Live Analysis of Mesh Networks

Abstract

Abstract In the realm of the Internet of Things (IoT), the complexity of mesh architectures, particularly those employing rebroadcasting(RBC) mesh, presents formidable challenges. These challenges, ranging from flooding of packets due to rebroadcast, congestion, packet loss, demand innovative solutions to ensure the seamless operation of interconnected devices. This study embarks on a journey to unravel the intricacies of IoT mesh networks, employing a multidimensional approach that blends cutting-edge visualization techniques with sophisticated monitoring tool. This study presents a Python-based application that has been painstakingly designed to make real-time IoT mesh network monitoring and analysis easier. The study provides thorough time series visualization by utilizing the powerful features of InfluxDB and Grafana, illuminating the dynamic interactions between devices in the network. Through an elaborate test setup simulating diverse household scenarios with over 200 interconnected devices, the research meticulously examines the efficacy of live analysis in unraveling the complexities of mesh networks. Parameters such as packet count, system time synchronization, version cache integrity, level validation, and scene validations are meticulously scrutinized in real-time, providing profound insights into the network’s behavior. The findings underscore the superiority of live monitoring in detecting nuanced outcomes and ensuring real-time validation of network performance. This thesis not only contributes to advancing smart home technology evaluation but also offers practical insights into the implementation and analysis of IoT mesh networks. Through this work, we aim to enhance the reliability and efficiency of Plejd’s smart home solutions, ultimately contributing to the broader field of IoT network research.