A TRUST-BASED MULTI-PATH CONGESTION AVOIDANCE TECHNIQUE TO CURB WORMHOLE ATTACKS FOR MOBILE NODES IN WIRELESS SENSOR NETWORKS

Abstract

Wireless Sensor Networks (WSNs) are widely adopted for various communication applications due to their scalability, flexibility, and ease of deployment. However, WSNs face significant security threats, such as wormhole attacks, where malicious nodes manipulate routing paths using low-latency links, leading to network disruption. Traditional cryptographic mechanisms struggle to detect wormhole attacks, making them difficult to mitigate. Additionally, network congestion is a critical challenge, as excessive traffic can degrade performance by overwhelming available communication channels. To address these challenges, this study proposes a Trust-Based Multi-Path Congestion Avoidance Technique (TB-MCAT) to enhance network security and optimize performance. TB-MCAT technique integrates trust evaluation mechanisms and congestion-aware multipath routing to mitigate wormhole attacks while ensuring efficient traffic management. A weighted trust value is computed for each intermediary router based on network parameters, including packet loss ratio, round-trip time (RRT), and energy consumption rate. This trust-based approach ensures that only reliable paths are selected for data transmission, minimizing the impact of malicious nodes. Additionally, a trust threshold is used to classify nodes and dynamically select the most secure and efficient routes, significantly improving network stability and reliability. The effectiveness of TB-MCAT was evaluated through simulations in a 50-node WSN using the NS-3 simulator, and performance was compared against AODV, OLSR, DSR, and DSDV. The evaluation was based on five key network performance metrics. TB-MCAT achieved an average route disjointedness of 89%, ensuring independent and non-overlapping multipath routes, reducing the risk of a single point of failure and improving network resilience against wormhole attacks. TB￾MCAT optimized encryption usage with an average of 19 to 22 encryption requests per session, reducing computational overhead while maintaining strong security mechanisms through trust-based path selection. TB-MCAT significantly reduced transmission delays, achieving an average end-to-end latency of 87ms, compared to 125ms Ad hoc On-Demand Distance Vector (AODV), 115ms Optimized Link State Routing (OLSR), 105ms Dynamic Source Routing (DSR), and 95ms Destination￾Sequenced Distance-Vector Routing (DSDV), making it the most efficient routing protocol for time-sensitive WSN applications. TB-MCAT maintained a high packet delivery ratio of 92.5%, outperforming AODV (70%), OLSR (80%), DSR (75%), and DSDV (77%), ensuring uninterrupted and reliable data transmission even in the presence of wormhole attacks. TB-MCAT exhibited optimized energy efficiency, consuming 3.2 - 3.6 Joules per node, which was 30% lower than AODV (4.6 - 4.8 Joules) and 20% lower than OLSR (4.4 - 4.6 Joules), extending the network lifespan and improving sustainability. Simulation results confirm that TB-MCAT outperforms benchmark techniques, achieving higher throughput, superior wormhole attack detection, lower congestion, and improved network stability. The key contribution of this study is the development of a robust, trust-based routing technique that effectively mitigates wormhole attacks and reduces congestion simultaneously while maintaining optimal network performance. Additionally, this study recommends implementing TB-MCAT in environments with dynamic network topologies where security threats and congestion are prevalent. As part of future work, the study aims to enhance TB-MCAT by integrating adaptive machine learning models to improve trust evaluation, intrusion detection, and congestion prediction, further strengthening network security and efficiency.