{"title":"基于遗传算法的移动无线自组网节点移动策略","authors":"Qi Wan, Bingqing Han","doi":"10.1109/ISCEIC53685.2021.00017","DOIUrl":null,"url":null,"abstract":"Mobility is the most distinctive and important feature of Mobile Wireless Ad Hoc Networks(MANET), which brings many limitations along with its wide applications. A good mobility model is a good basis for further improving the performance of routing layer and MAC layer. Therefore, a movement strategy is proposed for the node movement problem. Only those nodes that are poorly distributed and meet the conditions are selected to form the set of mobile nodes. Neighborhood distribution information and residual energy information are fully considered for improving the initial population and fitness function of the genetic algorithm. Then, the improved genetic algorithm is used to search for the optimal next mobile position for each node in the set. The simulation results show that the nodes in the network can achieve higher network coverage and connectivity with fewer moves, regardless of whether the nodes are well or poorly distributed.","PeriodicalId":342968,"journal":{"name":"2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Mobile Strategy for Nodes in Mobile Wireless Ad Hoc Networks Based on Genetic Algorithm\",\"authors\":\"Qi Wan, Bingqing Han\",\"doi\":\"10.1109/ISCEIC53685.2021.00017\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Mobility is the most distinctive and important feature of Mobile Wireless Ad Hoc Networks(MANET), which brings many limitations along with its wide applications. A good mobility model is a good basis for further improving the performance of routing layer and MAC layer. Therefore, a movement strategy is proposed for the node movement problem. Only those nodes that are poorly distributed and meet the conditions are selected to form the set of mobile nodes. Neighborhood distribution information and residual energy information are fully considered for improving the initial population and fitness function of the genetic algorithm. Then, the improved genetic algorithm is used to search for the optimal next mobile position for each node in the set. The simulation results show that the nodes in the network can achieve higher network coverage and connectivity with fewer moves, regardless of whether the nodes are well or poorly distributed.\",\"PeriodicalId\":342968,\"journal\":{\"name\":\"2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCEIC53685.2021.00017\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCEIC53685.2021.00017","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Mobile Strategy for Nodes in Mobile Wireless Ad Hoc Networks Based on Genetic Algorithm
Mobility is the most distinctive and important feature of Mobile Wireless Ad Hoc Networks(MANET), which brings many limitations along with its wide applications. A good mobility model is a good basis for further improving the performance of routing layer and MAC layer. Therefore, a movement strategy is proposed for the node movement problem. Only those nodes that are poorly distributed and meet the conditions are selected to form the set of mobile nodes. Neighborhood distribution information and residual energy information are fully considered for improving the initial population and fitness function of the genetic algorithm. Then, the improved genetic algorithm is used to search for the optimal next mobile position for each node in the set. The simulation results show that the nodes in the network can achieve higher network coverage and connectivity with fewer moves, regardless of whether the nodes are well or poorly distributed.