Title: Computation offloading using K-nearest neighbour time critical optimisation algorithm in fog computing

Authors: Ashwini Kumar Jha; Minal P. Patel; Tanmay D. Pawar

Addresses: Gujarat Technological University, Chandkheda, Ahmedabad, Gujarat, India ' Computer Engineering Department, Devang Patel Institute of Advance Technology and Research, CHARUSAT, Anand, Gujarat, India ' Electronics Department, Birla Vishvakarma Mahavidyalaya, Vallabh Vidyanagar, Anand, Gujarat, India

Abstract: The wide range of IoT devices and wireless devices used in healthcare, hospitals and enterprises generates a large volume of digital data that must be processed, analysed and stored. Owing to the small processing capacity of these devices, the data generated cannot be processed on-board. Therefore, we suggest offloading this data to an efficient server. Time-critical applications cannot rely on the availability of cloud servers since they are in a remote location. The paper examines algorithms such as Deep Reinforcement Learning for Online Computation Offloading (DROO), coordinate descent, adaptive boosting, and then implements the K-nearest neighbour time critical optimisation algorithm as a fog offloading network topology. The offloading decision is based on the cost function, which includes latency, memory consumption and model accuracy. The topology implementing K-NN can be trained quickly and offers almost 99% accuracy when it comes to data offloading. Based on the comparative analysis, it excels over other machine learning approaches.

Keywords: fog computing; edge computing; computation offloading; cloud computing; K-nearest neighbour.

DOI: 10.1504/IJWMC.2022.127593

International Journal of Wireless and Mobile Computing, 2022 Vol.23 No.3/4, pp.281 - 292

Received: 24 Nov 2021
Accepted: 15 Apr 2022
Published online: 12 Dec 2022 *

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