Estimating the number of contending IoT devices in 5G networks: Revealing the invisible

The explosive growth of connected objects is certainly one of the most important challenges facing operators' network infrastructures. Although it has been foreseen for a very long time, it is still not clear how to support such huge number of devices efficiently. Indeed, although a smarter planning of dedicated access slots would certainly limit the burden, this remains limited since some equipments react to events, which cannot be timed. Moreover, barring some IoT devices from accessing the network, using Access Class Barring-like techniques, is very efficient; nevertheless, efficiency is generally linked to precise knowledge of the number of contending devices. Although, before connection establishment, the terminals are invisible to access points, it is very difficult to estimate their number. A lower bound of backlogged devices can be determined. The average number of terminals can even be estimated with more or less precision, as this kind of problems comes to the classical balls-into-bins problem, which was extensively analyzed in the literature. However, an overestimation of this number implies underutilization of resources whereas an underestimation may lead to a congestion collapse. In this way, we propose a lightweight change to the standard to accurately reveal the state of network congestion by overloading connections' requests with the number of access attempts (number of times the device has been barred as well as the number of attempts). Using such information, we propose an accurate recursive estimator of the number of devices. The obtained results demonstrated that the proposed solution not only makes it possible to estimate the number of equipment much better than existing techniques but also allows determining precisely the number of blocked equipments.