Title: An incremental learning on cloud computed decentralised IoT devices

Authors: Satish S. Salunkhe; Aditya Tandon; M. Arun; Nazeer Shaik; Supriya Nandikolla; D. Ramkumar; S. Lakshmi Narayanan

Addresses: Computer Engineering Department, Terna Engineering College, India ' Department of CS&E, Krishna Engineering College, Ghaziabad, India ' School of Computing, Kalasalingam Academy of Research and Education, Krishnankoil, Tamil Nadu, India ' Department of CSE, Bapatla Engineering College, Bapatla, 522102 Guntur District, AP, India ' Department of CSE, Malla Reddy Engineering College (A), Hyderabad, Telangana, India ' Department of ECE, As-Salam College of Engineering and Technology, Thirumangalakudi, Aduthurai, Tamil Nadu, India ' Gojan School of Business and Technology, Chennai, Tamil Nadu 600052, India

Abstract: It is essential that IoT devices can constantly gather new ideas from streams of data independent of catastrophic forgetfulness. Although merely repeating all prior training samples can solve catastrophic forgetting issues, this method faces privacy problems, memory resources, as well as requires a lot of computational, making it unsuitable for limited-resources IoT devices. In this study, the proposed incremental learning for cloud computed decentralised IoT devices are developed and comprises of constant upgraded information and task resolution model. A neural network is trained and utilised to overcome this problem despite frequent disconnectivity or resource outages without losing a lot of progress using cloud computing. Several research experts have frequent disconnectivity issues regarding cloud computing frameworks because of the platform's free membership. Identical difficulties can be seen when working on a localised computer, where the machine will run out of resources or power at times, forcing the researchers to retrain the systems.

Keywords: incremental learning technique; cloud computing; decentralised IoT devices; internet of things; IoT.

DOI: 10.1504/IJESMS.2023.127397

International Journal of Engineering Systems Modelling and Simulation, 2023 Vol.14 No.1, pp.1 - 7

Received: 29 Jun 2021
Accepted: 16 Aug 2021
Published online: 03 Dec 2022 *