Machine-to-Machine (M2M) communication has emerged as a key technology with huge market potential for cellular service providers deploying LTE networks. Addition of enormous number of M2M devices into the cellular networks poses a heavy competition to existing Human-to-Human (H2H) devices for getting radio resources, thereby affecting the performance of the H2H communications. But, one can not treat all M2M flows as low priority and schedule them after H2H flows, as there are many M2M applications like healthcare and tracking which are of high importance and delay-intolerant. Hence, there is a need for class based priority scheduling of the traffic of M2M and H2H sessions in the network. In this paper, we propose a class based dynamic priority scheduling algorithm for uplink transmission of M2M and H2H traffic in LTE. The performance of the algorithm is evaluated by various metrics such as H2H throughput and system throughput and also compared with existing schedulers.

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Due to the ubiquitous coverage and seamless connectivity, cellular systems are very promising to support Machineto-Machine (M2M) communications. But, all of the cellular networks are designed and optimized for Human-to-Human (H2H) or Human-to-Machine (H2M) communications and therefore facing several challenges due to incorporation of M2M communications. One of such challenges is efficient resource allocation to M2M applications without affecting or least affecting H2H applications. In order to address this challenge, we need application specific priority based scheduling algorithms in which based on the QoS of the application, radio resources are allocated.In this paper, we have classified and prioritized all H2H and M2M flows based on their QoS requirements. Resources are allocated first to higher priority classes and in a given class, they are allocated to H2H flows first. In order to ensure the QoS of H2H flows, a threshold is kept on the maximum number of radio resource blocks to be assigned to M2M flows in a scheduling interval. Performance of the proposed scheduling algorithm is evaluated using various metrics such as system throughput and average utility per class and compared against existing scheduling schemes.

Machine to machine communication (M2M) or machine type communication (MTC) facilitates communication of two network enabled devices, without any human intervention, to take some intelligent decision based on the interaction of devices. Because of ubiquitous coverage and global connectivity, cellular networks are playing a major role in the deployment of M2M communications. Due to some unique characteristics of M2M communication, supporting M2M applications in cellular networks is very challenging. One of such challenge is congestion in radio access network (RAN) during RACH procedure. This is because of the fact that there are large numbers of M2M devices which access the radio network at the same time. As a solution, we propose an adaptive RACH congestion management function (ARC) which specifies congestion handling method to be used by all M2M devices based on the current congestion condition of the network.

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