基于Delaunay图的人工蜂群算法在WSN覆盖策略中的优化研究
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摘要
传统的人工蜂群算法在应用于无线传感器网络覆盖时,虽然可以提高网络覆盖率,但是其后期收敛速度慢和早熟收敛等缺点,大量的消耗时间和能量,也无法确保网络覆盖质量.为提高混合无线传感器网络的覆盖效率,提出一种基于Delaunay图的人工蜂群算法控制移动节点的部署策略.通过固定节点形成的Delaunay图先找出覆盖漏洞,估算覆盖漏洞面积并计算出移动节点即引领蜂的数量和初始位置,通过评价覆盖漏洞面积的大小确定侦查蜂的局部搜索空间.通过对不同算法的仿真结果分析表明:D-ABC提高了网络覆盖率,进行了混合无线传感器网络覆盖策略的优化.
The traditional artificial bee colony algorithm can improve the network coverage when it is applied to the coverage of wireless sensor networks. Though it can improve the network coverage, its shortcomings such as slow convergence,premature convergence,a lot of waste and energy consumption can not guarantee the quality of network coverage. In order to improve the coverage efficiency of hybrid wireless sensor networks,an artificial bee colony algorithm based on Delaunay graph is proposed to control the deployment strategy of mobile nodes. Firstly,coverage loopholes was found through the fixed node to form the Delaunay graph. Estimate the coverage of the vulnerability area and calculate the number of mobile nodes that lead the bee and the initial position. The local search space of the detection bee was determined by evaluating the size of the coverage vulnerability area. Through the analysis of the simulation results of different algorithms,D-ABC improved network coverage and optimized the hybrid wireless network coverage strategy.
The traditional artificial bee colony algorithm can improve the network coverage when it is applied to the coverage of wireless sensor networks. Though it can improve the network coverage, its shortcomings such as slow convergence,premature convergence,a lot of waste and energy consumption can not guarantee the quality of network coverage. In order to improve the coverage efficiency of hybrid wireless sensor networks,an artificial bee colony algorithm based on Delaunay graph is proposed to control the deployment strategy of mobile nodes. Firstly,coverage loopholes was found through the fixed node to form the Delaunay graph. Estimate the coverage of the vulnerability area and calculate the number of mobile nodes that lead the bee and the initial position. The local search space of the detection bee was determined by evaluating the size of the coverage vulnerability area. Through the analysis of the simulation results of different algorithms,D-ABC improved network coverage and optimized the hybrid wireless network coverage strategy.
引文
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[16]文政颖,翟红生.基于混沌人工蜂群算法的无线传感器网络覆盖优化[J].计算机测量与控制,2014,22(5):1609-1612.
[2]韦宁.无线传感器网络节点动态部署研究[D].大连:大连理工大学,2013:2-14.
[3]STANDEN P J,BROWN D J,BATTERSBY S,,et al.A Study to Evaluate a Low Cost Virtual Reality System for Home Based Rehabilitation of the Upper Limb Following Stroke[J].International Journal on Disability and Human Development,2011,10(4):337-341.
[4]胡珂.基于人工蜂群算法在无线传感网络覆盖优化策略中的应用研究[D].成都:成都电子科技大学,2012:12-18.
[5]OZTURK C,KARABOGA D,GORKEMLI B.Probabilistic Dynamic Deployment of Wireless Sen sor Networks by Artificial Bee Colony Algorithm[J].Sensors,2011,11(6):6056-6065.
[6]LEE H J,HAN Y H,KIM Y H,el al.Proceedings of the IEEE 70th Vehicular Technology Conference Fall(VTC 2009-Fall)[C].Anchorage,AK:IEEE,2009:20-23.
[7]徐凌伟,张浩,吕婷婷,等.移动无线传感器网络系统在n-Rayleigh信道下的性能分析[J].传感技术学报,2015,28(2):265-270.
[8]KARABOGA D.BASTURK B.On the Performance of Artificial Bee Colony(ABC)Algorithm[J].Applied Soft Comuting,2008,8(1):687-697.
[9]宋苏鸣.基于改进人工蜂群算法的无线传感器网络覆盖优化策略[D].西安:西安电子科技大学,2014:9-18.
[10]窦慧丽.基于Delaunay三角剖分的指纹匹配算法[D].长春:吉林大学,2014:5-17.
[11]秦宁宁,陈家乐,丁志国.覆盖率均衡区域覆盖算法[J].传感技术学报,2015,28(4):578-584.
[12]吴盼.传感器网络覆盖与定位中的优化问题研究[D].南京:南京大学,2015:25-30.
[13]刘晓爽.无线传感器网络覆盖与优化技术研究[D].北京:北京邮电大学,2015:23-39.
[14]张银雪,田学民,邓晓刚.基于改进人工蜂群算法的盲源分离方法[J].电子学报,2012,40(10):2026-2030.
[15]付光杰,胡明哲,乔永娜.改进蜂群算法的WSN节点分布优化研究[J].吉林大学学报(信息科学版),2017,35(5):507-512.
[16]文政颖,翟红生.基于混沌人工蜂群算法的无线传感器网络覆盖优化[J].计算机测量与控制,2014,22(5):1609-1612.
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