Information centric wireless communication for variation detection and Mitigation Model in industrial internet of Things

Abstract

The Industrial Internet of Things (IIoT) harmonizes smart devices, machines, and intelligent technologies to improve production efficiency through the information-centric network. Information-Centric Networking (ICN) is a potentially useful method in control sequences and intelligent operations due to adversary impacts. Here, the sharing of content chunks between different machines' varying control spans and operation cycles has been partially connected to the internet. This article introduces a Variant Loop Detection and Mitigation Model (VLDMM) for preventing illegitimate entries in ICN. The proposed model is backboned by recurrent learning for training the loop closures and time achievements in ICN. The harmonization is utilized under different production outputs observed from the previous cycles. The interruptions and process halts are identified using recurrent training by correlating the previous operation logs in ICN. Therefore, ICN may solve the challenges based on the available control slots and variations. This learning retains the production efficiency and prevents halts under controlled time and loop closures.