{"title":"Novel Geodetic Fuzzy Subgraph-Based Ranking for Congestion Control in RPL-IoT Network","authors":"Mohamed Sithik, Muthu Kumar","doi":"10.17559/tv-20220829132003","DOIUrl":null,"url":null,"abstract":": Congestion control is among the most challenging tasks in enhancing QoS in the Internet of Things (IoT). Currently, wireless networks are able to have a large number of connections but with a limited amount of network resources. Consequently, congestion occurs, which adversely affects throughput, transmission delay, packet losses, power consumption management, and the lifespan of a network. This is certainly relevant in networks where transmissions are controlled by the Routing Protocol for Low-Power and Lossy Networks (RPL), which is commonly employed in the Internet of Things network. To solve this problem, a novel Geodetic fuzzy subgraph-based ranking (GFSR-RPL) for congestion control is proposed. Initially, the proposed GFSR-RPL selects the cluster head using K-means clustering. Then the rank calculation can be done via the final route setting for data transmission. A route setup scheme consists of three elements: 1) a Round Trip Time (RTT) estimator that assesses congestion conditions in a variety of ways; 2) a trend and relative strength indicator analysis; and 3) a geodetic fuzzy subgraph rank calculation method that calculates initial RTO (initial retransmission timeouts) accurately. The proposed GFSR-RPL method reduces the energy consumption of up to 43.58%, 25.8%, 14.82% and 6.85% than existing methods such as RPR, CBR-RPL, ACW and ECLRPL.","PeriodicalId":510054,"journal":{"name":"Tehnicki vjesnik - Technical Gazette","volume":"11 8","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tehnicki vjesnik - Technical Gazette","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17559/tv-20220829132003","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
: Congestion control is among the most challenging tasks in enhancing QoS in the Internet of Things (IoT). Currently, wireless networks are able to have a large number of connections but with a limited amount of network resources. Consequently, congestion occurs, which adversely affects throughput, transmission delay, packet losses, power consumption management, and the lifespan of a network. This is certainly relevant in networks where transmissions are controlled by the Routing Protocol for Low-Power and Lossy Networks (RPL), which is commonly employed in the Internet of Things network. To solve this problem, a novel Geodetic fuzzy subgraph-based ranking (GFSR-RPL) for congestion control is proposed. Initially, the proposed GFSR-RPL selects the cluster head using K-means clustering. Then the rank calculation can be done via the final route setting for data transmission. A route setup scheme consists of three elements: 1) a Round Trip Time (RTT) estimator that assesses congestion conditions in a variety of ways; 2) a trend and relative strength indicator analysis; and 3) a geodetic fuzzy subgraph rank calculation method that calculates initial RTO (initial retransmission timeouts) accurately. The proposed GFSR-RPL method reduces the energy consumption of up to 43.58%, 25.8%, 14.82% and 6.85% than existing methods such as RPR, CBR-RPL, ACW and ECLRPL.