초록

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Abstract ▼ AI-Helper

Body area networks (BANs) have some unique characteristics like severe attenuation, miniaturized size of in-vivo sensors, high reliability and energy efficiency requirement, and higher throughput and low latency requirement etc. Due to meet these inimitable requirements, it is challenging to design ...

Body area networks (BANs) have some unique characteristics like severe attenuation, miniaturized size of in-vivo sensors, high reliability and energy efficiency requirement, and higher throughput and low latency requirement etc. Due to meet these inimitable requirements, it is challenging to design network protocols for body area networks (BANs). In this paper, we focused on energy-efficient and minimum hop routing with lower dropping rate for body sensor networks. Here we used Q-learning approach for exploring and exploiting route from sensor nodes to sink node of body area networks that can produce near-optimal policies without complex computation and underlying model. The proposed Q-learning based routing protocol (QRP) efficiently selects the optimal route while maintaining lower packet dropping rate with nearly balanced energy consumption of sensor nodes of BANs. Simulation study shows that he proposed routing protocol outperforms in controlling packet dropping rate and launching tradeoffs between energy balanced routing and minimum hop routing.

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