Delay-Aware resource allocation to increase efficiency over LTE network for M2M communications in a noisy channel

Abstract

Currently, the predominant source of Internet traffic stems from interpersonal user interactions, commonly referred to as user-to-user or H.2.H communications, facilitated through computer systems or mobile devices. The Internet of Things (IoT) is a transformative concept that has gained prominence over the past few years. It represents a paradigm shift from isolated systems to a vast interconnected network of devices that are capable of collecting environmental data, processing it, and making informed decisions based on the processed information. Therefore, it is imperative to have an effective resource scheduler in place for the management of an Long-Term Evolution (L.T.E) network system that encompasses both Machine-to-Machine (M.2.M) devices and Human-to-Human (H.2.H) users. In order to develop analytical formulas for assessing the performance of the suggested programs in respect to average waiting time, average system delay, and average number in the system for M.2.M and H.2.H users, this study uses the M / G / 1 priority queue model. It is possible to provide M.2.M consumers with good Quality of Service (QoS) while maintaining the targeted QoS levels for H.2.H users, according to the analysis of the collected data. The relaxing method is employed to cater to the needs of users in the M.2.M queue. To provide QoS for delay-sensitive traffic generated by H.2.H users, a classification scheme is employed to categorize H.2.H traffic into two primary classes. The findings show that the proposed systems have provided 87.34% improvement in machine-to-machine (M.2.M) performance while managing 92.26% QoS for H.2.H services.