融合 VFM 与 D* 算法的 WSN 动态栅栏覆盖优化

WSN dynamic barrier coverage optimization integrating VFM and D* algorithm

摘要

为了解决无线传感器网络（WSN）的覆盖优化问题，提出了一种基于动态参数调控和虚拟力增强的动态栅栏覆盖 PSO 算法（DPVF-PSO）。首先，初始化网络参数和粒子群参数，随机生成初始群体的位置和速度，并计算每个粒子的适应度值。随后，在迭代过程中动态调整 PSO 参数，计算节点间的虚拟力，选择目标栅栏区域，并融合 D* 算法寻找最短路径，更新粒子的速度和位置，同时进行适应度评估。每隔若干代进行一次局部搜索，以进一步优化全局最优解。尤其在含障碍物的检测区域内，DPVF-PSO 通过虚拟力机制引导节点避开障碍物，同时利用 D* 算法规划最短路径，优化节点的移动轨迹，减少传输延迟和能量消耗，确保网络覆盖的有效性和通信连通性。实验结果表明，在有障碍物的环境中，DPVF-PSO 的覆盖率比 Chaos ABC、IIC-CS、IHPO 和 HHO 算法分别高出 3.623%、5.762%、10.643% 和 4.385%，适用于智能交通管理、环境监测等实际应用场景，具有显著的实用价值。

Abstract

To solve the coverage optimization problem of wireless sensor networks (WSN), this paper proposes the dynamic parameter-controlled virtual force enhanced particle swarm optimization (DPVF-PSO) algorithm for region fence coverage. First, the network parameters and particle swarm parameters are initialized, and the initial positions and velocities of the particles are randomly generated. The fitness values of each particle are then calculated. During the iteration process, the PSO parameters are dynamically adjusted, virtual forces between nodes are computed, target fence regions are selected, and the shortest path is determined using D* algorithm. The particles’ velocities and positions are updated, and fitness evaluations are performed. Every few generations, a local search is conducted to further optimize the global best solution. In particular, within detection areas containing obstacles, DPVF-PSO uses a virtual force mechanism to guide nodes around obstacles, while also leveraging D* algorithm to plan the shortest path, optimize node movement trajectories, reduce transmission delays, and conserve energy, ensuring the effectiveness of network coverage and communication connectivity. Experimental results show that, in environments with obstacles, the coverage rate of DPVF-PSO exceeds that of the Chaos ABC, IIC-CS, IHPO, and HHO algorithms by 3.623%, 5.762%, 10.643%, and 4.385%, respectively. This makes it highly applicable to practical scenarios such as intelligent traffic management and environmental monitoring, demonstrating significant practical value.

关键词

无线传感器网络 / 粒子群优化 / D* 算法 / 虚拟力算法 / 动态参数调整 / 栅栏覆盖

Key words

wireless sensor network (WSN) / particle swarm optimization (PSO) / D* algorithm / virtual force algorithm / dynamic parameter adjustment / barrier coverage

参考文献

[1] BINH N T M , BINH H T T , LUONG H V D , et al. An efficient exact method with polynomial time—complexity to achieve k—strong barrier coverage in heterogeneous wireless multimedia sensor networks[J]. Journal of Network and Computer Applications, 2024, 231: 103985.

[2] SÁMANO—ORTEGA V , ARZATE—RIVAS O , MARTÍNEZ—NOLASCO J , et al. Multipurpose modular wireless sensor for remote monitoring and IoT applications[J]. Sensors, 2024, 24(4): 1277.

[3] WU P , YU L , YI X M , et al. Research on WSN reliable ranging and positioning algorithm for forest environment[J]. Scientific Reports, 2024, 14: 5417.

[4] ZHU L F , WANG D . Robust multi—factor authentication for WSNs with dynamic password recovery[J]. IEEE Transactions on Information Forensics and Security, 2024, 19: 8398-8413.

[5] BHARGAVI K V N A , VARMA G P S , HEMALATHA I , et al. An enhanced particle swarm optimization—based node deployment and coverage in sensor networks[J]. Sensors, 2024, 24(19): 6238.

[6] DE ASSIS P A Q , RODRIGUES L R , GALVÃO R K H , et al. A genetic algorithm method for improving suboptimal sensor arrangements in coverage and connectivity problems[J]. Applied Soft Computing, 2024, 166: 112047.

[7] 余修武, 晋诗琪 . 融合 VFA 和 ISSA 的多目标优化 WSN 覆盖算法[J]. 西安电子科技大学学报, 2024, 51(6): 60-72.

[8] YU X W , JIN S Q . Multi—objective optimized WSN coverage algorithm integrating VFA and ISSA[J]. Journal of Xidian University, 2024, 51(6): 60-72.

[9] LI Z Q , ZHANG Z H , WU B C , et al. An adaptive coverage strategy for WSNs with dynamic energy decay based on multiobjective dingo optimization algorithm[J]. IEEE Sensors Journal, 2024, 24(9): 15133-15144.

[10] 贾润亮, 张海玉 . 改进群体智能算法的无线传感器网络覆盖优化[J]. 西南大学学报（自然科学版）, 2024, 46(1): 155-166.

[11] JIA R L , ZHANG H Y . Improved population intelligence algorithm for wireless sensor network coverage optimization[J]. Journal of Southwest University (Natural Science Edition), 2024, 46(1): 155-166.

[12] 谢珊, 马琳娟, 苏鑫, 等. 无线传感器网络覆盖优化的混沌交叉人工蜂群算法[J]. 南京理工大学学报, 2024, 48(3): 360-366.

[13] XIE S , MA L J , SU X , et al. Chaotic crossover artificial bee colony algorithm for coverage optimization of wireless sensor networks[J]. Journal of Nanjing University of Science and Technology, 2024, 48(3): 360-366.

[14] 杨乐, 张达敏, 何庆, 等. 改进猎人猎物优化算法在 WSN 覆盖中的应用[J]. 计算机应用, 2024, 44(8): 2506-2513.

[15] YANG L , ZHANG D M , HE Q , et al. Application of improved hunter—prey optimization algorithm in WSN coverage[J]. Journal of Computer Applications, 2024, 44(8): 2506-2513.

[16] 张士荣, 赵俊杰, 谈发明 . 基于多策略混合改进 HHO 算法的 WSN 节点覆盖优化[J]. 计算机工程与设计, 2024, 45(2): 328-338.

[17] ZHANG S R , ZHAO J J , TAN F M . Wireless sensor network node coverage optimization based on multi—strategy hybrid improved Harris hawk algorithm[J]. Computer Engineering and Design, 2024, 45(2): 328-338.

[18] LIU X B , WANG Y , GAO H , et al. A coverage—aware task allocation method for UAV—assisted mobile crowd sensing[J]. IEEE Transactions on Vehicular Technology, 2024, 73(7): 10642-10654.

[19] WANG Z D , HUANG L L , YANG S X , et al. Multi—strategy enhanced grey wolf algorithm for obstacle—aware WSNs coverage optimization[J]. Ad Hoc Networks, 2024, 152: 103308.

[20] MA Y Y , OTA K , DONG M X . Multi—RIS deployment location optimization for coverage enhancement in outdoor wireless communication networks[J]. IEEE Transactions on Vehicular Technology, 2024, 73(10): 15332-15344.

[21] 范兴刚, 王超, 杨静静, 等. 一种基于选择框的有向 K—栅栏构建算法[J]. 计算机学报, 2016, 39(5): 946-960.

[22] FAN X G , WANG C , YANG J J , et al. A strong K—barrier construction scheme based on selecting box for directional sensor networκs[J]. Chinese Journal of Computers, 2016, 39(5): 946-960.

[23] 党小超, 李月霞, 郝占军, 等. 一种基于改进蚁群算法的三维 K—栅栏覆盖算法[J]. 计算机工程, 2020, 46(2): 221-229.

[24] DANG X C , LI Y X , HAO Z J , et al. A 3D K—barrier coverage algorithm based on improved ant colony algorithm[J]. Computer Engineering, 2020, 46(2): 221-229.

[25] AFIZUDEEN S , PAVITHRA R . Grundy number—based optimal sensor placement in 3D wireless sensor network[J]. IEEE Access, 2024, 12: 148502-148515.

[26] 孙泽宇, 伍卫国, 曹仰杰, 等. 无线传感器网络中能量均衡参数可控覆盖算法[J]. 西安交通大学学报, 2016, 50(8): 77-83.

[27] SUN Z Y , WU W G , CAO Y J , et al. EBPCC: Energy balance parameters—controlled covering algorithm for wireless sensor networks[J]. Journal of Xi’an Jiaotong University, 2016, 50(8): 77-83.

[28] HU H B , YANG Y . Piecewise combinatorial particle swarm optimization[J]. Applied Soft Computing, 2024, 166: 112060.

[29] WEN Q , ZHAO X Q , CUI Y P , et al. Coverage enhancement algorithm for WSNs based on vampire bat and improved virtual force[J]. IEEE Sensors Journal, 2022, 22(8): 8245-8256.

[30] SUN J G , WANG W S , LI S Z , et al. Scheduling optimization for UAV communication coverage using virtual force—based PSO model[J]. Digital Communications and Networks, 2024, 10(4): 1103-1112.

[31] ZHANG W , WANG N X , WU W H . A hybrid path planning algorithm considering AUV dynamic constraints based on improved A* algorithm and APF algorithm[J]. Ocean Engineering, 2023, 285: 115333.

[32] YANG Y L , LUO X Y , LI W , et al. AAPF*: A safer autonomous vehicle path planning algorithm based on the improved A* algorithm and APF algorithm[J]. Cluster Computing, 2024, 27(8): 11393-11406.

[33] TIAN Y S , ZHANG D , ZHANG H B , et al. An improved cuckoo search algorithm for global optimization[J]. Cluster Computing, 2024, 27(6): 8595-8619.

基金资助

湖南省自然科学基金(2024JJ5338)