Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6613
Title: Efficient computation of wireless sensor network lifetime through deep neural networks
Authors: Yılmaz, Muhammed
Özbayoğlu, Ahmet Murat
Tavlı, Bülent
Keywords: Wireless sensor networks
Network lifetime
Lifetime prediction
Machine learning
Deep neural networks
Multi-layer perceptron
Issue Date: 2021
Publisher: Springer
Abstract: The most important quality-of-service metric for wireless sensor networks (WSNs), arguably, is the lifetime. Estimating the network lifetime under optimal operation conditions can be done by casting the problem as a mixed integer programming (MIP) model and solving the problem instances to optimality. Yet, solution times of such models are excessively high. Therefore, it is not possible to work with large problem instances within an acceptable solution time. Adopting learning based algorithms has the ability to produce near-optimal results much more rapidly in comparison to MIP models. In this study, we propose a deep neural network (DNN) based model to determine the WSN lifetime near-optimally virtually instantly. The proposed model is able to predict the lifetime of a randomly deployed WSN over a predetermined area with an average accuracy more than 98.5%. An interesting outcome of the study is that the DNN based model is able to estimate the lifetime of WSNs with higher number of nodes successfully even if it is trained with a dataset obtained with lower number of nodes.
URI: https://doi.org/10.1007/s11276-021-02556-8
https://hdl.handle.net/20.500.11851/6613
ISSN: 1022-0038
1572-8196
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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