Secure Optimization of RPL Routing in IoT Networks: Analysis of Metaheuristic Algorithms in the Face of Attacks

Abstract

The security and efficiency of Internet of Things (IoT) networks depend on optimizing the routing protocol for low-power, lossy networks (LPNs) to manage various challenges, including expected number of transmissions (ETX), latency and energy consumption. This study proposes an advanced meta-heuristic optimization framework integrating several algorithms, including Particle Swarm Optimization (PSO), Mixed Integer Linear Programming (MILP), Adaptive Random Search with two-step Adjustment (ARS2A) and Simulated Annealing (SA), to improve the performance of RPL-based IoT networks under attack scenarios. Our methodology focuses on secure routing by integrating dynamic anomaly detection and adaptive optimization mechanisms to mitigate network threats such as Blackhole, Sinkhole, and Wormhole attacks.Simulations were carried out on large-scale IoT networks with 100 and 150 nodes to evaluate the performance of the proposed algorithms. Experimental results indicate that ARS2A and MILP offer the best compromise between security and performance, achieving minimal ETX (1.28), reduced latency (0.12 ms) and optimized energy consumption (0.85 J) in dense networks. Furthermore, simulated annealing demonstrates high adaptability to mitigate routing attacks while guaranteeing stable energy efficiency. The comparative analysis highlights the strengths and weaknesses of each algorithm, underscoring the need for hybrid optimization strategies that balance computational cost and real-time adaptability. This work establishes a secure and scalable optimization framework for IoT networks, contributing to the development of intelligent, resilient and energy-efficient routing solutions.