S. Abbas, Z. Raza, Nida Siddiqui, Faheem Khan, T. Whangbo
{"title":"Edge Metric Dimension of Honeycomb and Hexagonal Networks for IoT","authors":"S. Abbas, Z. Raza, Nida Siddiqui, Faheem Khan, T. Whangbo","doi":"10.32604/cmc.2022.023003","DOIUrl":null,"url":null,"abstract":": Wireless Sensor Network (WSN) is considered to be one of the fundamental technologies employed in the Internet of things (IoT); hence, enabling diverse applications for carrying out real-time observations. Robot navigation in such networks was the main motivation for the introduction of the concept of landmarks. A robot can identify its own location by sending signals to obtain the distances between itself and the landmarks. Considering networks to be a type of graph, this concept was redefined as metric dimension of a graph which is the minimum number of nodes needed to identify all the nodes of the graph. This idea was extended to the concept of edge metric dimension of a graph G , which is the minimum number of nodes needed in a graph to uniquely identify each edge of the network. Regular plane networks can be easily constructed by repeating regular polygons. This design is of extreme importance as it yields high overall performance; hence, it can be used in various networking and IoT domains. The honeycomb and the hexagonal networks are two such popular mesh-derived parallel networks. In this paper, it is proved that the minimum landmarks required for the honeycomb network HC ( n ), and the hexagonal network HX ( n ) are 3 and 6 respectively. The bounds for the landmarks required for the hex-derived network HDN 1( n ) are also proposed.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"14 1","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cmc-computers Materials & Continua","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.32604/cmc.2022.023003","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 3
Abstract
: Wireless Sensor Network (WSN) is considered to be one of the fundamental technologies employed in the Internet of things (IoT); hence, enabling diverse applications for carrying out real-time observations. Robot navigation in such networks was the main motivation for the introduction of the concept of landmarks. A robot can identify its own location by sending signals to obtain the distances between itself and the landmarks. Considering networks to be a type of graph, this concept was redefined as metric dimension of a graph which is the minimum number of nodes needed to identify all the nodes of the graph. This idea was extended to the concept of edge metric dimension of a graph G , which is the minimum number of nodes needed in a graph to uniquely identify each edge of the network. Regular plane networks can be easily constructed by repeating regular polygons. This design is of extreme importance as it yields high overall performance; hence, it can be used in various networking and IoT domains. The honeycomb and the hexagonal networks are two such popular mesh-derived parallel networks. In this paper, it is proved that the minimum landmarks required for the honeycomb network HC ( n ), and the hexagonal network HX ( n ) are 3 and 6 respectively. The bounds for the landmarks required for the hex-derived network HDN 1( n ) are also proposed.
期刊介绍:
This journal publishes original research papers in the areas of computer networks, artificial intelligence, big data management, software engineering, multimedia, cyber security, internet of things, materials genome, integrated materials science, data analysis, modeling, and engineering of designing and manufacturing of modern functional and multifunctional materials.
Novel high performance computing methods, big data analysis, and artificial intelligence that advance material technologies are especially welcome.