基于虚拟力和果蝇优化算法混合控制水声传感器网络部署策略

摘要/Abstract

摘要： 提出了基于改进的虚拟力和果蝇优化(Virtual Force and Fruit Fly Optimization，VFFO)算法混合控制水声传感器网络部署优化的方法.该方法首先通过虚拟力算法对传感器节点的初始部署进行优化，以得到较好的初始部署状态；然后通过改进的果蝇算法对水声传感器网络进行重部署，同时分析了算法的移动部署能耗问题.仿真结果表明，该算法在相同能耗下能够得到更高的网络有效覆盖率.

关键词: 水声传感器网络；部署优化；虚拟力；果蝇优化算法

Abstract: This paper proposes the virtual force and fruit fly optimization (VFFO) algorithm which consists of the improved virtual force and fruit fly optimization methods. The algorithm firstly optimizes the initial deployment by virtual force to get a better initial deployment status, then the improved fruit fly optimization algorithm is used to redeploy the sensor networks. Moreover, the energy consumption of the deployment is analysed. The simulation results show that the proposed algorithm can achieve a higher effective coverage rate with the same energy consumption.

Key words: acoustic sensor networks； deployment optimization； virtual force； fruit fly optimization algorithm

中图分类号:

TP 393

张颖，乔运龙,张海洋. 基于虚拟力和果蝇优化算法混合控制水声传感器网络部署策略[J]. 上海交通大学学报, 2017, 51(6): 715-721.

ZHANG Ying,QIAO Yunlong*,ZHANG Haiyang. Coverage Enhancing for Underwater Acoustic Sensor Networks Based on
Virtual Force and Fruit Fly Optimization Algorithm[J]. Journal of Shanghai Jiao Tong University, 2017, 51(6): 715-721.

参考文献

［1］LIORET J. Underwater sensor nodes and networks ［J］. Sensors, 2013, 13(9): 1178211796.
［2］AKYILDIZ I F, POMPILI D, MELODIA T. Underwater acoustic sensor networks: Research challenges ［J］. Ad Hoc Networks, 2005, 3(3): 257279.
［3］GAGE D W. Command control for manyrobot systems ［C］∥Proceedings of the 19th Annual AUVS Technical Symposium. Huntsville, USA: Unmanned Systems, 1992: 2834.
［4］ZOU Y, CHAKRABARTY K. Sensor deployment and target localization based on virtual forces ［C］∥Proceedings of the 22nd Annual Joint Conference of the IEEE Computer Communications. Washington, D C, USA: IEEE, 2003: 12931303.
［5］HOWARD A, MATARIC M J, SUKHATME G S. Mobile sensor network deployment using potential fields: A distributed scalable solution to the area coverage problem ［C］∥Proceedings of the 6th International Symposium on Distributed Autonomous Robotic Systems. Fukuoka, Japan: Springer, 2002: 299308.
［6］LIAN X, ZHANG J, CHEN C, et al. Threedimensional deployment optimization of sensor network based on an improved Particle Swarm Optimization algorithm ［C］∥2012 10th World Congress on Intelligent Control and Automation (WCICA). Beijing, China: IEEE, 2012: 43954400.
［7］LI S, XU C, PAN W, et al. Sensor deployment optimization for detecting maneuvering targets ［C］∥2005 8th International Conference on Information Fusion. Philadephia, PA, USA: IEEE, 2005: 16291635.
［8］TIAN D, GEORGANAS N D. Connectivity maintenance and coverage preservation in wireless sensor networks ［J］. Ad Hoc Networks, 2005, 3(6): 744761.
［9］YE F, ZHONG G, CHENG J, et al. PEAS: A robust energy conserving protocol for longlived sensor networks ［C］∥2003 23rd International Conference on Distributed Computing Systems. Providence, RI, USA: IEEE, 2003: 2837.
［10］PAL A. Localization algorithms in wireless sensor networks: Current approaches and future challenges ［J］. Network Protocols and Algorithms, 2010, 2(1): 4573.
［11］NICULESCU D, NATH B. Ad hoc positioning system (APS) ［C］∥Global Telecommunications Conference, 2001 GLOBECOM’01. San Antonio, TX, USA: IEEE, 2001: 29262931.
［12］LI X, FREY H, SANTORO N, et al. Localized sensor selfdeployment with coverage guarantee ［C］∥Proceedings of the ACM SIGMOBILE Mobile Computing and Communications Review. New York, USA: ACM, 2008: 5052.
［13］TAN G, JARVIS S A, KERMARREC A M. Connectivity guaranteed and obstacleadaptive deployment schemes for mobile sensor networks ［C］∥Proceedings of the 28th International Conference on Distributed Computing Systems. Washington, D C, USA: IEEE, 2008: 429437.
［14］LIU H, CHU X, LEUNG Y W, et al. Simple movement control algorithm for biconnectivity in robotic sensor networks ［J］. IEEE Journal on Selected Areas in Communications, 2010,28(7): 9941005.
［15］PAN W T. A new fruit fly optimization algorithm: Taking the financial distress model as an example ［J］. KnowledgeBased Systems, 2012, 26: 6974.

编辑推荐 0

Metrics

阅读次数

全文

HTML			PDF

最新录用	在线预览	正式出版	最新录用	在线预览	正式出版
0	0	0	2	0	74

来源	本网站	其他网站

次数	26	50
比例	34%	66%

摘要

596

最新录用	在线预览	正式出版

53	0	543

来源	本网站	其他网站

次数	297	299
比例	50%	50%

[1]	赵子任1, 杜世昌1, 黄德林1, 任斐2, 梁鑫光2. 多工序制造系统暂态阶段产品质量#br# 马尔科夫建模与瓶颈分析[J]. 上海交通大学学报, 2017, 51(10): 1166-1173.
[2]	周鹏辉, 马红占, 陈东萍, 陈梦月, 褚学宁. 基于模糊随机故障模式与影响分析的#br# 产品再设计模块识别[J]. 上海交通大学学报, 2017, 51(10): 1189-1195.
[3]	李昌玺1, 2, 周焰1, 林菡3, 李灵芝1, 郭戈1. 基于MIMOFNN模型的弹道导弹目标#br# 时空序贯融合识别方法[J]. 上海交通大学学报, 2017, 51(9): 1138-.
[4]	冯明月, 何明浩, 韩俊, 郁春来. 基于协方差拟合旋转不变子空间信号参数#br# 估计算法的高分辨到达角估计[J]. 上海交通大学学报, 2017, 51(9): 1145-.
[5]	杨平1，盛杰1，王禹程2，李柱永1，金之俭1，洪智勇1. YBa2Cu3O7δ超导带材非均匀性对失超传播特性的影响[J]. 上海交通大学学报（自然版）, 2017, 51(9): 1090-1096.
[6]	王星, 周一鹏, 田元荣, 陈游, 周东青, 贺继渊. 基于改进遗传算法和SinChirplet原子的调频#br# 雷达信号稀疏分解[J]. 上海交通大学学报, 2017, 51(9): 1124-1130.
[7]	张良俊1, 2, 李晓慈1, 吴静怡1, 蔡爱峰1. 大型空间展开机构微重力环境模拟#br# 悬吊装置热结构耦合分析[J]. 上海交通大学学报, 2017, 51(8): 954-961.
[8]	夏海亮1, 2, 刘亚坤1, 2, 刘全桢3, 刘宝全3, 傅正财1, 2. 长持续时间雷电流分量作用下电极形状#br# 对金属烧蚀特性的影响[J]. 上海交通大学学报, 2017, 51(8): 903-908.
[9]	谷家扬, 谢玉林, 陶延武, 黄祥宏, 吴介. 新型浮式钻井生产储油平台#br# 涡激运动数值模拟及试验研究 [J]. 上海交通大学学报, 2017, 51(7): 878-885.
[10]	林达, 朱益佳, 魏小栋, 王志宇, 张武高. 喷油参数对聚甲氧基二甲醚/柴油发动机燃烧及其#br# 颗粒物排放的影响[J]. 上海交通大学学报, 2017, 51(7): 787-795.
[11]	孟庆阳1, 阎威武1, 胡勇1, 程建林1, 陈世和2, 张曦2. 基于子空间方法的超超临界机组#br# 过热蒸汽系统模型辨识[J]. 上海交通大学学报, 2017, 51(6): 672-678.
[12]	蒋华军a, 蔡艳a, b, 李超豪a, 李芳a, b, 华学明a, b. 基于改进Sobel算法的焊缝X射线图像#br# 气孔识别方法[J]. 上海交通大学学报, 2017, 51(6): 665-671.
[13]	董冠华,殷勤,殷国富,向召伟. 机床结合部耦合动刚度的辨识与建模[J]. 上海交通大学学报（自然版）, 2015, 49(09): 1263-1434.
[14]	谢启江，余海东. 硬岩掘进机刀盘载荷与撑靴接触界面刚度的耦合关系[J]. 上海交通大学学报（自然版）, 2015, 49(09): 1269-1275.
[15]	仲健林1，马大为1，任杰1，李士军2，王旭3. 基于平面应变假设的橡胶圆筒静态受压分析[J]. 上海交通大学学报（自然版）, 2015, 49(09): 1276-1280.