EDGF: Empirical dataset generation framework for wireless sensor networks
Küçük Resim Yok
Tarih
2021
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
In 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.
Açıklama
Anahtar Kelimeler
Dataset Generation, Random Deployment, Wireless Sensor Network, Clustering, Traffic Data, Steiner Tree, Algorithm, Protocol, Mac
Kaynak
Computer Communications
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
180
