{"title":"Congestion Control in Wireless Sensor Networks Based on Bioluminescent Firefly Behavior","authors":"M. S. Manshahia, M. Dave, S. Singh","doi":"10.4236/WSN.2015.712013","DOIUrl":null,"url":null,"abstract":"Congestion in Wireless Sensor Network (WSN) is an issue of concern for several researchers in recent years. The key challenge is to develop an algorithmic rule which may realize the optimased route on the idea of parameters like residual energy, range of retransmissions and the distance between source and destination. The Firefly Algorithmic rule is implemented in this paper that relies on the attractiveness issue of the firefly insect to control congestion in WSN at transport layer. The results additionally show that the projected approach is best as compared to Congestion Detection and Avoidance (CODA) and Particle Swarm Optimization (PSO) on network lifetime and throughput of the network.","PeriodicalId":58712,"journal":{"name":"无线传感网络(英文)","volume":"38 1","pages":"149-156"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"无线传感网络(英文)","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.4236/WSN.2015.712013","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
Congestion in Wireless Sensor Network (WSN) is an issue of concern for several researchers in recent years. The key challenge is to develop an algorithmic rule which may realize the optimased route on the idea of parameters like residual energy, range of retransmissions and the distance between source and destination. The Firefly Algorithmic rule is implemented in this paper that relies on the attractiveness issue of the firefly insect to control congestion in WSN at transport layer. The results additionally show that the projected approach is best as compared to Congestion Detection and Avoidance (CODA) and Particle Swarm Optimization (PSO) on network lifetime and throughput of the network.