Deep Belief Network Based Wireless Sensor Network Connectivity Analysis

Authors : Ayhan AKBAŞ, Selim BUYRUKOĞLU

Pages : 262-266

Doi:10.17694/bajece.1281060

View : 31 | Download : 33

Publication Date : 2023-08-21

Article Type : Research Paper

Abstract :Wireless sensor networks insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(MIP); model that deeply investigates deployment parameters to optimize lifetime and analyze network connectivity. We further analyze the obtained results using Deep Belief Network insert ignore into journalissuearticles values(DBN); and Deep Neural Network insert ignore into journalissuearticles values(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.
Keywords : Deep Belief Network, Wireless Sensor Network, Connectivity