Machine Learning Assisted Information Management Scheme in Service Concentrated IoT

Gunasekaran Manogaran, Mamoun Alazab, Vijayalakshmi Saravanan, Bharat S. Rawal, P. Mohamed Shakeel, Revathi Sundarasekar, Senthil Murugan Nagarajan, Seifedine Nimer Kadry, Carlos Enrique Montenegro-Marin

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Internet of Things (IoT) has gained significant importance due to its flexibility in integrating communication technologies and smart devices for the ease of service provisioning. IoT services rely on a heterogeneous cloud network for serving user demands ubiquitously. The service data management is a complex task in this heterogeneous environment due to random access and service compositions. In this article, a machine learning aided information management scheme is proposed for handling data to ensure uninterrupted user request service. The neural learning process gains control over service attributes and data response to abruptly assign resources to the incoming requests in the data plane. The learning process operates in the data plane, where requests and responses for service are instantaneous. This facilitates the smoothing of the learning process to decide upon the possible resources and more precise service delivery without duplication. The proposed data management scheme ensures less replication and minimum service response time irrespective of the request and device density.

Original languageEnglish (US)
Article number9152085
Pages (from-to)2871-2879
Number of pages9
JournalIEEE Transactions on Industrial Informatics
Volume17
Issue number4
DOIs
StatePublished - Apr 2021

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

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