A Swarm Intelligence-Based Path Selection for Low-Power and Lossy Networks

In a Low-power and Lossy Network (LLN), owing to its unique characteristics, such as lossy channels, dynamically changing network topology and resource-constrained nodes, finding a global optimum path to deliver packets efficiently and reliably is a challenging task. Current path-finding solutions in LLNs often fall short in addressing the issue of finding a global optimum path based on a comprehensive set of factors that impact network performance. To address these challenges, there is a critical need for a new approach that can dynamically and intelligently optimise routing paths in real-time, considering the most important factors. This paper investigates how to obtain a global optimum path in real-time in LLNs. It critically analyses existing path-finding solutions designed for LLNs and identifies two aspects for improvement. The first one is to capture all the factors that may impact packet delivery reliability by using a greater set of routing metrics. The set of metrics not only measures packet delivery reliability, the level of control overhead and packet delivery delays, but also energy preservation for prolonging network lifetime. The second one is a novel Swarm Intelligence-based Path Selection (SIPaS) framework for finding a global optimum path based on the real-time values of the routing metrics. We have coded the framework into an Ant Colony Objective Function (ACOF) equipped with multiple routing metrics seamlessly integrated into the Routing Protocol for LLNs (RPL). Simulation results indicate that the SIPaS framework outperforms related solutions significantly in terms of packet delivery ratio, energy preservation, network lifetime extension, and reduction of both delay and control overhead.