Publication:
Novel random models of entity mobility models and performance analysis of random entity mobility models

dc.contributor.authorBilgin, Metin
dc.contributor.buuauthorBilgin, Metin
dc.contributor.buuauthorBİLGİN, METİN
dc.contributor.departmentMühendislik Fakültesi
dc.contributor.departmentBilgisayar Mühendisliği Bölümü
dc.contributor.researcheridAAH-2049-2021
dc.date.accessioned2024-07-02T07:55:07Z
dc.date.available2024-07-02T07:55:07Z
dc.date.issued2020-01-01
dc.description.abstractIt has become possible to collect data from geographically large areas with smart devices that are prevalently used today. Sensors that are integrated into smart devices make it possible for these devices to receive and transmit data wirelessly. The most important problem of this model that is known as mobile crowd sensing and that allows inferences on the data obtained from its users is lack of data. The main reason for this problem is the lack of sufficient usage of the sensors on devices by the user. To increase the amount of data collected, while users may be incentivized in various ways, the amount and accuracy of the collected data may be increased by developing random entity mobility models (REMMs). In this study, two new models (random point and random journey) were proposed as alternatives to existing REMMs. In the experiment environment that was created to measure the performances of the proposed models, their performances were compared to those that are currently used prevalently (random waypoint (RWP), random walk (RW), and random direction (RD)). In the experiment environment, the performances were compared in terms of three different metrics (visiting rates of nodes, rates of reaching the basis, and the number of messages they carried to the basis). The greatest increase in differently sized areas and at different numbers of nodes in the RP model in terms of rates of reaching the basis was 2.6% compared to RWP, 7% compared to RW, and 46.34% compared to RD, while these values for the number of nodes that were visited were 3% compared to RWP, 1.5% compared to RW, and 17.67% compared to RD. In the same conditions in terms of the metric on the number of messages, the model collected 1465.4, 2933.46, and 7260.12 more messages than those in respectively RWP, RW, and RD. The greatest increase in differently sized areas and at different numbers of nodes in the RJ model in terms of reaching the basis was 1% compared to RWP, 3.5% compared to RW, and 25% compared to RD, while these values for the number of nodes that were visited were 0.75% compared to RWP, 2% compared to RW, and 21.4% compared to RD. In the same conditions in terms of the metric on the number of messages, the model collected 1109.56, 1534.26, and 4488.5 more messages than those in RWP, RW, and RD, respectively.
dc.identifier.doi10.3906/elk-1904-102
dc.identifier.endpage726
dc.identifier.issn1300-0632
dc.identifier.issue2
dc.identifier.startpage708
dc.identifier.urihttps://doi.org/10.3906/elk-1904-102
dc.identifier.urihttps://hdl.handle.net/11452/42710
dc.identifier.volume28
dc.identifier.wos000522447800009
dc.indexed.wosWOS.SCI
dc.language.isoen
dc.publisherTubitak Scientific & Technological Research Council Turkey
dc.relation.journalTurkish Journal Of Electrical Engineering And Computer Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectRandom entity mobility model
dc.subjectModeling and simulation
dc.subjectAnalysis of mobile networks
dc.subjectMobile crowd sensing
dc.subjectSensor
dc.subjectConnectivity
dc.subjectMobile sensor networks
dc.subjectScience & technology
dc.subjectTechnology
dc.subjectComputer science, artificial intelligence
dc.subjectEngineering, electrical & electronic
dc.subjectComputer science
dc.subjectEngineering
dc.titleNovel random models of entity mobility models and performance analysis of random entity mobility models
dc.typeArticle
dspace.entity.typePublication
local.contributor.departmentMühendislik Fakültesi/Bilgisayar Mühendisliği Bölümü
relation.isAuthorOfPublicationcf59076b-d88e-4695-a08c-b06b98b4e25a
relation.isAuthorOfPublication.latestForDiscoverycf59076b-d88e-4695-a08c-b06b98b4e25a

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