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The Internet of Things (IoT) is large-scale by nature. This is not onlymanifested by the large number of connected devices, but also by the highvolumes of traffic that must be accommodated. Cellular networks are indeed anatural candidate for the data tsunami the IoT is expected to generate inconjunction with legacy human-type traffic. However, the random access processfor scheduling request represents a major bottleneck to support IoT via LTEcellular networks. Accordingly, this paper develops a mathematical framework tomodel and study the random access channel (RACH) scalability to accommodate IoTtraffic. The developed model is based on stochastic geometry and discrete timeMarkov chains (DTMC) to account for different access strategies and possiblesources of inter-cell and intra-cell interferences. To this end, the developedmodel is utilized to assess and compare three different access strategies,which incorporate a combination of transmission persistency, back-off, andpower ramping. The analysis and the results showcased herewith clearlyillustrate the vulnerability of the random access procedure as the IoTintensity grows. Finally, the paper offers insights into effective scenariosfor each transmission strategy in terms of IoT intensity and RACH detectionthresholds.

Tractable Stochastic Geometry Model for IoT Access in LTE Networks