面向差异化覆盖的异构有向传感器节点调度算法  

Heterogeneous directional sensor node scheduling algorithm for differentiated coverage

作　　者：李明[1,2] 胡江平 曹晓莉[1] 彭鹏[3] LI Ming;HU Jiangping;CAO Xiaoli;PENG Peng(School of Computer Science and Information Engineering,Chongqing Technology and Business University,Chongqing 400067,China;College of Automation Engineering,University of Electronic Science and Technology of China,Chengdu Sichuan 611731,China;Chongqing Yingka Electronic Company Limited,Chongqing 400039,China)

机构地区：[1]重庆工商大学计算机科学与信息工程学院,重庆400067 [2]电子科技大学自动化工程学院,成都611731 [3]重庆英卡电子有限公司,重庆400039

出　　处：《计算机应用》2020年第12期3563-3570,共8页journal of Computer Applications

基　　金：重庆市社会科学规划项目(2017YBGL142);重庆教委科学技术研究项目(KJQN201900839,KJ1600627);重庆工商大学科研平台开放课题(KFJJ2019072,KFJJ2017048);重庆市教育科学规划项目(2018-GX-023);智能生态物联网创新创业团队项目(CQYC201903246)。

摘　　要：为延长异构有向传感器网络的寿命,提出一种基于改进珊瑚礁优化算法(ECRO)的面向不同监测目标有不同监测要求的节点调度算法。利用ECRO将传感器集合划分成符合覆盖要求的多个集合,通过集合间的调度达到延长网络寿命的目的。对珊瑚礁优化算法(CRO)的改进体现在四个方面:一是在珊瑚礁的雌雄同体繁殖过程中融入生物地理学优化算法中的迁移操作,保留原有种群的优秀解;二是在雌雄同体繁殖过程中采用一种带有混沌参数的差分变异因子,增强子代的优化能力;三是通过对最差个体执行随机反向学习,增强种群的多样性;四是通过CRO与模拟退火算法的结合,增强算法的局部搜索能力。对数值基准函数和节点调度进行了大量的仿真实验。在数值测试方面的结果表明,与遗传算法、模拟退火算法、差分进化算法及其改进算法相比,ECRO的优化能力更强;在传感器网络节点调度方面的结果表明,与贪婪算法、基于学习自动机的差分进化(LADE)算法和未改进的CRO相比,ECRO使网络寿命分别提高了53.8%、19.0%和26.6%,验证了所提算法的有效性。In order to prolong the lifespan of heterogeneous directional sensor network,a node scheduling algorithm based on Enhanced Coral Reef Optimization algorithm(ECRO)and with different monitoring requirements for different monitoring targets was proposed.ECRO was utilized to divide the sensor set into multiple sets satisfying the coverage requirements,so that the network lifespan was able to be prolonged by the scheduling among sets.The improvement of Coral Reef Optimization algorithm(CRO)was reflected in four aspects.Firstly,the migration operation in biogeography-based optimization algorithm was introduced into the brooding of coral reef to preserve the excellent solutions of the original population.Secondly,the differential mutation operator with chaotic parameter was adopted in brooding to enhance the optimization ability of the offspring.Thirdly,a random reverse learning strategy were performed on the worst individual of population in order to improve the diversity of population.Forthly,by combining CRO and simulated annealing algorithm,the local searching capability of algorithm was increased.Extensive simulation experiments on both numerical benchmark functions and node scheduling were conducted.The results of numerical test show that,compared with genetic algorithm,simulated annealing algorithm,differential evolution algorithm and the improved differential evolution algorithm,ECRO has better optimization ability.The results of sensor network node scheduling show that,compared with greedy algorithm,the Learning Automata Differential Evolution(LADE)algorithm,the original CRO,ECRO has the network lifespan improved by53.8%,19.0%and 26.6%respectively,which demonstrates the effectiveness of the proposed algorithm.

关 键 词：异构有向传感器网络 覆盖调度算法 珊瑚礁优化算法 生物地理学优化算法 差分变异因子 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]