Adaptable Probabilistic Transmission Framework for Wireless Sensor Networks

We propose a novel framework that combines probabilistic transmission with Latin squares characteristics to tune channel access, meeting various demands in network performance (Energy vs. Delay). The proposed technique is decentralized, scalable, and has low overhead. We develop an analytical model to estimate the network performance and validate the benefits of the proposed framework via simulation-based experiments.