Energy efficient compressive sensing with predictive model for IoT based medical data transmission

Abstract

Internet of Things (IoT) systems tends to produce massive and diverse kinds of data which needs to be processes and responds in a smallperiod. A most important challenge exists in IoT devices is the amount of energy utilization while transmitting data into cloud. This paper presents a new energy efficient compressive technique with predictive model for IoT based medical data collection and transmission. The proposed model make use of Sensor-Lempel Ziv Welch (SLZW) technique is utilized to perform compressive sensing earlier to data transmission followed by particle swarm optimization (PSO) based deep neural network (DNN) based prediction. The PSO algorithm is applied for optimizing the node count of hidden layer in DNN due to the issue that the classical DNN got trapped into local minima and the node count in hidden layer have to select manually. The performance of the presented SLZW-PSO-DNN algorithm has been validated and the results are investigated under distinct scenarios. The obtained experimental outcome indicated that the SLZW-PSO-DNN algorithm is found to be effective under several aspects over the existing method. The experimental results stated that the PSO-DNN model has resulted in a maximum predictive average accuracy of 98.5% and 98.4% under original and compressed data respectively.