A Secured Swarm Intelligence-based Path Selection framework for Malicious Low-power and Lossy Networks under RPL protocol

Abstract

Low-power and Lossy Networks (LLNs) face persistent challenges, including dynamic topologies, unreliable links, limited energy, and constrained computational resources. These issues are exacerbated under malicious conditions such as Packet Dropping Attacks (PDAs), where conventional routing and security mechanisms fall short due to their high computational overhead. To address these challenges, this paper proposes the Secured Swarm Intelligence-based Path Selection (S-SIPaS) framework, designed to enhance reliability and security in Malicious LLNs (MLLNs). S-SIPaS builds on our previous SIPaS framework by integrating a lightweight trust model and a novel Secured Ant Colony Objective Function (S-ACOF) into the RPL protocol. S-ACOF applies Ant Colony Optimisation (ACO) principles to compute globally optimal, trustworthy paths while reducing energy consumption and control overhead. A key feature of S-SIPaS is its three-phase trust model: monitoring, trust measurement, and trust determination, which detects and isolates malicious nodes based on packet-forwarding behaviour, without relying on cryptographic techniques.

The framework combines multiple routing metrics, including physical distance, energy level, link quality, and trust score, enabling adaptive and efficient path selection in dynamic LLNs. Simulation results show that S-SIPaS improves Packet Delivery Ratio (PDR) by up to 51% over existing methods, especially in high-density and high-attack scenarios.

Despite strong performance, the framework has limitations: (i) it requires C1-class nodes (e.g., Z1); (ii) evaluation is limited to simulations; and (iii) it currently addresses only PDA threats and static topologies. Overall, S-SIPaS offers an effective, scalable, and secure routing solution for enhancing MLLNs and IoT systems.