{"title":"Fault-Tolerant Sensor Coverage for Achieving Wanted Coverage Lifetime with Minimum Cost","authors":"Zhijun Yu, Jie Wang","doi":"10.1109/WASA.2007.22","DOIUrl":null,"url":null,"abstract":"We study how to select and arrange multiple types of wireless sensors to build a star network that meets the coverage, the lifetime, the fault-tolerance, and the minimum-cost requirements, where the network lifetime, the acceptable failure probability of the network, and the failure rate of each type of sensors are given as parameters. This problem is NP-hard. We model this problem as an integer linear programming minimization problem. We then present an efficient approximation algorithm to find a feasible solution to the problem, which provides a sensor arrangement and a scheduling. We show that, through numerical experiments, our approximation provides solutions with approximation ratios less than 1.4.","PeriodicalId":316831,"journal":{"name":"International Conference on Wireless Algorithms, Systems and Applications (WASA 2007)","volume":"337 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Wireless Algorithms, Systems and Applications (WASA 2007)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WASA.2007.22","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12
Abstract
We study how to select and arrange multiple types of wireless sensors to build a star network that meets the coverage, the lifetime, the fault-tolerance, and the minimum-cost requirements, where the network lifetime, the acceptable failure probability of the network, and the failure rate of each type of sensors are given as parameters. This problem is NP-hard. We model this problem as an integer linear programming minimization problem. We then present an efficient approximation algorithm to find a feasible solution to the problem, which provides a sensor arrangement and a scheduling. We show that, through numerical experiments, our approximation provides solutions with approximation ratios less than 1.4.