Deep learning with LSTM based distributed data mining model for energy efficient wireless sensor networks

Wireless sensor network (WSN) comprises a collection of sensor nodes employed to monitor and record the status of the physical environment and organize the gathered data at a central location. This paper presents a deep learning based distributed data mining (DDM) model to achieve energy efficiency and optimal load balancing at the fusion center of WSN. The presented DMM model includes a recurrent neural network (RNN) based long short-term memory (LSTM) called RNN-LSTM, which divides the network into various layers and place them into the sensor nodes. The proposed model reduces the overhead at the fusion center along with a reduction in the number of data transmission. The presented RNN-LSTM model is tested under a wide set of experimentation with varying number of hidden layer nodes and signaling intervals. At the same time, the amount of energy needed to transmit data by RNN-LSTM model is considerably lower than energy needed to transmit actual data. The simulation results indicated that the RNN-LSTM reduces the signaling overhead, average delay and maximizes the overall throughput compared to other methods. It is noted that under the signaling interval of 240 ms, it can be shown that the RNN-LSTM achieves a minimum average delay of 190 ms whereas the OSPF and DNN models shows average delay of 230 ms and 230 ms respectively. (C) 2020 Elsevier B.V. All rights reserved.