Walaa Abd Ellatief, O. Younes, Hatem Ahmed, Mohee Hadhoud
{"title":"Transmission range adaption technique for non-uniform wireless sensor network topology extraction","authors":"Walaa Abd Ellatief, O. Younes, Hatem Ahmed, Mohee Hadhoud","doi":"10.1109/IACS.2016.7476104","DOIUrl":null,"url":null,"abstract":"Wireless sensor networks are characterized with large number of nodes deployed randomly over the network area. In many real applications, random deployment process produce non-uniform distribution of nodes. This means that there is no constant density over any unit area and therefore no constant number of neighbours for each node. With non-uniform distribution, network appear as if it divided to a set of sub-regions each with a different density level. Density in these sub-regions ranging from high density areas, medium, and others are empty areas. For this reason, topology extraction techniques is needed for sensors which helps to discover the layout of the network around them. It assists to figure the skeleton of the whole network. It can help in the discovery of holes and used to solve this problem as a guide for redeployment process to produce a full covered area. Our aim is to define a simple distributed technique that allow all sensors in the network to share information between them and extract the layout of the network. This is done by defining the closed boundary of sub-regions of different density levels which form the network. Previous techniques used for topology extraction need networks with very high density and deal with special deployment figures. Many of them requires uniform distribution which is not always applicable in real situations. Our proposed technique is simple, use lower density than other previously proposed techniques, and do not need special deployment figures.","PeriodicalId":6579,"journal":{"name":"2016 7th International Conference on Information and Communication Systems (ICICS)","volume":"17 1","pages":"162-167"},"PeriodicalIF":0.0000,"publicationDate":"2016-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 7th International Conference on Information and Communication Systems (ICICS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IACS.2016.7476104","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
Wireless sensor networks are characterized with large number of nodes deployed randomly over the network area. In many real applications, random deployment process produce non-uniform distribution of nodes. This means that there is no constant density over any unit area and therefore no constant number of neighbours for each node. With non-uniform distribution, network appear as if it divided to a set of sub-regions each with a different density level. Density in these sub-regions ranging from high density areas, medium, and others are empty areas. For this reason, topology extraction techniques is needed for sensors which helps to discover the layout of the network around them. It assists to figure the skeleton of the whole network. It can help in the discovery of holes and used to solve this problem as a guide for redeployment process to produce a full covered area. Our aim is to define a simple distributed technique that allow all sensors in the network to share information between them and extract the layout of the network. This is done by defining the closed boundary of sub-regions of different density levels which form the network. Previous techniques used for topology extraction need networks with very high density and deal with special deployment figures. Many of them requires uniform distribution which is not always applicable in real situations. Our proposed technique is simple, use lower density than other previously proposed techniques, and do not need special deployment figures.