{"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}
引用次数: 10
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.