EDGF: Empirical dataset generation framework for wireless sensor networks

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dc.authoridDonta, Praveen Kumar/0000-0002-8233-6071
dc.authoridCengiz, Korhan/0000-0001-6594-8861
dc.authorwosidDonta, Praveen Kumar/H-3021-2017
dc.authorwosidSah, Dinesh/A-4912-2018
dc.authorwosidCengiz, Korhan/HTN-8060-2023
dc.contributor.authorSah, Dinesh Kumar
dc.contributor.authorCengiz, Korhan
dc.contributor.authorDonta, Praveen Kumar
dc.contributor.authorInukollu, Venkata N.
dc.contributor.authorAmgoth, Tarachand
dc.date.accessioned2024-06-12T10:59:09Z
dc.date.available2024-06-12T10:59:09Z
dc.date.issued2021
dc.departmentTrakya Üniversitesien_US
dc.description.abstractIn wireless sensor networks (WSNs), simulation practices, system models, algorithms, and protocols have been published worldwide based on the assumption of randomness. The applied statistics used for randomness in WSNs are broad, e.g., random deployment, activity tracking, packet generation, etc. Even though authors' adequate formal and informal information and pledge validation of the proposal became challenging, the minuscule information alteration in implementation and validation can reflect the enormous effect on eventual results. In this proposal, we show how the results are affected by the generalized assumption made on randomness. In sensor node deployment, ambiguity arises due to node error-value (epsilon), and its upper bound in the relative position is estimated to understand the delicacy of diminutives changes. Besides, the effect of uniformity in the traffic and participation of scheduling position of nodes is also generalized. We propose an algorithm to generate the unified dataset for the general and some specific applications system models in WSNs. The results produced by our algorithm reflect the pseudo-randomness and can efficiently regenerate through seed value for validation.en_US
dc.identifier.doi10.1016/j.comcom.2021.08.017
dc.identifier.endpage56en_US
dc.identifier.issn0140-3664
dc.identifier.issn1873-703X
dc.identifier.scopus2-s2.0-85114999931en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage48en_US
dc.identifier.urihttps://doi.org/10.1016/j.comcom.2021.08.017
dc.identifier.urihttps://hdl.handle.net/20.500.14551/20342
dc.identifier.volume180en_US
dc.identifier.wosWOS:000704054300004en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofComputer Communicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDataset Generationen_US
dc.subjectRandom Deploymenten_US
dc.subjectWireless Sensor Networken_US
dc.subjectClusteringen_US
dc.subjectTraffic Dataen_US
dc.subjectSteiner Treeen_US
dc.subjectAlgorithmen_US
dc.subjectProtocolen_US
dc.subjectMacen_US
dc.titleEDGF: Empirical dataset generation framework for wireless sensor networksen_US
dc.typeArticleen_US

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