{"title":"一种基于帝王蝶优化和簇头负载分配的MANET节能算法","authors":"V. T, R. Chakravarthi","doi":"10.1109/IC3IOT53935.2022.9767917","DOIUrl":null,"url":null,"abstract":"The energy consumption of mobile devices which are battery powered is increasing due to the emerging multimedia applications and tremendous traffic demands for ubiquitous access. So in mobile ad hoc networks (MANETs), Energy Efficiency (EE) is an important parameter. This paper proposes an optimum load distribution system for MANET in the head of cluster and member nodes of cluster, which decreases energy fatigue. Artificial Bee Colony algorithm which is incorporated with Monarch Butterfly Optimization algorithm is used to form the cluster and this algorithm is termed as HAMBO in association with Cluster Head Load Distribution (HAMBO-CHLD). Associative clustering is used by this proposed load distribution scheme. Associative clustering is done using the balancing energy and load factors in sensor nodes. Associative cluster heads lower the cluster head's transmission burden. The cluster's member nodes are using multi-hop communication to send data to the cluster head (CH) in a load-balanced fashion. The energy wastage in cluster are avoided using distribution of load. Using multi-hop technique, routing from clusters to Access Points (AP) is made more effective. When compared to existing systems, the experimental findings reveal that the suggested method improves energy efficiency and increases the lifetime of a network.","PeriodicalId":430809,"journal":{"name":"2022 International Conference on Communication, Computing and Internet of Things (IC3IoT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An energy efficient algorithm in MANET using Monarch Butterfly Optimization and Cluster Head Load Distribution\",\"authors\":\"V. T, R. Chakravarthi\",\"doi\":\"10.1109/IC3IOT53935.2022.9767917\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The energy consumption of mobile devices which are battery powered is increasing due to the emerging multimedia applications and tremendous traffic demands for ubiquitous access. So in mobile ad hoc networks (MANETs), Energy Efficiency (EE) is an important parameter. This paper proposes an optimum load distribution system for MANET in the head of cluster and member nodes of cluster, which decreases energy fatigue. Artificial Bee Colony algorithm which is incorporated with Monarch Butterfly Optimization algorithm is used to form the cluster and this algorithm is termed as HAMBO in association with Cluster Head Load Distribution (HAMBO-CHLD). Associative clustering is used by this proposed load distribution scheme. Associative clustering is done using the balancing energy and load factors in sensor nodes. Associative cluster heads lower the cluster head's transmission burden. The cluster's member nodes are using multi-hop communication to send data to the cluster head (CH) in a load-balanced fashion. The energy wastage in cluster are avoided using distribution of load. Using multi-hop technique, routing from clusters to Access Points (AP) is made more effective. When compared to existing systems, the experimental findings reveal that the suggested method improves energy efficiency and increases the lifetime of a network.\",\"PeriodicalId\":430809,\"journal\":{\"name\":\"2022 International Conference on Communication, Computing and Internet of Things (IC3IoT)\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-03-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Communication, Computing and Internet of Things (IC3IoT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IC3IOT53935.2022.9767917\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Communication, Computing and Internet of Things (IC3IoT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IC3IOT53935.2022.9767917","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An energy efficient algorithm in MANET using Monarch Butterfly Optimization and Cluster Head Load Distribution
The energy consumption of mobile devices which are battery powered is increasing due to the emerging multimedia applications and tremendous traffic demands for ubiquitous access. So in mobile ad hoc networks (MANETs), Energy Efficiency (EE) is an important parameter. This paper proposes an optimum load distribution system for MANET in the head of cluster and member nodes of cluster, which decreases energy fatigue. Artificial Bee Colony algorithm which is incorporated with Monarch Butterfly Optimization algorithm is used to form the cluster and this algorithm is termed as HAMBO in association with Cluster Head Load Distribution (HAMBO-CHLD). Associative clustering is used by this proposed load distribution scheme. Associative clustering is done using the balancing energy and load factors in sensor nodes. Associative cluster heads lower the cluster head's transmission burden. The cluster's member nodes are using multi-hop communication to send data to the cluster head (CH) in a load-balanced fashion. The energy wastage in cluster are avoided using distribution of load. Using multi-hop technique, routing from clusters to Access Points (AP) is made more effective. When compared to existing systems, the experimental findings reveal that the suggested method improves energy efficiency and increases the lifetime of a network.