32], that is, the shortest moving scheme between the initial position of each sensor node and the position to be deployed is determined by the maximum matching algorithm of bipartite graph [33,34]. The above algorithms based on task assignment all ignore the optimization of reducing the maximum energy cost of sensors and balancing the residual energy, which are exactly the keys that affect the life-cycle of WSNs.…”

Section: Related Workmentioning

confidence: 99%

An Energy-Efficient Coverage Enhancement Strategy for Wireless Sensor Networks Based on a Dynamic Partition Algorithm for Cellular Grids and an Improved Vampire Bat Optimizer

Zhao

Cui

Guo

et al. 2020

Sensors

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Sensor nodes perform missions based on the effectual invariable coverage of events, and it is commonly guaranteed by the determinate deployment for sensor nodes who deviate from the optimum site frequently. To reach the optimal coverage effect with the lowest costs is a primary goal of wireless sensor networks. In this paper, by splicing the sensing area optimally with cellular grids, the best deployment location for sensors and the required minimum number of them are revealed. The optimization problem of coverage rate and energy consumption is converted into a task assignment problem, and a dynamic partition algorithm for cellular grids is also proposed to improve the coverage effect when the number of sensors is variable. Furthermore, on the basis of solving the multi-objective problem of reducing and balancing the energy cost of sensors, the vampire bat optimizer is improved by introducing virtual bats and virtual preys, and finally solves the asymmetric assignment problem once the number of cellular grids is not equal to that of sensors. Simulation results indicate that the residual energy of sensors during redeployment is balanced notably by our strategy when compared to three other popular coverage-enhancement algorithms. Additionally, the total energy cost of sensor nodes and coverage rate can be optimized, and it also has a superior robustness when the number of nodes changes.

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“…However, NSGAII has a disadvantage that the distribution of non-dominated solutions is uneven, especially on high-dimensional optimization problems. In [14], the multi-objective differential evolution algorithm (MODEA) is applied to the WSN deployment of the geometric polygon monitoring area, the coverage rate and energy consumption rate of WSN are taken as optimization objectives. A good solution is provided in [14], but the paper does not consider the energy consumption due to data transmission and reception.…”

Section: Related Workmentioning

confidence: 99%

“…In [14], the multi-objective differential evolution algorithm (MODEA) is applied to the WSN deployment of the geometric polygon monitoring area, the coverage rate and energy consumption rate of WSN are taken as optimization objectives. A good solution is provided in [14], but the paper does not consider the energy consumption due to data transmission and reception. The works of [15]- [16] optimize the network coverage, energy consumption rate and energy balance rate, using different approaches.…”

Section: Related Workmentioning

confidence: 99%

Wireless Sensor Network Deployment Optimization Based on Two Flower Pollination Algorithms

Wang

Xie

et al. 2019

IEEE Access

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For the wireless sensor networks (WSNs) heterogeneous node deployment optimization problem with obstacles in the monitoring area, two new flower pollination algorithms (FPA) are proposed to deploy the network. Firstly, an improved flower pollination algorithm (IFPA) is proposed based on FPA, aiming at the shortcomings of the convergence speed is slow and the precision is not high enough of FPA. The nonlinear convergence factor is designed to correct the scaling factor of FPA, the Tent chaotic map effectively maintains the diversity of the population in the late iteration, and a greedy crossover strategy is designed to assist the remaining individual search with better individuals. Secondly, based on FPA, a non-dominated sorting multi-objective flower pollination algorithm (NSMOFPA) is proposed. The external archive strategy and leader strategy are introduced, to solve the global pollination problem. The proposed crowding degree method and the introduced elite strategy effectively maintain the diversity of the population. Then, IFPA is applied to WSN deployment aiming at optimizing coverage rate, simulation experiments show that IFPA can obtain a higher coverage rate with shorter iterations, which can save network deployment costs. Finally, applying NSMOFPA to the WSN deployment with optimization objectives for coverage rate, node radiation overflow rate and energy consumption rate. The experimental results verify that NSMOFPA has a good optimization effect and can provide a better solution for WSN deployment.INDEX TERMS Deployment optimization, improved flower pollination algorithm, non-dominated sorting, multi-objective flower pollination algorithm, wireless sensor networks.

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Differential evolution algorithm applied to wireless sensor distribution on different geometric shapes with area and energy optimization

Cited by 24 publications

References 25 publications

An Innovative Hyperheuristic, Gaussian Clustering Scheme for Energy‐Efficient Optimization in Wireless Sensor Networks

An Innovative Hyperheuristic, Gaussian Clustering Scheme for Energy‐Efficient Optimization in Wireless Sensor Networks

An Energy-Efficient Coverage Enhancement Strategy for Wireless Sensor Networks Based on a Dynamic Partition Algorithm for Cellular Grids and an Improved Vampire Bat Optimizer

Wireless Sensor Network Deployment Optimization Based on Two Flower Pollination Algorithms

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