Energy efficient compressive sensing with predictive model for IoT based medical data transmission
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ORIGINAL RESEARCH
Energy efficient compressive sensing with predictive model for IoT based medical data transmission R. Bharathi1 · T. Abirami2 Received: 11 August 2020 / Accepted: 3 November 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
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. Keywords Internet of Things · Smart healthcare · Deep learning · Compressive sensing · Energy efficiency
1 Introduction Cloud computing and Internet of Things (IoT) are generally integrated together and finds its applicability in diverse situations. At the same time, cloud based Internet of things (CoT) suffers from various challenges like communication delay, bandwidth and energy efficiency. For example, transmission of an individual bit of data through cellular network spends more amount of energy and thereby decreases the IoT system lifetime. Besides, edge computing has grown as an important area which links the cloud services to the edge of the network. Edge computing is found to be effective compared to cloud systems in diverse IoT based application scenarios (Shi et al. 2016). For example, real time sensitive * R. Bharathi [email protected] T. Abirami [email protected] 1
Department of Computer Science and Engineering, Cheran College of Engineering, K. Paramathi, Karur 639111, India
Department of Information Technology, Kongu Engineering College, Perundurai, Erode, India
2
applications like driverless cars and e-health does not operate effectively because of maximum latency and poor bandwidth caused by numerous sensors linked into the network. In general, Wireless Sensor Network (WSN), wearable andWireless Body Sensor Network tend to develop required segments of IoT structures. Various smart devicesprocesses several operations like
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