Leveraging crowd sourcing technique for an optimized M2M access during emergency situations

Machine-to-Machine (M2M) communications are expected to be one of the major drivers for the future 5G network. The significant increase in the number of these devices could, however, lead to a huge amount of traffic at the access network, which may cause heavy congestion and service degradation, especially for the most critical applications. Indeed, when the M2M traffic's arrivals become correlated and synchronous, the performance of the Random Access Channel (RACH) is drastically reduced, causing a large number of devices to experience outages due to repetitive failed access attempts. This results in increased power consumption and intolerable access delays. In this paper, we propose a novel approach to deal with massive synchronous access attempts, tailored for both M2M delay-sensitive applications and energy constrained ones. The main idea behind the paper is to leverage crowd sourcing data, transmitted from the devices succeeding in the RACH procedure, to tune the access parameters, without requiring too complex techniques for the estimation of the number of attempts. Simulation results show that the proposed scheme achieves sub-optimal performance in the wireless resources' utilization while reducing significantly both the number of access attempts and the access latency for delay sensitive applications, which allows guaranteeing energy conservation.