An optimized deep learning-based fault-tolerant mechanism for energy efficient data transmission in IoT

Siddharth Kumar, Mahadev , Reema Goyal, Preet Kamal, Alankrita Aggarwal


Artificial Intelligence (AI) based framework for the Internet of Things (IoTs) have gained worldwide attention in recent years, mainly with the explosion of Micro-Electro-Mechanical Systems (MEMS) technology. Basically, MEMS has facilitated the development of tiny and smart sensors for the IoT-based framework. An AI-based IoT model is an emerging technology that helps in both fault-tolerant as well as energy-efficient data transmission purposes. For efficient data transmission in an IoT-based model, the concept of Wireless Sensor Network (WSN) plays a vital role that comprises various sensor nodes that communicate together to monitor and gather information from the various Region of Interest (RoI). Generally, sensor nodes are tiny in size and having a small battery life, limited sensing, processing, and communication capabilities. So, the fault-tolerant mechanism for energy efficient data transmission in IoT is a good initiative with the combination of Deep Learning as an AI approach. In this research article, the concept of deep learning-based fault-tolerant mechanism in IoT frameworks for energy efficient data transmission is proposed in an optimized manner. Here, the concept of the Grouped-Bee Colony (GBC) algorithm is designed for the fault detection mechanism as an optimization approach along with the Deep Learning as an AI.


internet; Internet of Things; fault analysis; WSN; Artificial Intelligence; GBC; deep learning

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