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Enhancing Whale optimization Algorithm with Levy Flight for coverage optimization in wireless sensor networks

COMPUTERS & ELECTRICAL ENGINEERING 2021年 94卷 107359-107359页

作者： Deepa, R. Venkataraman, Revathi SRM Inst Sci & Technol Dept Comp Sci & Engn Kattankulathur Tamil Nadu India

coverage optimization is one of the most essential pre-requisites in Wireless Sensor Networks (WSNs) which plays a significant and impactful role in the field of environmental monitoring, surveillance, socio-economic Cyber-networkings, etc. The Whale optimization Algorithm (WOA) is a swarm intelligence based Search-Algorithm while browsing for an optimal solution, but, it suffers from the poor & inconsistent exploration problem and that causes trapping of local optima in randomly deployed nodes that fail to guarantee network coverage. To resolve the issue, an innovative study has been researched which presents an embedded coverage optimization WSN and is based on Levy Flight mechanism with WOA (LWOA) .This updates the current search of location for positioning the sensors in the field. This mechanism can enhance and balance the exploration ability of WOA, which allows trapping of the local optima. This thichnically enhanced and updated proposal LWOA is validated by 25 benchmark optimization functions and is compared with existing Particle Swarm optimization and WOA. From the experimental results, it can be construed and proved that the performance of Levy WOA (LWOA) has significantly improved the global search capacity and increase the efficiency of convergence, which immensely enhances the efficacy of coverage of nodes inturn amplifying the overall performance of the network. Finally, by using the K-Nearest Neighbour KNN(centroid) approach nearly 33% of nodes were optimized.

关键词： coverage optimization Whale optimization algorithm Levy Flights K-Nearest Neighbor (centroid)

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A coverage optimization Method for WSNs Based on the Improved Weed Algorithm

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SENSORS 2021年第17期21卷 5869页

作者： Zhu, Fang Wang, Wenhao Northeastern Univ Qinhuangdao Sch Comp & Commun Engn Qinhuangdao 066004 Hebei Peoples R China

Wireless sensor networks (WSNs) is a multi-hop wireless network composed of a group of static or mobile sensor nodes in the form of self-organization. Uneven distribution of nodes often leads to the problem of over coverage and incomplete coverage of monitoring areas. To solve this problem, this paper establishes a network coverage optimization model and proposes a coverage optimization method based on an improved hybrid strategy weed algorithm (LRDE_IWO). The improvement of the weed algorithm includes three steps. Firstly, the standard deviation of normal distribution based on the tangent function is used as the seed's new step size in the seed diffusion stage to balance the ability of the global search and local search of weed algorithm. Secondly, to avoid the problem of premature convergence, a disturbance mechanism combining enhanced Levy flight and the adaptive random walk strategy is proposed in the process of seed breeding. Finally, in competition of invasive weed stage, the differential evolution strategy is introduced to optimize the competition operation process and speed up convergence. The improved weed algorithm is applied to coverage optimization of WSNs. The simulation results show that the coverage rate of LRDE_IWO is increased by about 1% to 6% compared with the original invade weed algorithm (IWO) and the differential evolution invasive weed optimization algorithm (DE_IWO), and the coverage rate of the LRDE_IWO algorithm is increased by 4.10%, 2.73% and 1.19%, respectively, compared with the antlion optimization algorithm (ALO), the fruit fly optimization algorithm (FOA) and the gauss mutation weed algorithm (IIWO). The results prove the superiority and validity of the improved weed algorithm for coverage optimization of wireless sensor networks.

关键词： wireless sensor networks coverage optimization Levy flight random walk differential evolution

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The coverage optimization Method for Underwater Sensor Network Based on VF-PSO Algorithm 32

The Coverage Optimization Method for Underwater Sensor Netwo...

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32nd Chinese Control And Decision Conference (CCDC)

作者： Sun, Yujiao Hu, Yifan Chen, Lu Liu, Hailin Chen, Jie Lv, Bin Qilu Univ Technol Inst Oceanog Instrumentat Shandong Acad Sci Qingdao Peoples R China Shandong Prov Key Lab Ocean Environm Monitoring T Qingdao Peoples R China Natl Engn & Technol Res Ctr Marine Monitoring Equ Qingdao Peoples R China

ISBN: (纸本)9781728158556

coverage is an important indicator of the quality of underwater sensor network (UWSN) service. It reflects the monitoring capability of the network nodes to the UWSN deployment area and determines the overall lifetime of the UWSN. In order to improve network coverage and prolong network lifetime, this paper proposed a combination algorithm of virtual three and particle swarm optimization (VF-PSO). By the action of the virtual force, the underwater mobile nodes are moved to a relatively ideal position to maximize the network coverage. The virtual three is used to guide the optimization of the particle swarm so as to speed up the convergence of the particles to the global optimal solution. This algorithm not only effectively solves the problem of insufficient optimization ability of the virtual force algorithm, but also makes the particle swarm algorithm evolve towards the target solution that maximizes the area coverage. Simulation results show that the VF-PSO algorithm can effectively improve the effective coverage of underwater monitoring area, and avoid void-hole problem, while ensuring network connectivity compared with the algorithm proposed earlier.

关键词： Underwater sensor network VF-PSO algorithm coverage optimization Void hole

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Adaptive Antenna Tilt for Cellular coverage optimization in Suburban Scenario

Adaptive Antenna Tilt for Cellular Coverage Optimization in ...

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International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM)

作者： Samal, Soumya Ranjan Dandanov, Nikolay Bandopadhaya, Shuvabrata Poulkov, Vladimir Tech Univ Sofia Fac Telecommun Sofia Bulgaria BML Munjal Univ Gurgaon India

ISBN: (纸本)9783030390334;9783030390327

Radio coverage optimization is a critical issue for mobile network operators (MNO) in the deployment of future generation cellular networks, especially on users at cell edge. The key factor that influences the coverage in mobile networks is mostly related to the configuration of the antennas and especially the angle of antenna tilt. The received signal power in a cell can be increased with proper antenna tilt, causing a significant improvement in signal-to-interference-plus-noise ratio (SINR) at the cell edge. This also leads to reduction in interference towards other cells. In this paper, a method for coverage optimization using base station antenna electrical tilt in mobile networks for suburban scenario is proposed. The main focus is on the downlink power setting by using electrical antenna tilt in the mobile network. This proposed solution uses reinforcement learning technique and the simulation results shows that the proposed algorithm can used to improve overall network performance in terms of SINR and received signal power at the cell edge with 80-100% user satisfaction. The simulation has carried out by considering a multi-cell and three sector cellular networks in a suburban scenario where the users are randomly distributed.

关键词： Self-organizing networks (SON) coverage optimization Antenna tilt angle Reinforcement learning Cell edge users

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The coverage optimization for underwater sensor networks based on SAPSO algorithm

The coverage optimization for underwater sensor networks bas...

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第33届中国控制与决策会议

作者： Xiaobin Gai Yifan Hu Pengfei Li Institute of Oceanographic Instrumentation Qilu University of Technology (Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Ocean Environmental Monitoring Technology National Engineering and Technological Research Center of Marine Monitoring Equipment

The coverage of underwater sensor network（UWSN） is a basic problem of underwater wireless sensor network,which is related to the integrity and accuracy of underwater sensor network data collection for target monitoring *** addition,the working environment of underwater sensor nodes is often harsh and the influencing factors are complex,which is not convenient for frequent battery ***,optimizing the coverage of underwater sensor networks can effectively improve the monitoring quality and lifetime of *** at the shortcomings of previous coverage optimization algorithms,such as easy to fall into the local optimal solution,complex parameters and slow convergence speed,this paper proposes a simulated annealing particle swarm optimization algorithm（SAPSO）,which improves the particle swarm optimization algorithm（PSO） by introducing simulated annealing algorithm,so that the algorithm can maintain a faster convergence speed and overcome the shortcomings of PSO algorithm easy to fall into the local optimal solution,so as to improve the coverage of underwater sensor networks,It also makes the distribution of nodes more uniform and reduces the network energy *** results show that SAPSO algorithm can effectively improve the coverage rate of target monitoring area,reduce the redundancy of node coverage and reduce the network energy consumption.

关键词： underwater sensor network SAPSO algorithm coverage optimization energy consumption

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Data fusion based coverage optimization in heterogeneous sensor networks: A survey

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INFORMATION FUSION 2019年 52卷 90-105页

作者： Deng, Xianjun Jiang, Yalan Yang, Laurence T. Lin, Man Yi, Lingzhi Wang, Minghua USC Sch Elect Engn Hunan Prov Key Lab Ultrafast Micro Nano Technol & Hengyang Hunan Peoples R China St Francis Xavier Univ Dept Comp Sci Antigonish NS Canada

Sensor networks, as a promising network paradigm, have been widely applied in a great deal of critical real-world applications. A key challenge in sensor networks is how to improve and optimize coverage quality which is a fundamental metric to characterize how well a point or a region or a barrier can be sensed by the geographically deployed heterogeneous sensors. Because of the resource-limited, battery-powered and type-diverse features of the sensors, maintaining and optimizing coverage quality includes a significant amount of challenges in heterogeneous sensor networks. Many researchers from both academic and industrial communities have performed numerous significant works on coverage optimization problem in the past decades. Some of them also have surveyed the current models, theories and solutions on the problem of coverage optimization. However, most of the existing surveys and analytical studies ignore how to exploit data fusion and cooperation of the deployed sensors to enhance coverage performance. In this paper, we provide an insightful and comprehensive summarization and classification on the data fusion based coverage optimization problem and techniques. Aiming at overcoming the shortcomings existed in current solutions, we also discuss the future issues and challenges in this area and sketch a general research framework in the context of reinforcement learning.

关键词： Data fusion coverage optimization Heterogeneous sensor networks Survey Reinforcement learning

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Multi-species evolutionary algorithm for wireless visual sensor networks coverage optimization with changeable field of views

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APPLIED SOFT COMPUTING 2020年 96卷 106680-106680页

作者： Zhang, Daifeng Zhang, Jiliang Beihang Univ Sch Automat Sci & Elect Engn Beijing 100083 Peoples R China Shandong Univ Sci & Technol Intelligent Equipment Coll Tai An 271019 Shandong Peoples R China

The coverage optimization of wireless visual sensor networks (WVSNs) with changeable field of views (FOVs) brings more challenges on the decision making of modern cyber-physical systems as it is a multi-objective decision problem where some contradictory indices, e.g. the sensor coverage and the redundancy, should be taken into consideration concurrently. In this paper, a novel multi-species evolutionary algorithm (MSEA) is proposed to address this issue by introducing a multi-species evolution scheme to enhance the search ability. A competition mechanism based on the deductive sort and the crowding distances is developed to facilitate the generation of Pareto front and the elitist individuals are evolved from a multi-species hybrid population. Comparative results show a better balancing performance between the exploration and the exploitation of the proposed algorithm which induces a strong approximation to the feasible WVSN managements. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Wireless visual sensor network coverage optimization Multi-objective optimization Multi-species evolutionary algorithm

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A Homology Based coverage optimization Algorithm for Wireless Sensor Networks 11th

A Homology Based Coverage Optimization Algorithm for Wireles...

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11th EAI Annual International Conference on Ad Hoc Networks (ADHOCNETS)

作者： Xiang, Lei Yan, Feng Zhu, Yaping Xia, Weiwei Shen, Fei Xing, Song Wu, Yi Shen, Lianfeng Southeast Univ Natl Mobile Commun Res Lab Nanjing Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Shanghai Peoples R China Calif State Univ Los Angeles Dept Informat Syst Los Angeles CA 90032 USA Fujian Normal Univ Key Lab OptoElect Sci & Technol Med Fujian Prov Key Lab Photon Technol Minist Educ Fuzhou Peoples R China

ISBN: (纸本)9783030372620;9783030372613

Simplicial complex provides a precise and tractable representation of the topology of wireless sensor networks. In this paper, a coverage optimization algorithm based on Rips complex is given for the purpose of energy conservation of wireless sensor networks. Considering an area of interest which is covered by sensor nodes completely and even superfluously, our algorithm is performed to turn off redundant sensor nodes effectively in the network while maintaining the coverage consistently. Simulation results show that this distributed algorithm can remove more than 70% internal sensor nodes, and complexity analysis for our algorithm is given.

关键词： coverage optimization Homology Wireless sensor networks

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The coverage optimization Method for Underwater Sensor Network Based on VF-PSO Algorithm

The Coverage Optimization Method for Underwater Sensor Netwo...

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第32届中国控制与决策会议

作者： Yujiao Sun Yifan Hu Lu Chen Hailin Liu Jie Chen Bin Lv Institute of Oceanographic Instrumentation Qilu University of Technology(Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Ocean Environmental Monitoring Technology National Engineering and Technological Research Center of Marine Monitoring Equipment

coverage is an important indicator of the quality of underwater sensor network(UWSN) *** reflects the monitoring capability of the network nodes to the UWSN deployment area and determines the overall lifetime of the *** order to improve network coverage and prolong network lifetime,this paper proposed a combination algorithm of virtual force and particle swarm optimization(VF-PSO).By the action of the virtual force,the underwater mobile nodes are moved to a relatively ideal position to maximize the network *** virtual force is used to guide the optimization of the particle swarm so as to speed up the convergence of the particles to the global optimal *** algorithm not only effectively solves the problem of insufficient optimization ability of the virtual force algorithm,but also makes the particle swarm algorithm evolve towards the target solution that maximizes the area *** results show that the VF-PSO algorithm can effectively improve the effective coverage of underwater monitoring area,and avoid void-hole problem,while ensuring network connectivity compared with the algorithm proposed earlier.

关键词： Underwater sensor network VF-PSO algorithm coverage optimization Void hole

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Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization

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APPLIED SOFT COMPUTING 2018年 68卷 268-282页

作者： Xu, Ying Ding, Ou Qu, Rong Li, Keqin Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China Univ Nottingham Sch Comp Sci Nottingham NG8 1BB England SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

In Wireless Sensor Networks (WSN), maintaining a high coverage and extending the network lifetime are two conflicting crucial issues considered by real world service providers. In this paper, we consider the coverage optimization problem in WSN with three objectives to strike the balance between network lifetime and coverage. These include minimizing the energy consumption, maximizing the coverage rate and maximizing the equilibrium of energy consumption. Two improved hybrid multi-objective evolutionary algorithms, namely Hybrid-MOEA/D-I and Hybrid-MOEA/D-II, have been proposed. Based on the well-known multi-objective evolutionary algorithm based on decomposition (MOEA/D), Hybrid-MOEA/D-I hybrids a genetic algorithm and a differential evolutionary algorithm to effectively optimize subproblems of the multi-objective optimization problem in WSN. By integrating a discrete particle swarm algorithm, we further enhance solutions generated by Hybrid-MOEA/D-I in a new Hybrid-MOEA/D-II algorithm. Simulation results show that the proposed Hybrid-MOEA/D-I and Hybrid-MOEA/D-II algorithms have a significant better performance compared with existing algorithms in the literature in terms of all the objectives concerned. (C) 2018 Elsevier B.V. All rights reserved.

关键词： coverage optimization MOEA/D Multi-objective optimization Wireless sensor networks

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