Trimet graph optimization (TGO) based methodology for scalability and survivability in wireless networks

Abstract

Wireless networks gain popularity in recent years with tremendous growth in Wi-Fi, 5G cellular, V2X, Low-power wide-area, Wireless-sensing, Millimeter-wave, Backscatter networks. The common problem we identified in the above technologies is scalability and survivability will affect the performance of the systems. The current development demands for an improvement in traditional topologies. The above challenges cannot be addressed by a star, wheel, full mesh chain, ring, and etc. due to lack of scalability, survivability and resilience properties. These problems can be addressed in different ways by using machine learning, AI, game theory, etc. In this paper graph theory techniques were applied because this method is well equipped with mathematical techniques that provide a systematic alternative to traditional topologies. Physical features like cost, latency, congestion, survivability, resilience are correlated with graph invariants like node degree, diameter, average distance, and edge betweenness and Wiener impact. The above-mentioned Scalability, survivability problems are applied on backhaul, wireless networks respectively. Finally, a general framework was designed for wireless networks using the NetworkX package in python.