Adaptation of Loss Recovery Mechanisms for improving Scalability and Quality of Service in IoT Networks

Adaptation of Loss Recovery Mechanisms for improving Scalability and Quality of Service in IoT Networks

연구 분야: Networking

학회: 2023 IEEE International Conference on Advances in Data-Driven Analytics And Intelligent Systems (ADACIS)

Over the past decade, the Internet has known significant transformations in diverse sectors, such as health, transportation, and the environment. These changes are based to the TCP/IP model protocols, a four-layer architectural framework with the transport layer notably enabling Internet communication and Quality of Service management.This growth goes from the original ARPANET design to the appearance of Web 3.0, including Web 1.0 and Web 2.0. It encourages the creation of new communication structures, like the Internet of Things (IoT).IoT aims to unify all physical objects and devices in various fields under a common infrastructure in order to control them and also to know their states in real time [1]. Therefore ,IoT becomes one of the most well-known technologies in many markets with an immense growth of connected devices ranging from 30.7 trillion in 2020 to 75.4 trillion in 2025[2]. This gigantic number of things leads to many challenges and problems related particularly to QoS and scalability, which represent a hindrance for IoT and specifically M2M networks.The main objective of this paper is to provide a comprehensive contribution that facilitates the implementation of mechanisms for autonomous scalability management and QoS. This is achieved by integrated the loss recovery protocols, especially those tailored to the Transport Layer of the TCP/IP model, at the middle-ware level within IoT networks.The outcome of this work involves suggesting a new architecture for the existing IoTScal-H approach. It incorporates additional components simulating loss recovery mechanisms to boost the success rate from 95% to 99.99% for e-Health traffic for example, thereby enhancing the current reference approach.