An Adaptive Hybrid Epidemic-PRoPHET Routing Framework for Opportunistic Internet of Things Networks

Abstract

Opportunistic Internet of Things (O-IoT) networks operate in highly dynamic, infrastructureless environments where connectivity is intermittent and unpredictable, making efficient and reliable data delivery a persistent challenge. Traditional routing protocols such as Epidemic Routing and PRoPHET have been widely studied, yet both present significant drawbacks: Epidemic Routing ensures high delivery probability by replicating messages extensively, but this causes excessive buffer usage, bandwidth consumption, and energy drain, while PRoPHET employs probabilistic forwarding based on encounter histories, which is more resource-efficient but struggles in highly mobile or sparse networks where prediction accuracy decreases. To overcome these issues, this paper proposes a Hybrid Adaptive Routing framework that integrates the predictive capability of PRoPHET with a controlled epidemic fallback mechanism. The framework applies a predictability threshold of 0.6 to decide when to rely on probabilistic forwarding and when to activate epidemic replication, while carefully constraining the latter with EPIDEMIC_LIMIT = 5, HOP_LIMIT = 8, and TTL = 300 minutes to prevent resource exhaustion. . The framework was subsequently simulated and analyzed in an Opportunistic Network Environment (ONE) at different network densities and compared with the traditional routing protocols Epidemic and PRoPHET. The system's performance was evaluated using parameters such as delivery probability, overhead ratio, average latency, hop count, and buffer utilization. Experimental results confirm the approach’s effectiveness at high node density (246) nodes, where the hybrid protocol achieves a 19.03% improvement in delivery probability over Epidemic routing and 30.45% improvement over PRoPHET, alongside a 43.5% and 31.1% overhead reduction compared to Epidemic and PRoPHET respectively, and 35.5% and 37.1% latency reduction compared to Epidemic and PRoPHET respectively, making it a robust and resource-efficient solution for real-world O-IoT applications.