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8th International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Tang, Min Yan, Feng Deng, Shuguang Shen, Lianfeng Kuang, Sufeng Xing, Song Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Jiangsu Peoples R China Hunan City Univ Coll Commun & Elect Engn Yiyang 413000 Peoples R China Calif State Univ Los Angeles Dept Informat Syst Los Angeles CA 90032 USA

ISBN: (纸本)9781509028603

In this paper, two coverage optimization algorithms based on Voronoi diagram are proposed to reduce the energy consumption of software-defined sensor networks (SDSNs). In the first algorithm named Minimax Radius Algorithm (MRA), we try to reduce the sensing radii of nodes as much as possible while preserving the original network coverage. In the second algorithm named Sleeping-based Algorithm (SLA), we try to set as many nodes as possible into sleep mode without generating new coverage holes. Simulation results show that both algorithms can reduce the energy consumption significantly and the energy reduction rate increases with no des' sensing radius and the number of nodes.

关键词： software-defined sensor networks Voronoi diagram coverage optimization

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coverage optimization for Wireless Sensor Networks by Evolutionary Algorithm 7th

Coverage Optimization for Wireless Sensor Networks by Evolut...

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7th International Symposium on Intelligence Computation and Applications (ISICA)

作者： Li, Kangshun Wen, Zhichao Li, Shen South China Agr Univ Coll Math & Informat Guangzhou 510642 Guangdong Peoples R China

ISBN: (纸本)9789811003561;9789811003554

Wireless sensor network consists of a large number of tiny sensor nodes owned capable of perception in monitoring region by self-organized wireless communication, has been widely applied in military and civil fields. From the perspective of resource-saving, under the condition of the network's connectivity and specific coverage, the number of sensor nodes is assumed to be opened as few as possible. So, computing the sensor nodes collection which meeting the requirements is called the problem of network coverage optimization for Wireless Sensor Network;also called the problem of minimum connected covering node set. The innovation point of the article is: Firstly, it analyzed the deficiencies of traditional evolution algorithm fitness function, put forward an improved fitness function design scheme, and has been proved that it has advantage of solving problem on wireless sensor networks coverage optimization;Secondly, it applied the method of control variables, comparison and analysis of the influence on the various operations and parameters selection in evolution algorithm on the optimization results and performance, and then point out how to design algorithm to manage to the best optimize effect and performance.

关键词： Wireless sensor network Evolutionary algorithm coverage optimization

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coverage optimization strategy for multilayer mobile sensor network based on artificial bee colony algorithm 2nd

Coverage optimization strategy for multilayer mobile sensor ...

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2nd International Conference on Computer, Intelligent Computing and Education Technology (CICET)

作者： Zeng, Xia Ling Jiangxi Sci & Technol Normal Univ Nanchang Peoples R China

ISBN: (纸本)9781315685892;9781138028005

For the multi-layer structure of mobile sensor network, hierarchical mechanism is proposed to apply in artificial bee colony algorithm, in order to complete the deployment optimization of sensor nodes and to improve the network coverage ratio. Firstly the monitoring area is regionally divided according to equal proportions, and one sink node is deployed in each sub-region. Based on the network coverage optimization model, the sink nodes run an artificial bee colony algorithm to obtain the optimal deployment solution of sensor nodes. Results show that based on the coverage optimization strategy, the coverage ratio gradually increases as the number of mobile nodes increases, and when the iterations of the algorithm are only 300 the network coverage has quickly increased, showing a good coverage optimization performance.

关键词： coverage optimization Artificial bee colony algorithm Hierarchical Sensor network

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Bayesian coverage optimization models

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JOURNAL OF COMBINATORIAL optimization 2010年第2期19卷 158-173页

作者： Cochran, James J. Levy, Martin S. Camm, Jeffrey D. Louisiana Tech Univ Dept Mkt & Anal Ruston LA 71272 USA Univ Cincinnati Dept Quantitat Anal & Operat Management Cincinnati OH 45221 USA

Finding a subset collection that provides optimal population coverage is a frequently encountered deterministic problem. A random sample is often used to formulate the optimal coverage (OC) model, which is then used to select the subsets that provide the estimated optimal population coverage. Such problems are ubiquitous and occur in both the public and private sectors;examples include media selection, placement of municipal services such as sirens and waste dumps, and reserve site selection. Conceptualizing sample elements as counts in a contingency table, we show how decision-makers can combine prior information with sample data to help formulate OC models. We consider conjugate and vague priors with classical and empirical Bayesian interpretations. We show that the predictive approach yields a common marketing exposure model that has previously been justified empirically. Finally, we demonstrate the potential importance of our results on problems generated from a well-known example from the literature.

关键词： coverage optimization Statistical estimation Bayesian statistics

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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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Boosting the Cellular Network coverage optimization in Accordance With the Metric Structure of Antenna Variables

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2020年第12期19卷 8303-8314页

作者： Qin, Yunhui Wei Huangfu Zhang, Haijun Long, Keping Univ Sci & Technol Beijing Beijing Adv Innovat Ctr Mat Genome Engn Beijing Engn & Technol Res Ctr Convergence Networ Inst Artificial Intelligence Beijing 100083 Peoples R China

Cellular networks are bound to connect an ever-increasing number of subscribers. The issue of securing both sufficient capacity and reliable coverage remains to be resolved. This paper introduces the maximum coverage problem in wireless cellular networks and gets insight into the metric structure of the solution space for antenna orientation variables. We construct two metric spaces in mathematical views, in which both the deterministic search method (e.g., Nelder-Mead simplex algorithm) and the stochastic search method (e.g., Genetic Algorithm) have been fully discussed without using gradient information. Accordingly, we propose the improved deterministic and stochastic search methods to boost the coverage optimization procedure. Experiments show that the proposed algorithms not only obtain the close-to-optimal solution but greatly improve the convergence speed by reason that redundant exploration is avoided in the tailored solution spaces. Metric structure, as an essential topology in the antenna orientation solution space, therefore, provides a new perspective to settle other antenna orientation-related coverage and capacity optimization problems.

关键词： Antennas optimization Search methods Quaternions Genetic algorithms coverage optimization metric space deterministic search method stochastic search method

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Grey wolf optimizer with an enhanced hierarchy and its application to the wireless sensor network coverage optimization problem

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APPLIED SOFT COMPUTING 2020年 96卷 106602-106602页

作者： Miao, Zhaoming Yuan, Xianfeng Zhou, Fengyu Qiu, Xuanjie Song, Yong Chen, Ke Shandong Univ Sch Mech Elect & Informat Engn Weihai 264209 Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China East China Univ Technol Fuzhou Teachers Coll Fuzhou 344000 Peoples R China

Grey wolf optimizer (GWO), which is inspired by the social behaviours of grey wolf packs, is a nature-inspired and population-based algorithm. The GWO technique has the advantage of conceptual simplicity and shows good results for solving various real-world problems. However, this technique has the drawback of premature convergence and is prone to stagnation in local optima. The leadership hierarchy is the paramount characteristic of the GWO and influences its searching precision. Therefore, a grey wolf optimizer with enhanced hierarchy (GWO-EH) is proposed to overcome these deficiencies. Firstly, fitness-based self-adaptive weight coefficients are introduced to better imitate the hierarchy of the grey wolves, which also have a positive effect on the convergence speed. Then, we propose an improved position-updating equation to enhance the leadership of the high-ranking wolves, whereby the global exploration ability of the GWO is strengthened. Finally, the strategy of repositioning wolves around the leading wolves is designed to keep a perfect balance between exploration and exploitation. The search ability of the GWO-EH is thoroughly compared with the GWO itself, some promising GWO variants, and several well-established algorithms on twenty-three widely used benchmark functions. Empirical studies reveal that GWO-EH has a competitive overall performance according to the average value (standard deviation), Wilcoxon rank-sum test results, and convergence curve. Moreover, our method is applied to address the wireless sensor network coverage optimization problem, and the applicability and validity of the GWO-EH are indicated by the experimental results. (C) 2020 Elsevier B.V. All rights reserved.

关键词： GWO Enhanced hierarchy Swarm intelligence Numerical optimization coverage optimization

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A Collaborative Planning Method of Space-Ground Sensor Network coverage optimization for Multiparameter Observation Tasks

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IEEE SENSORS JOURNAL 2021年第6期21卷 8384-8399页

作者： Wang, Ke Wu, Qianqian Peng, Yuling Hu, Chuli Chen, Nengcheng China Univ Geosci Sch Geog & Informat Engn Wuhan 430074 Peoples R China Wuhan Inst Technol Sch Civil Engn & Architecture Wuhan 430073 Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China

A multiparameter observation task includes a comprehensive theme with multiple indispensable parameters that need to be monitored simultaneously. However, with limited observation sensor resources for a multiparameter observation task, the spatial misalignment of the coverage area of each parameter decreases the observation efficiency, especially in a space-ground sensor network. To solve this problem, we developed a collaborative planning method in the sensor planning phase. With this method, a space-ground maximal coverage model with multiple parameters (SGMC-MP) was introduced. The proposed collaborative planning method cooperatively utilizes the space-ground sensors to make an observation plan. This method aims to maximize the overlay coverage range among the parameters in the task to reduce the spatial misalignment and improve the utilization of the sensors. The proposed method was applied to a multiparameter observation task in the Three Gorges Reservoir Area in Chongqing. The results indicate that the proposed method exhibits better coverage performance for sensor planning in the multiparameter observation task than the traditional separate planning method. In addition, planning strategies, coverage flexibility, model extension, and algorithm comparison are further discussed.

关键词： Sensors Task analysis Planning Satellites Collaboration Image sensors optimization coverage optimization collaborative planning multiparameter observation task satellite image collection planning sensor deployment

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Game Theory and coverage optimization Based Multihop Routing Protocol for Network Lifetime in Wireless Sensor Networks

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IEEE SENSORS JOURNAL 2022年第13期22卷 13739-13752页

作者： Yao, Yin-Di Li, Xiong Cui, Yan-Peng Deng, Lang Wang, Chen Xian Univ Posts & Telecommun Sch Commun & Informat Xian 710121 Peoples R China Shaanxi Key Lab Informat Commun Network & Secur Xian 710121 Peoples R China Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100876 Peoples R China

Wireless sensor networks (WSNs) are self-organizing monitoring networks with a large number of randomly deployed micro sensor nodes to collect various physical information so as to realize tasks such as intelligent perception, efficient control and decision-making. However, WSNs nodes are powered by batteries, so they will run out of energy after a certain time. This energy limitation will greatly constrain the network performance like network lifetime and energy efficiency. In this study, for the purpose of prolonging network lifetime, we proposed a multi-hop routing protocol based on game theory and coverage optimization (MRP-GTCO). Briefly, in the stage of setup, two innovational strategies including clustering game with penalty function and cluster head coverage set were designed to realize the uniformity of cluster head distribution and improve the rationality of cluster head election. In the data transmission stage, we firstly derived the applicable conditions theorem of inter-cluster multi-hop routing. Based on this, a novel multi-hop path selection algorithm related to residual energy and node degree was proposed to provide an energy-efficient data transmission path. The simulation results showed that MRP-GTCO protocol can effectively reduce the network energy consumption and extend the network lifetime 159.22%, 50.76% and 16.46% compared with LGCA, RLEACH and ECAGT protocols.

关键词： Sensors Routing protocols Energy consumption Wireless sensor networks Games optimization Task analysis coverage optimization game theory network lifetime routing protocol wireless sensor networks

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Rethinking Cellular System coverage optimization: A Perspective of Pseudometric Structure of Antenna Azimuth Variable Space

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IEEE SYSTEMS JOURNAL 2021年第2期15卷 2971-2979页

作者： Qin, Yunhui Huangfu, Wei Zhang, Haijun Long, Keping Yuan, Jinhong Univ Sci & Technol Beijing Beijing Engn & Technol Res Ctr Convergence Networ Inst Artificial Intelligence Beijing 100083 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney New 2052 Australia

This article, with the intent of maximizing the cellular system coverage ratio, reveals the pseudometric structure of the solution space for antenna azimuth variables. First, we study the solution space of azimuth variables with equivalent and neighboring characteristics. A generalized improvement to coverage and capacity optimization (CCO) approaches is proposed only in the light of the pseudometric structure of the solution space. Then, we model the optimization problem as an unconstrained one and propose a pseudometric-driven evolutionary algorithm to optimize the coverage ratio. Experiments are carried out both in the ideal and the practical urban scenarios. Simulation results show that the proposed algorithm edges out other baseline algorithms belonging to evolutionary computation and swarm intelligence with respect to the convergence speed and the final coverage result. Furthermore, the pseudometric structure, as a fundamental topology on the azimuth solution space, provides a new lens to address other CCO issues concerning the antenna azimuth variables and, thus, provokes rethinking on the coverage optimization of cellular system in general.

关键词： optimization Azimuth Antennas Evolutionary computation Task analysis Genetic algorithms Linear programming Antenna azimuth coverage optimization cellular systems evolutionary algorithm pseudometric structure

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