Performance Comparison of Various Wireless Sensor Network Dataset using Deep Learning Classifications

Deep Learning is the subset of artificial intelligence and various techniques are available to predict the performance of real time applications. Wireless devices are available to access the devices from multiple places based environment and coverage area. Wireless sensor network (WSN) tools are positioned in multiple places which has sense the information from the substances. The performance is the major factor to facilitate the bandwidth and power. In this paper to analyse the wireless sensor network performance using deep learning techniques. Here to measure the performance indicators such as accuracy, precision and score function of sensor dataset using TensorFlow. The deep learning models such as convolutional neural networks, recurrent neural network and k-means neural network performance factors are classified by using sensory dataset. The accuracy factor is obtained as 96% and compares this with existing models.