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Multi-Node Small Radar Network deployment optimization in 3D Terrain

SENSORS 2025年第7期25卷 1964-1964页

作者： Wang, Zhiyi Wang, Min Wu, Xinghui Yang, Shuyuan Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China Xian Univ Finance & Econ Informat Ctr Xian 710100 Peoples R China Xidian Univ Sch Artificial Intelligence Xian 710126 Peoples R China

When deploying multi-node small radar networks in cities or mountains, it is crucial to consider the influence of terrain. The propagation of radio waves in areas with known three-dimensional (3D) terrain differs significantly from that in free space. However, existing radar deployment optimization methods often rely on simplistic propagation models that do not accurately capture the variations in coverage at different heights. Therefore, the parabolic equation model (PEM) is first introduced to radar network deployment considering terrain constraints. After obtaining coverage results for different altitude layers, the Layered Effective Coverage Rate (LECR) is proposed as our optimization objective. Then, the nondominated sorting genetic algorithm III (NSGA-III) is employed to address this multi-objective optimization problem. Finally, the experimental results demonstrate the superiority of introducing PEM and the effectiveness of NSGA-III.

关键词： radar network deployment optimization radio wave propagation layered effective coverage rate

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deployment optimization for target perpetual coverage in energy harvesting wireless sensor network

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Digital Communications and Networks 2024年第2期10卷 498-508页

作者： Zhenkun Jin Yixuan Geng Chenlu Zhu Yunzhi Xia Xianjun Deng Lingzhi Yi Xianlan Wang Wuhan Business University 430056China Wuhan Research Institute of Post and Telecommunication Wuhan430074China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data SecuritySchool of Cyber Science and EngineeringHuazhong University of Science and TechnologyWuhan430074China Hubei Chutian Expressway Digital Technology Co.Ltd Wuhan430074China School of Information and Safety Engineering Zhongnan University of Economics and LawWuhan430073China

Energy limitation of traditional Wireless Sensor Networks(WSNs)greatly confines the network lifetime due to generating and processing massive sensing data with a limited *** energy harvesting WSN is a novel network architecture to address the limitation of traditional ***,existing coverage and deployment schemes neglect the environmental correlation of sensor nodes and external energy with respect to physical *** considering the spatial correlation of the environment and the uneven distribution of energy in energy harvesting WSN,we investigate how to deploy a collection of sensor nodes to save the deployment cost while ensuring the target perpetual *** Confident Information Coverage(CIC)model is adopted to formulate the CIC Minimum deployment Cost Target Perpetual Coverage(CICMTP)problem to minimize the deployed sensor *** the CICMTP is NP-hard,we devise two approximation algorithms named Local Greedy Threshold Algorithm based on CIC(LGTA-CIC)and Overall Greedy Search Algorithm based on CIC(OGSA-CIC).The LGTA-CIC has a low time complexity and the OGSA-CIC has a better approximation *** simulation results demonstrate that the OGSA-CIC is able to achieve lower deployment cost and the performance of the proposed algorithms outperforms GRNP,TPNP and EENP algorithms.

关键词： Energy harvesting WSN deployment optimization Confident information coverage(CIC) Target perpetual coverage

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deployment optimization of Roadside Unit With Failure Probability Based on Stochastic Mixed Traffic Equilibrium

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第7期25卷 7792-7804页

作者： Zeng, Minghua He, Jing Guangdong Polytech Normal Univ Sch Automot & Traff Engn Guangzhou 510665 Peoples R China East China Jiaotong Univ Sch Traff & Transportat Engn Nanchang 330013 \ Peoples R China Jiangxi Tech Coll Mfg Sch Automot & Aviat Nanchang 330095 Peoples R China

The reasonable deployment of roadside units (RSUs) in a vehicular ad hoc network (VANET) is important for the efficient and safe operation of automated vehicles (AVs) that are mixed with human-driven vehicles (HVs) in a near future modern city. Because an RSU faces failure under the circumstances of such as electricity depletion and line fault, we consider RSU failure probability in studying the problem of RSU deployment optimization. First, we establish the formulas of connectivity considering failure probability and communication radius of RSUs. Second, assuming that the road traffic is mixed with AVs and HVs, we propose a connectivity-based stochastic user equilibrium model to describe the route choice of mixed traffic. Third, we propose a bi-level programming for optimizing RSUs deployment with the objective function as total connectivity and coverage (TCC) of the network. The lower level is the stochastic user equilibrium of mixed traffic. Fourth, we design an improved binary particle swarm optimization algorithm embedded with the method of successive average to solve the bi-level model to obtain the optimal deployment scheme of RSUs. Numerical results show that: 1) the optimal RSUs deployment scheme suggests deploying the optimal number of RSUs with appropriate communication radius at selected sites. Especially, to ensure at least 90% TCC, the optimal scheme reduces the number of deployed RSUs;and 2) failure probability significantly affects the TCC in scenarios with low modal share of AV (0.05-0.1). RSU communication range, compared with the failure probability, has greater influence on deployed RSU number to ensure at least 90% TCC.

关键词： Vehicular ad hoc network roadside unit deployment optimization mixed traffic bi-level programming

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deployment optimization for 3D industrial wireless sensor networks based on particle swarm optimizers with distributed parallelism

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2018年 103卷 225-238页

作者： Cao, Bin Zhao, Jianwei Lv, Zhihan Liu, Xin Kang, Xinyuan Yang, Shan Hebei Univ Technol Sch Comp Sci & Engn Tianjin 300401 Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou 510006 Guangdong Peoples R China Hebei Prov Key Lab Big Data Calculat Tianjin 300401 Peoples R China Qingdao Univ Sch Data Sci & Software Engn Qingdao 266071 Peoples R China

For wireless sensor networks (WSNs), traditional studies on deployment problems center upon 2D plane or 3D full space. However, practical situations are more complex, and simplifications may hinder real-world application. In this paper, we study the scenario of a 3D industrial space with obstacles (i.e., devices). Heterogeneous directional sensor nodes and relay nodes are deployed to maximize coverage and prolong lifetime, respectively. Specifically, sensor nodes are deployed for the maximization of coverage;after the positions of sensor nodes are generated, we deploy relay nodes to maximize the lifetime. A modified 3D coverage model and a lifetime model with reliability constraint are presented to facilitate the mathematical analysis of the deployment problem. For the NP-hard deployment problem, two particle swarm optimizers, the cooperative coevolutionary particle swarm optimization 2 (CCPSO2) and the comprehensive learning particle swarm optimizer (CLPSO), are employed. To reduce the computation time, distributed parallelism based on message passing interface (MPI) is conducted by dividing the 3D deployment space. Extensive experimentations are conducted by using various numbers of sensor nodes and relay nodes, and thorough understandings are obtained w.r.t. both the deployment problem and the optimizers.

关键词： Industrial wireless sensor networks (IWSNs) Heterogeneous directional sensor nodes Relay nodes deployment optimization Coverage Lifetime Particle swarm optimization (PSO) Message passing interface (MPI) Distributed parallelism

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deployment optimization of wireless mesh networks in wind turbine condition monitoring system

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WIRELESS NETWORKS 2021年第2期27卷 1459-1476页

作者： Yang, Yanjun Liu, Aimin Xin, Hongwei Wang, Jianguo Yu, Xin Zhang, Wen Shenyang Univ Technol Sch Elect Engn Shenyang 110870 Peoples R China Northeast Elect Power Univ Sch Automat Engn Jilin 132012 Jilin Peoples R China Jilin Jidian New Energy Co Ltd Changchun 130117 Peoples R China

This study aims to propose a deployment optimization method based on IEEE 802.11 heterogeneous wireless mesh networks (WMNs) for the condition monitoring system (CMS) of wind turbines. This method can provide a flexible, low-cost, and easy-to-implement network framework for wind farms in harsh environments, thereby avoiding interference of the newly installed CMS on the communication network of the SCADA system. The K-medoids clustering algorithm transforms the continuous space location problem into the discrete space location problem. Moreover, the mesh client (MC) coverage and backbone network connectivity issues are considered to ensure that the generated candidate point set can meet the coverage and connectivity requirements without generating numerous redundant nodes. Firstly, according to the characteristics of the model, the K-medoids clustering algorithm is used to obtain a set of candidate points of mesh routers (MRs) that meet the coverage rate. Secondly, a reasonable connection algorithm is proposed according to the constraints, and the optimal deployment is selected from the candidate points. Thirdly, taking the number of hops from MR to MG and the priority of MC as the weight. The path planning with the least number of MRs and load balancing is obtained by constructing a minimum spanning tree (MST) based on the improved Kruskal algorithm. Finally, Multi-channel inter-frequency networking technology is adopted to reduce interference between co-channels and between adjacent channels. The result shows that the method proposed can minimize network operating costs, meet the capacity requirements of MC, and reduce link losses.

关键词： deployment optimization Wireless mesh networks Wind turbine K-medoids Condition monitoring system

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deployment optimization for Shared e-Mobility Systems With Multi-Agent Deep Neural Search

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第3期23卷 2549-2560页

作者： Luo, Man Du, Bowen Klemmer, Konstantin Zhu, Hongming Wen, Hongkai Alan Turing Inst London NW1 2DB England Univ Warwick Dept Comp Sci Coventry CV4 7AL W Midlands England Univ Exeter Dept Comp Sci Exeter EX4 4QF Devon England Tongji Univ Sch Software Engn Shanghai 200092 Peoples R China

Shared e-mobility services have been widely tested and piloted in cities across the globe, and already woven into the fabric of modern urban planning. This paper studies a practical yet important problem in those systems: how to deploy and manage their infrastructure across space and time, so that the services are ubiquitous to the users while sustainable in profitability. However, in real-world systems evaluating the performance of different deployment strategies and then finding the optimal plan is prohibitively expensive, as it is often infeasible to conduct many iterations of trial-and-error. We tackle this by designing a high-fidelity simulation environment, which abstracts the key operation details of the shared e-mobility systems at fine-granularity, and is calibrated using data collected from the real-world. This allows us to try out arbitrary deployment plans to learn the optimal given specific context, before actually implementing any in the real-world systems. In particular, we propose a novel multi-agent neural search approach, in which we design a hierarchical controller to produce tentative deployment plans. The generated deployment plans are then tested using a multi-simulation paradigm, i.e., evaluated in parallel, where the results are used to train the controller with deep reinforcement learning. With this closed loop, the controller can be steered to have higher probability of generating better deployment plans in future iterations. The proposed approach has been evaluated extensively in our simulation environment, and experimental results show that it outperforms baselines e.g., human knowledge, and state-of-the-art heuristic-based optimization approaches in both service coverage and net revenue.

关键词： optimization Public transportation Urban areas Reinforcement learning Planning Search problems Data models Shared mobility systems electric vehicles deployment optimization deep reinforcement learning

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deployment optimization of UAV-Aided Networks Through a Dynamic Tunable Model

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IEEE COMMUNICATIONS LETTERS 2021年第7期25卷 2348-2352页

作者： Liu, Jianghui Zhang, Hongtao He, Yuan Beijing Univ Posts & Telecommun Key Lab Universal Wireless Commun Minist Educ China Beijing 100876 Peoples R China

In the existing work of unmanned aerial vehicle base station (UAV-BS) deployment, where serving radius (SR) is fixed regardless of resource waste and overlapping interference, dynamic tunability of SR has not been considered. In this letter, a dynamic tunable model where SR can be properly adjusted is proposed, based on which a semi-progressive offloading deployment scheme devoted to UAV number and overlapping interference optimization is raised. Specifically, UAV is deployed around macro base stations (MBSs), where a certain proportion (gamma) of the ground terminals (GTs) are covered first with the principle of minimum overlap and maximum coverage, then through tunability of SR, the rest GTs are covered without increasing UAV number. Moreover, the base station expands its SR to cover the closest uncovered GT through fine adjustment of power (MBS) or antenna down-tilt (UAV), after which surrounding UAVs are also adjusted to reduce overlapping interference. The results show our proposed scheme reduces 17.26% UAV quantity compared with the cluster method and decreases 71.75% interference compared with the core-set method.

关键词： Dynamic tunable model deployment optimization overlapping interference coverage ratio UAV-aided network

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deployment optimization in wireless sensor networks using advanced artificial bee colony algorithm

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2024年第6期17卷 3571-3582页

作者： Zhu, Jueyu Rong, Jifang Gong, Zhi Liu, Ying Li, Wenjun Alqahtani, Fayez Tolba, Amr Hu, Jinbin Hunan First Normal Univ Sch Educ Changsha 410205 Peoples R China Hunan Mech & Elect Polytech Dept Informat Engn Changsha 410151 Peoples R China Hunan Univ Informat Technol Sch Comp Sci & Engn Changsha 410151 Peoples R China Changsha Univ Sci & Technol Sch Comp & Commun Engn Changsha 410004 Peoples R China Changsha Univ Sci & Technol Hunan Prov Key Lab Intelligent Proc Big Data Tran Changsha 410004 Peoples R China King Saud Univ Software Engn Dept Coll Comp & Informat Sci Riyadh 12372 Saudi Arabia King Saud Univ Dept Comp Sci Community Coll Riyadh 11437 Saudi Arabia

With the widespread implementation of artificial intelligence techniques and self-driving systems in smart cars, providing excellent coverage of wireless sensor networks is critical for stable and effective tasks. Coverage control is an essential task for the design of wireless sensor networks. However, considering the influence of network resources and coverage features, several normal optimization methods are hard to carry out, yet heuristic iterative algorithms could generate an estimated ideal feasible solution for this issue. We present an artificial bee colony algorithm based on random dual strategies, called RDABC. Specifically, RDABC modifies the optimization direction through alternating between dual search techniques with the goal to find further excellent feasible solution. At the same time, through incorporating cross-mutation strategy to improve variety, and increase the algorithm's optimization efficiency. According to simulation experiments, RDABC outperforms four well-known algorithms in terms of coverage optimization. As a whole, RDABC optimizes the location and deployment of wireless sensors, enhances the overall performance and stability of intelligent transportation systems, and simplifies vehicle monitoring and traffic sign tasks.

关键词： Wireless sensor networks deployment optimization Artificial bee colony Local optimum

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deployment optimization of Dynamic Wireless Chargers for Electric Vehicles

Deployment Optimization of Dynamic Wireless Chargers for Ele...

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IEEE/AIAA Transportation Electrification Conference / Electric Aircraft Technologies Symposium (ITEC+EATS)

作者： El Meligy, Ahmed O. Elghanam, Eiman A. Hassan, Mohamed S. Osman, Ahmed H. American Univ Sharjah Dept Elect Engn Sharjah U Arab Emirates

ISBN: (纸本)9781665405607

The search for eco-friendly means of transportation is significantly increasing, causing the demand for electric vehicles (EVs) to skyrocket. However, the main limitation that is slowing down the adoption of electric vehicles is range anxiety, where users are discouraged to drive an electric vehicle (EV) due to fear of running out of charge mid-trip. This revealed the need for implementing better EV charging solutions including dynamic wireless charging systems. Dynamic Wireless Charging (DWC) exploits wireless power transfer (WPT) to deliver electrical energy from the grid to the EV while in motion, via wireless charging lanes (WCL) placed on the roads. In addition to reducing range anxiety, DWC also eliminates the need for charging downtime. However, expensive infrastructure is needed to deploy the necessary hardware needed for DWC, which explains the need for optimizing the deployment locations of the charging lanes along with the corresponding lane lengths and charging power levels. In this work, the objective of the deployment optimization framework is to minimize the total system costs. The optimization framework is formulated based on traffic simulation data for the city of Sharjah. The problem is then solved using the metaheuristic genetic algorithm to provide recommendations on the most suitable locations to deploy the charging lanes within the traffic map under consideration.

关键词： Electric vehicles dynamic wireless charging deployment optimization charging lanes genetic algorithm

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deployment optimization for camera network coupling with geographic environment

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ANNALS OF GIS 2018年第1期24卷 9-17页

作者： Wang, Ziran Wang, Meizhen Liu, Xuejun Zhang, Yanan Nanjing Normal Univ Coll Informat Engn Taizhou Coll Taizhou Peoples R China Nanjing Normal Univ Sch Geog Sci Nanjing Jiangsu Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Reso Nanjing Jiangsu Peoples R China

Surveillance cameras are widely deployed to monitor the geographical environment, traffic condition, public security, so on. Optimal deployment of surveillance cameras is an effective way to promote their validity and utilization. Because of the ignorance of geographic environment in major existing researches, a new optimization method which considers the constraining relationships between cameras and geographic environment is proposed. Firstly, coverage models for single camera and camera network which consider the occlusions in geographic environment are discussed. Secondly, the objective of optimization is put forward, which is to employ the minimum cameras to monitor the specific areas. Thirdly, to achieve the objective, the heuristic search algorithm is introduced. The deployable camera pose and location, and target area are discretized to promote the efficiency. Consequently, the deployment optimization problem is abstracted into a combinatorial optimization problem. Then optimization model including the graph structure, cost function and heuristic function for the algorithm is built to meet the requirement of the heuristic search algorithm, and its correctness is also strictly proofed. Finally, experiment in real city area with occlusions (such as building) validates the usability of our method. The sampling steps of camera pose and location and the split size of target area for discrete process are two important factors to influence the degree of accuracy and efficiency. The results obtained by our method can provide decision support for optimal camera deployment in real geographic environment, as well as provide reference for other environmental monitoring sensor deployment optimization tasks.

关键词： deployment optimization geographic environment camera network camera coverage heuristic search algorithm

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