LAUSR

We consider an Internet of Things (IoT)-based sensing system, in which an energy harvesting powered sensor observes the phenomenon of interest and transmits its observations to a remote monitor through a Gaussian channel. Based on the received signals, the monitor makes estimations of source signals with some distortion requirement. We measure the timeliness of the recovered signals using the Age of Information (AoI), which could be reduced by transmitting observations more frequently (i.e., with shorter intervals). We evaluate the recovery distortion with the mean-squared-error (MSE) metric, which would be reduced if a larger transmit power and a larger source coding rate were used. Since the energy harvested by the sensor is quite limited, however, the frequency and power of transmissions cannot be increased at the same time. Thus, we shall investigate the timeliness-distortion tradeoff of the system by minimizing the average weighted sum AoI and distortion over all possible transmit powers and transmission intervals. First, we explicitly present the optimal transmit powers for the performance limit achieving save-and-transmit policy and the easy-implementing fixed power transmission policy. Second, we investigate the offline optimization of the system and propose a backward water-filling-based power allocation scheme, as well as a genetic-based joint transmission scheduling and power control algorithm. Third, we formulate the online power control as a Markov decision process (MDP) and solve the problem with an iterative algorithm, which closely approaches the tradeoff limit of the system. We show that the optimal transmit power is a monotonic and bivalued function of current AoI and distortion. Finally, we present our results via simulations and extend the results on the save-and-transmit policy to fading sensing systems.