{"title":"基于粒子群算法的无线传感器网络拓扑控制","authors":"Robert Cristian Abreu, J. Arroyo","doi":"10.1109/SCCC.2011.2","DOIUrl":null,"url":null,"abstract":"This paper addresses the minimum energy network connectivity (MENC) problem. This problem consists of minimizing the transmission power of each sensor in a wireless network, which results in minimizing the energy consumption of the network, while keeping its global connectivity at the same time. The MENC problem is NP-hard in the strong sense. The NP-hardness of the problem motivates us to develop a heuristic algorithm based on the Particle Swarm Optimization to obtain near-optimal solutions. The proposed heuristic is tested on a set of 50 instances of the problem. The computational results show that our approach is a promising heuristic and it performs better than the classical minimum spanning tree (MST) heuristic.","PeriodicalId":173639,"journal":{"name":"2011 30th International Conference of the Chilean Computer Science Society","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"A Particle Swarm Optimization Algorithm for Topology Control in Wireless Sensor Networks\",\"authors\":\"Robert Cristian Abreu, J. Arroyo\",\"doi\":\"10.1109/SCCC.2011.2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper addresses the minimum energy network connectivity (MENC) problem. This problem consists of minimizing the transmission power of each sensor in a wireless network, which results in minimizing the energy consumption of the network, while keeping its global connectivity at the same time. The MENC problem is NP-hard in the strong sense. The NP-hardness of the problem motivates us to develop a heuristic algorithm based on the Particle Swarm Optimization to obtain near-optimal solutions. The proposed heuristic is tested on a set of 50 instances of the problem. The computational results show that our approach is a promising heuristic and it performs better than the classical minimum spanning tree (MST) heuristic.\",\"PeriodicalId\":173639,\"journal\":{\"name\":\"2011 30th International Conference of the Chilean Computer Science Society\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-11-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 30th International Conference of the Chilean Computer Science Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SCCC.2011.2\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 30th International Conference of the Chilean Computer Science Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SCCC.2011.2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Particle Swarm Optimization Algorithm for Topology Control in Wireless Sensor Networks
This paper addresses the minimum energy network connectivity (MENC) problem. This problem consists of minimizing the transmission power of each sensor in a wireless network, which results in minimizing the energy consumption of the network, while keeping its global connectivity at the same time. The MENC problem is NP-hard in the strong sense. The NP-hardness of the problem motivates us to develop a heuristic algorithm based on the Particle Swarm Optimization to obtain near-optimal solutions. The proposed heuristic is tested on a set of 50 instances of the problem. The computational results show that our approach is a promising heuristic and it performs better than the classical minimum spanning tree (MST) heuristic.