Optimal planning of sensor networks for asset tracking in hospital environments
An optimization framework is proposed to plan a sensor network in hospital environments aimed at tracking medical assets. Firstly, an innovative statistical simulation model of the asset movements is developed, to define the critical levels of the hospital locations. The model feeds an optimization algorithm, which determines the optimal placements of the sensors, modeled by their coverage characteristics. Since the optimization framework is modular, it can be used for different network technologies. Simulation results are presented, considering a case-study based on the plan of the cardiology department of the San Camillo hospital in Rome and on interviews with the personnel.
optimal planning sensor networks hospital asset tracking reinforcement learning