Performance evaluation of IoT networks: A product density approach

Mobile cellular-based Internet of Things (IoT) networking is set to be enhanced with the addition of two important pillars of 5G - massive Machine Type Communications and ultra-Reliable Low Latency Communications. Temporal traffic uncertainty from diverse applications in IoT networks mandates novel modeling approaches for performance evaluation. This paper introduces a novel stochastic point process approach that can be used to evaluate the time-dependent performance of IoT base stations. Special correlation functions called Product Densities are used to (a) evaluate time-dependent offered traffic, and (b) analyze delay performance. These performance measures are evaluated at IoT base station for Poisson as well as non-Homogeneous Poisson (Beta distributed) traffic arrival processes suggested by 3rd Generation Partnership Project(3GPP). The Product Density estimates of offered traffic are found to be more accurate than the point wise stationary approximation (PSA) under non-stationary traffic arrival rates. Results from the proposed analytical model are compared with results from a simulation of two queuing models of the base station; infinite server model for offered traffic and multi-server model for delay performance. Analytical results from Product Density functions also correlate with the simulation outcomes suggesting that the proposed Product Density technique is effective when modeling the time-dependent performance of IoT networks subjected to non-stationary traffic conditions, which reflects the real-world scenario.