Ricerc@Sapienza

The advent of low-power sensor nodes coupled with intelligent software and hardware technologies has led to the era of green wireless networks. From the hardware perspective, green sensor nodes are endowed with energy scavenging capabilities to overcome energy-related limitations. They are also endowed with low-power triggering techniques, i.e., wake-up radios, to eliminate idle listening-induced communication costs.

Superior performance in wireless sensor networks is obtained by taking key protocol decisions based on the outcome of local learning-based computations, informing nodes on past and expected availability of resources. This paper investigates the impact on protocol performance of local computational requirements of learning techniques.