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The wide deployment of wireless sensor networks has two limiting factors: the power-limited sensors and the congested radio frequency spectrum. A promising way to reduce the transmission power of sensors, and consequently prolonging their lifetime, is deploying reconfigurable intelligent surfaces (RISs) that passively beamform the sensors transmission to remote data centers. Furthermore, spectrum limitation can be overcome by spectrum sharing between sensors and radars. This paper utilizes tools from stochastic geometry to characterize the power reduction in sensors due to utilizing RISs in a shared spectrum with radars. We show that allowing RIS-assisted communication reduces the power consumption of the sensor nodes, and that the power reduction increases with the RISs density. Furthermore, we show that radars with narrow beamwidths allow more power saving for the sensor nodes in its vicinity. 

Communication over large-bandwidth millimeter wave (mmWave) spectrum bands can provide high data rate, through utilizing highgain beamforming vectors (briefly, beams). Real-time tracking of such beams, which is needed for supporting mobile users, can be accomplished through developing machine learning (ML) models. While computer simulations were used to show the success of such ML models, experimental results are still limited. Consequently in this paper, we verify the effectiveness of mmWave beam tracking over the open-source COSMOS testbed. We particularly utilize a multi-armed bandit (MAB) scheme, which follows reinforcement learning (RL) approach. In our MAB-based beam tracking model, the beam selection is modeled as an action, while the reward of the algorithm is modeled through the link throughput. Experimental results, conducted over the 60-GHz COSMOS-based mobile platform, show that the MAB-based beam tracking learning model can achieve almost 92% throughput compared to the Genie-aided beams after a few learning samples.