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Evaluation of Quality Of Service Parameters for MQTT Communication in IoT Application by using Deep Neural Network - 2024

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Research Paper on Evaluation of Quality Of Service Parameters for MQTT Communication in IoT Application by using Deep Neural Network

Research Area:  Internet of Things

Abstract:

The three main pillars of the Internet of Things (IoT) are Computation, Communication and things that are connected in a network of IoT. In IoT for communication, various protocols like Constrained Application Protocol (CoAP), Extensible Messaging and Presence Protocol (XMPP), Message Queuing Telemetry Transport (MQTT), etc. are used. As MQTT requires less battery power, has a small bandwidth, requires fewer resources and is easy to use, it is an ideal protocol for IoT. Hence it is commonly used in IoT. Recently the number of IoT devices has been increasing at a faster rate. Therefore, it is important to focus on the Quality of Service (QoS) of IoT applications and the smooth transmission of data over the network. QoS is one of the important elements in IoT systems that can be used for the evaluation of the performance and quality of IoT systems. The security breach in IoT will have a great impact on the performance of the IoT system. Therefore, it is essential to evaluate the QoS parameters. This research focuses on the secure data transmission in IoT applications using the MQTT protocol. The performance and security related QoS parameters of MQTT communication in IoT were evaluated and the security breaches were detected by using a neural network. The developed system provides better security and if still, any misactivity happens then it can also be detected. The developed system provides very good accuracy for the detection of security breaches.

Keywords:  

Author(s) Name:  Shital Pawar, D. B. Jadhav, Meghana Lokhande, Punam Raskar & Megha Patil

Journal name:  Information Technology

Conferrence name:  

Publisher name:  Springer

DOI:  10.1007/s41870-023-01664-2

Volume Information:  Volume 16, pages 1123–1136, (2024)