{"title":"基于遗传算法的群检测数据网络健康率优化","authors":"A. R. Suhas, M. Manoj Priyatham","doi":"10.1142/s0219265922410018","DOIUrl":null,"url":null,"abstract":"A physical region can have multiple parts, each part is monitored with the help of a Special DDN (SDDN). In the existing methods, namely, LEACH, the Fuzzy method has a larger path between the initiator DDN to destination DDN. Non-healthy DDNs can occur in the Group-based Detection Data Network (GDDN) when the battery level of the DDN reaches below the threshold. The possibility of more Non-healthy DDNs can be of multiple reasons (i) when the link path is of larger length (ii) Same DDN is used multiple times as an SDDN and (iii) repeated communication between base station to DDNs causes the DDN to lose more battery. If a mechanism is created to recover the DDNs or recharge them, then the number of Non-healthy DDNs can be reduced and DDN performance can be improved a lot. The Proposed Genetic (PGENETIC) method will find the SDDN in a battery-aware manner and also at path will be of minimum length along with regular interval trigger to identify DDNs which are non-healthy and replace or recharge them. PGENETIC is compared with LEACH, Fuzzy method, and Proposed CHEF (PCHEF) and proved that PGENETIC exhibits better performance.","PeriodicalId":53990,"journal":{"name":"JOURNAL OF INTERCONNECTION NETWORKS","volume":"1 1","pages":""},"PeriodicalIF":0.5000,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Health Ratio Optimization of Group Detection-Based Data Network Using Genetic Algorithm\",\"authors\":\"A. R. Suhas, M. Manoj Priyatham\",\"doi\":\"10.1142/s0219265922410018\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A physical region can have multiple parts, each part is monitored with the help of a Special DDN (SDDN). In the existing methods, namely, LEACH, the Fuzzy method has a larger path between the initiator DDN to destination DDN. Non-healthy DDNs can occur in the Group-based Detection Data Network (GDDN) when the battery level of the DDN reaches below the threshold. The possibility of more Non-healthy DDNs can be of multiple reasons (i) when the link path is of larger length (ii) Same DDN is used multiple times as an SDDN and (iii) repeated communication between base station to DDNs causes the DDN to lose more battery. If a mechanism is created to recover the DDNs or recharge them, then the number of Non-healthy DDNs can be reduced and DDN performance can be improved a lot. The Proposed Genetic (PGENETIC) method will find the SDDN in a battery-aware manner and also at path will be of minimum length along with regular interval trigger to identify DDNs which are non-healthy and replace or recharge them. PGENETIC is compared with LEACH, Fuzzy method, and Proposed CHEF (PCHEF) and proved that PGENETIC exhibits better performance.\",\"PeriodicalId\":53990,\"journal\":{\"name\":\"JOURNAL OF INTERCONNECTION NETWORKS\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.5000,\"publicationDate\":\"2022-05-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JOURNAL OF INTERCONNECTION NETWORKS\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1142/s0219265922410018\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, THEORY & METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JOURNAL OF INTERCONNECTION NETWORKS","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/s0219265922410018","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
摘要
一个物理区域可以包含多个部分,每个部分通过SDDN (Special DDN)进行监控。在现有的方法中,即LEACH,模糊方法在发起者DDN到目的DDN之间的路径更大。当GDDN (Group-based Detection Data Network)的电池电量低于阈值时,可能会出现非健康DDNs。出现更多非健康DDN的可能性有多种原因:(i)链路路径长度较大;(ii)同一DDN作为SDDN多次使用;(iii)基站与DDN之间的重复通信导致DDN消耗更多电池。如果建立恢复DDN或为其充值的机制,则可以减少非健康DDN的数量,并大大提高DDN的性能。提出的遗传(PGENETIC)方法将以电池感知的方式找到SDDN,并且在路径长度最小的情况下,以及定期间隔触发来识别非健康ddn并替换或充电。将PGENETIC算法与LEACH、Fuzzy、Proposed CHEF (PCHEF)算法进行了比较,证明了PGENETIC算法具有更好的性能。
Health Ratio Optimization of Group Detection-Based Data Network Using Genetic Algorithm
A physical region can have multiple parts, each part is monitored with the help of a Special DDN (SDDN). In the existing methods, namely, LEACH, the Fuzzy method has a larger path between the initiator DDN to destination DDN. Non-healthy DDNs can occur in the Group-based Detection Data Network (GDDN) when the battery level of the DDN reaches below the threshold. The possibility of more Non-healthy DDNs can be of multiple reasons (i) when the link path is of larger length (ii) Same DDN is used multiple times as an SDDN and (iii) repeated communication between base station to DDNs causes the DDN to lose more battery. If a mechanism is created to recover the DDNs or recharge them, then the number of Non-healthy DDNs can be reduced and DDN performance can be improved a lot. The Proposed Genetic (PGENETIC) method will find the SDDN in a battery-aware manner and also at path will be of minimum length along with regular interval trigger to identify DDNs which are non-healthy and replace or recharge them. PGENETIC is compared with LEACH, Fuzzy method, and Proposed CHEF (PCHEF) and proved that PGENETIC exhibits better performance.
期刊介绍:
The Journal of Interconnection Networks (JOIN) is an international scientific journal dedicated to advancing the state-of-the-art of interconnection networks. The journal addresses all aspects of interconnection networks including their theory, analysis, design, implementation and application, and corresponding issues of communication, computing and function arising from (or applied to) a variety of multifaceted networks. Interconnection problems occur at different levels in the hardware and software design of communicating entities in integrated circuits, multiprocessors, multicomputers, and communication networks as diverse as telephone systems, cable network systems, computer networks, mobile communication networks, satellite network systems, the Internet and biological systems.