Balkan Journal of Electrical and Computer Engineering 2147-284X 2147-284X Balkan Yayın 10.17694/bajece.1281060 Artificial Intelligence Yapay Zeka Deep Belief Network Based Wireless Sensor Network Connectivity Analysis https://orcid.org/0000-0002-6425-104X Akbaş Ayhan ABDULLAH GÜL ÜNİVERSİTESİ https://orcid.org/0000-0001-7844-3168 Buyrukoğlu Selim ÇANKIRI KARATEKİN ÜNİVERSİTESİ 08 21 2023 11 3 262 266 04 11 2023 07 03 2023 Copyright © 2013, Balkan Journal of Electrical and Computer Engineering 2013 Balkan Journal of Electrical and Computer Engineering

Wireless sensor networks (WSNs) are widely used in various fields, and their deployment is critical to ensure area coverage and full network connectivity to achieve the maximum network lifetime. In this study, we present a mixed-integer programming (MIP) model that deeply investigates deployment parameters to optimize lifetime and analyze network connectivity. We further analyze the obtained results using Deep Belief Network (DBN) and Deep Neural Network (DNN) algorithms to achieve higher accuracy rates. Our evaluation shows that the DBN outperforms the DNN with an accuracy rate of 81.2%, precision of 81.2%, recall of 99.1%, and an F1-Score of 0.78. We also utilize two different datasets to justify the efficiency of the DBN in this research. The findings of this study emphasize the validity of our DBN algorithm and encourage further research into lifetime optimization and connectivity analysis in WSNs.

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