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The deployment of machine-to-machine (M2M) communications on cellular networks provides ubiquitous services to Internet-of-Things (IoT) systems. Cellular networks have been chosen as the best infrastructure for M2M communications due to the wide coverage and spectral efficiency. However, with the increased number of devices connecting to the network, massive number of devices are expected to simultaneously access the network resources. This massive access results in excessive congestion and collisions in the random access channel (RACH) which causes major degradation in systems performance. This article focuses on resolving the RACH collisions during the massive access scenarios for cellular M2M communications. We propose a collision resolution scheme using the backoff procedure which dynamically adjusts the backoff indicator (BI) based on the number of backlog devices and the available resources. The proposed scheme is integrated with three well-known random access schemes; standard random access (SRA), static access class barring (ACB) and dynamic access class barring (DAB). Furthermore, the paper presents an analysis for access success probability based on the dynamic backoff procedure. The optimal value of BI that achieves the highest access success probability is derived for the three different schemes. The analysis and simulation results indicate that the dynamic value of BI achieves approximately 99.9% access success rate with a slight increase in access delay of around 10%, which is considered a reasonable increment for delay-tolerant applications during the massive arrivals scenarios.

Dynamic Backoff Collision Resolution for Massive M2M Random Access in Cellular IoT Networks