A Review on Deep Learning Models for Wireless Sensor Networks

The sensors in wireless sensor networks gather data about the objects they are used to sense. However, these sensors are limited in their performance by constraints of energy and bandwidth. Deep learning models like LSTM, CNN, RNN and KNN can help in overcoming such constraints. This review surveys deep learning models which have been used to improve the working efficiency of such networks. The emphasis is on applications and this paper also discusses directions for further research work in this area.