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Title: Neural network based instant parameter prediction for wireless sensor network optimization models
Authors: Akbaş, Ayhan
Yıldız, Hüseyin Uğur
Özbayoğlu, Ahmet Murat
Tavlı, Bülent
Keywords: Wireless sensor networks
neural networks
multi-layer perceptron
maximum lifetime
lifetime prediction
transmission power level
internode distance
Issue Date: Aug-2019
Publisher: Springer New York LLC
Source: Akbas, A., Yildiz, H. U., Ozbayoglu, A. M., and Tavli, B. (2019). Neural network based instant parameter prediction for wireless sensor network optimization models. Wireless Networks, 25(6), 3405-3418.
Abstract: Optimal operation configuration of a Wireless Sensor Network (WSN) can be determined by utilizing exact mathematical programming techniques such as Mixed Integer Programming (MIP). However, computational complexities of such techniques are high. As a remedy, learning algorithms such as Neural Networks (NNs) can be utilized to predict the WSN settings with high accuracy with much lower computational cost than the MIP solutions. We focus on predicting network lifetime, transmission power level, and internode distance which are interrelated WSN parameters and are vital for optimal WSN operation. To facilitate an efficient solution for predicting these parameters without explicit optimizations, we built NN based models employing data obtained from an MIP model. The NN based scalable prediction model yields a maximum of 3% error for lifetime, 6% for transmission power level error, and internode distances within an accuracy of 3m in prediction outcomes.
ISSN: 1022-0038
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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