Analysis of Temporal Robustness in Massive Machine Type Communications

TL;DR

This paper introduces a low-complexity, stochastic temporal robustness metric for massive machine type communication networks, addressing limitations of existing metrics in dynamic, large-scale 5G scenarios.

Contribution

It provides the first complete analysis of a low-complexity temporal robustness metric for large, time-varying networks, supported by an analytical framework and numerical validation.

Findings

The proposed metric accurately measures network robustness in massive MTC scenarios.

System parameters like cluster head probability and node failure significantly affect robustness.

The analytical results match numerical simulations, confirming the metric's effectiveness.

Abstract

The evolution of fifth generation (5G) networks needs to support the latest use cases, which demand robust network connectivity for the collaborative performance of the network agents, like multi-robot systems and vehicle to anything (V2X) communication. Unfortunately, the user device's limited communication range and battery constraint confirm the unfitness of known robustness metrics suggested for fixed networks, when applied to time-switching communication graphs. Furthermore, the calculation of most of the existing robustness metrics involves non-deterministic polynomial-time complexity, and hence are best-fitted only for small networks. Despite a large volume of works, the complete analysis of a $\textit{low-complexity}$ temporal robustness metric for a communication network is absent in the literature, and the present work aims to fill this gap. More in detail, our work provides a stochastic analysis of network robustness for a massive machine type communication (mMTC) network. The numerical investigation corroborates the exactness of the proposed analytical framework for temporal robustness metric. Along with studying the impact on network robustness of various system parameters, such as cluster head (CH) probability, power threshold value, network size, and node failure probability, we justify the observed trend of numerical results probabilistically.