Distributed Edge Computing For Resource Allocation In Mmtc Using Non-Linear Stochastic Optimization

Abstract

This research work investigates distributed edge computing for efficient resource allocation in mMTC networks by means of non-linear stochastic optimisation techniques. The non-linear and stochastic nature of the communication channels as well as the irregular behaviour of IoT devices complicate the resource allocation in mMTC greatly. In this study, we propose a novel method using networked edge computing to optimise resource allocation in demanding settings. Using a non-linear stochastic optimisation algorithm, the method dynamically adjusts resource allocation decisions depending on real-time network conditions and device requirements so improving the overall network performance. Simulations produce results demonstrating the quality of the recommended strategy. More precisely, our approach boosts resource economy by 28% and lowers latency by 35% vs to typical centralised systems. Moreover, the recommended architecture increases the scalability of the network therefore enabling up to 50% more devices without compromising speed. These results draw attention to how distributed edge computing could address the critical resource allocation issues in mMTC systems.