Deep learning techniques of losses in data transmitted in wirelesssensor networks


Abstract: Wireless sensor network (WSN) systems are frequently used today as a result of rapid technological developments. Wireless sensor networks, which form the basis of the Internet of Things (IoT), have a wide range of use in theworld from education to health, and from military applications to home applications. It enables the data obtained fromthe sensors to be transferred between nodes with the help of end-to-end wireless protocols. In parallel with the increasingnumber of nodes in WSN, data tra?ic density also increases. Due to the limitations of the WSN network, lost packetrates also increase with increasing data tra?ic. In this study, a data set was created by examining the data transfersof different amounts of WSN nodes placed in different places. The effects of the number of sensors and the distancebetween them were evaluated from the data set. In this study, a data set was created by collecting the data from thesensor nodes placed at 1500m x 1500m intervals in the ns-3 discrete event emulator program. Today, with the rapiddevelopment of technology, deep learning methods which are one of the artificial intelligence methods, are also used inWSN. In this study, the loss rate in the transferred data packets was tried to be estimated with the highest accuracy byusing deep belief network (DBN), recurrent neural network (RNN), and deep neural network (DNN) over the obtaineddataset. Of these three deep learning methods, DNN deep learning method was found to accurately estimate the lossrate in the transferred data packets with an accuracy rate of 88.50%.

Keywords: Wireless sensor networks (WSNs), deep learning, deep belief networks (DBNs), deep neural networks(DNNs), recurrent neural networks (RNNs)

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