Multi-Disjoint Path opportunistic networks with Hidden Markov Chain modeling

Abstract

Advancing the efficiency and reliability of wireless sensor networks is a paramount pursuit in modern networking research. In this context, we introduce a groundbreaking approach based on Hidden Markov Chain (HMC) modeling with opportunistic routing, harnessed by the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) mechanism. Our innovative algorithm addresses key challenges in Wireless Body Area Networks, including delay, throughput, and bandwidth utilization. By strategically integrating the HMC model, which captures intricate state transitions and emission probabilities, our proposed method introduces a robust solution for optimizing node connections and routing decisions. This fusion of HMC and opportunistic routing capitalizes on the strengths of both paradigms, enhancing the network's ability to make intelligent decisions in dynamic scenarios. Through rigorous simulations, we showcase the algorithm's prowess in achieving efficient data transmission. The empirical evidence from these simulations underscores the algorithm's superiority when juxtaposed against state-of-the-art mechanisms such as Simple Multi-Packet Access (SMPA) and Priority-based Congestion-avoidance Routing Protocol (PCRP). Our algorithm not only outperforms existing solutions in terms of delay, throughput, and bandwidth utilization, but it also showcases its ability to adapt to varying network conditions, making it a versatile tool for diverse applications.