Advanced centralized and distributed SVM models over different IoT levels for edge layer intelligence and control
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RESEARCH PAPER
Advanced centralized and distributed SVM models over different IoT levels for edge layer intelligence and control Bhawani Shankar Pattnaik1 · Arunima Sambhuta Pattanayak1 · Siba Kumar Udgata2 · Ajit Kumar Panda3 Received: 6 February 2020 / Revised: 28 September 2020 / Accepted: 28 October 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract In this era, internet-of-things (IoT) deal with billions of edge devices potentially connected to each other. Maximum applications built on these edge devices generate a massive amount of online data and also require real-time computation and decision making with low latency (e.g., robotics/ drones, self-driving cars, smart IoT, electronics/ wearable devices). To suffice the requirement, the future generation intelligent edge devices need to be capable of computing complex machine learning algorithms on live data in real-time. Considering different layers of IoT and distributed computing concept, this paper suggests three different operational models where the ML algorithm will be executed in a distributed manner between the edge and cloud layer of IoT so that the edge node can take a decision in real-time. The three models are; model 1: training and prediction both will be done locally by the edge, model 2: training at the server and decision making at the edge node, and model 3: distributed training and distributed decision making at the edge level with global shared knowledge and security. All three models have been tested using support vector machine using thirteen diverse datasets to profile their performance in terms of both training and inference time. A comparative study between the computational performance of the edge and cloud nodes is also presented here. Through the simulated experiments using the different datasets, it is observed that, the edge node inference time is approximately ten times faster than cloud time for all tested datasets for each proposed model. At the same time, the model 2 training time is approximately nine times faster than model 1 and eleven times faster than model 3. Keywords Intelligent system · Machine learning · Edge intelligence · Centralised and distributed training · Real time prediction
1 Introduction 1.1 Background * Ajit Kumar Panda [email protected] Bhawani Shankar Pattnaik [email protected] Arunima Sambhuta Pattanayak [email protected] Siba Kumar Udgata [email protected] 1
Department of Computer Science and Engineering, National Institute of Science and Technology and student under Biju Patnaik University, Brahmapur, Orissa, India
2
School of Computer and Information Sciences, University of Hyderabad, Hyderabad, India
3
Department of Elelctronics and Communication Engineering, National Institute of Science and Technology, Berhampur, Orissa, India
Internet of things (IoT) system provides largescale sensor interfaces to monitor and control an extensive range of physical systems. The IoT systems generate massive amount of real-time data and the rate of
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