Empirical Analysis of the Impact of Routing Protocols on Malicious Node Detection in Opportunistic Networks

Abstract

The Internet of Things (IoT) has revolutionized how devices communicate and interact, enhancing the effectiveness of diverse applications. Opportunistic IoT (O-IoT) networks, characterized by dynamic, decentralized, and resource-constrained architectures, are increasingly being adopted in environments with unstable or absent infrastructure, such as disaster-stricken areas. However, the transient connectivity and mobility in these networks pose significant security challenges, particularly in detecting malicious nodes. This paper investigates the influence of routing protocols—Epidemic, Spray and Wait, and ProPHET—on malicious node detection efficiency in O-IoT networks. The problem is compounded by varying parameters such as buffer size and Time-to-Live (TTL), which affect both performance and security. We used the ONE simulator to build a simulation-based framework that examined different routing protocols with buffer sizes ranging from 5 to 20 MB and TTL values between 100 and 400 seconds. The results demonstrate that routing protocol selection significantly influences detection capabilities and resource consumption. Epidemic routing provides high delivery probability (80-85%) and packet delivery (85%), but incurs substantial overhead (70-80%) and latency (70-75%). Spray and Wait reduces overhead (40-55%) at the cost of lower delivery rates (60-65%). ProPHET achieves the best balance, maintaining moderate delivery rates (70%) while minimizing overhead (30-40%), buffer time (40%), and latency (40%). Our findings provide valuable insights for designing secure and efficient O-IoT systems, especially in resource-constrained environments such as disaster management and military applications. The results highlight the importance of protocol selection and parameter tuning in achieving optimal detection efficiency while maintaining acceptable network performance. Future work will focus on enhancing security measures through advanced cryptographic techniques and machine learning integration to improve malicious node detection without compromising network performance.