{"title":"基于蚂蚁狮子优化算法的水声传感器网络能量感知节点部署","authors":"R. S. Kumar, G. Sivaradje","doi":"10.1109/ICSTSN57873.2023.10151482","DOIUrl":null,"url":null,"abstract":"underwater wireless sensor networks were created as a result of the spread of wireless sensor networks. Because of its extensive and in-the-moment applications, it has had a significant impact on research. However, there are numerous challenges to successfully implementing underwater wireless sensor networks. The issue of energy depletion in sensor nodes is the biggest concern in the underwater sensor network. In this article, the Ant Lion optimization algorithm (ALOA) is employed to extend the lifespan of the underwater wireless sensor network, collect information from inner sub-cluster sensor nodes, and shorten the relay nodes’ multi-hop transmission distance. The undersea network area is modeled as a set of three-dimensional concentric cylinders with many stages. Each stage is also separated into a number of blocks, each of which represents a single cluster. The suggested algorithm operates in a bottom-up Vertical communication from the ocean floor to the surface. The communication problems caused by strong water pressure toward the sea floor are resolved by using many levels of differing heights. Simulations are run to demonstrate the effectiveness of the suggested approach, which performs better in terms of throughput of 297.99 kbps, PDR of 46.34%, energy usage of 13%, and packet loss of 165.6 kbps.","PeriodicalId":325019,"journal":{"name":"2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Energy Aware Node deployment using Ant Lion optimization algorithm in underwater acoustic sensor Network\",\"authors\":\"R. S. Kumar, G. Sivaradje\",\"doi\":\"10.1109/ICSTSN57873.2023.10151482\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"underwater wireless sensor networks were created as a result of the spread of wireless sensor networks. Because of its extensive and in-the-moment applications, it has had a significant impact on research. However, there are numerous challenges to successfully implementing underwater wireless sensor networks. The issue of energy depletion in sensor nodes is the biggest concern in the underwater sensor network. In this article, the Ant Lion optimization algorithm (ALOA) is employed to extend the lifespan of the underwater wireless sensor network, collect information from inner sub-cluster sensor nodes, and shorten the relay nodes’ multi-hop transmission distance. The undersea network area is modeled as a set of three-dimensional concentric cylinders with many stages. Each stage is also separated into a number of blocks, each of which represents a single cluster. The suggested algorithm operates in a bottom-up Vertical communication from the ocean floor to the surface. The communication problems caused by strong water pressure toward the sea floor are resolved by using many levels of differing heights. Simulations are run to demonstrate the effectiveness of the suggested approach, which performs better in terms of throughput of 297.99 kbps, PDR of 46.34%, energy usage of 13%, and packet loss of 165.6 kbps.\",\"PeriodicalId\":325019,\"journal\":{\"name\":\"2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSTSN57873.2023.10151482\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSTSN57873.2023.10151482","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Energy Aware Node deployment using Ant Lion optimization algorithm in underwater acoustic sensor Network
underwater wireless sensor networks were created as a result of the spread of wireless sensor networks. Because of its extensive and in-the-moment applications, it has had a significant impact on research. However, there are numerous challenges to successfully implementing underwater wireless sensor networks. The issue of energy depletion in sensor nodes is the biggest concern in the underwater sensor network. In this article, the Ant Lion optimization algorithm (ALOA) is employed to extend the lifespan of the underwater wireless sensor network, collect information from inner sub-cluster sensor nodes, and shorten the relay nodes’ multi-hop transmission distance. The undersea network area is modeled as a set of three-dimensional concentric cylinders with many stages. Each stage is also separated into a number of blocks, each of which represents a single cluster. The suggested algorithm operates in a bottom-up Vertical communication from the ocean floor to the surface. The communication problems caused by strong water pressure toward the sea floor are resolved by using many levels of differing heights. Simulations are run to demonstrate the effectiveness of the suggested approach, which performs better in terms of throughput of 297.99 kbps, PDR of 46.34%, energy usage of 13%, and packet loss of 165.6 kbps.