Connectivity Aware and Energy Efficient Self-Organizing Distributed IoT Topology Control

TL;DR

This paper introduces a self-organizing topology control model for IoT networks that optimizes connectivity and energy efficiency using Hamiltonian-based dynamics, suitable for emergency and distributed scenarios.

Contribution

It presents a novel Hamiltonian-based model for self-organizing IoT network topology control, balancing connectivity and energy consumption without centralized coordination.

Findings

Model effectively maintains connectivity in simulations.

Energy consumption is minimized while ensuring reliable communication.

Suitable for emergency and distributed IoT applications.

Abstract

Internet of Things has pervaded every area of modern life. From a research and industry standpoint, there has been an increasing demand and desire in recent years to develop Internet of Things networks with distributed structure. Wireless communication under emergency circumstances is one of the important applications that distributed Internet of Things can have. In order for a network to be functional in this scenario, it must be developed without the aid of a pre-established or centralized structure and operated in a self-organized manner to accommodate the communication requirements of the time. Although the design and development of such networks can be highly advantageous, they frequently confront difficulties, the most significant of which is attaining and maintaining effective connectivity to have reliable communications despite the requirement to optimize energy usage. In this study, we present a model for self-organizing topology control for ad hoc-based Internet of Things networks that can address the aforementioned challenges. The model that will be presented employs the notion of the Hamiltonian function in classical mechanics and has two key objectives: regulating the network's topology and dynamics to enhance connectivity to a desirable level while requiring the least amount of energy possible. The results of the simulation indicate that the proposed model satisfactorily fulfills the goals of the problem.