LAUSR

We consider a system consisting of multiple sensors, a central monitoring station, and multiple orthogonal frequency channels. The sensors measure heterogeneous time-varying signals and report their measurements to the central monitoring station which uses them to make control decisions. Due to limited communication capacity, not all sensors can send updates to the monitoring station at all times. The cost the system pays is a function of the weighted sum of the ages-of-information of the various sensors over time. The goal is to design scheduling policies which minimize the time-average of this cost. We propose a policy called SQRT-Weight which fetches updates from sensors at a frequency proportional to the square-root of the corresponding weights and show that this policy is asymptotically 8-optimal. In addition, we compare the performance of the SQRT-Weight policy with other natural scheduling policies via simulations and through analysis for certain special cases.