This is in response to the search for new technologies that help to reduce the consumption of cooling equipment in central offices and data centers. The project is is in line with Telefónica’s commitment to the increasingly urgent need to contribute to reducing CO2 emissions to help limit the rise in global temperature.
/PRNewswire/ Aviso AI, the only predictive Revenue Intelligence platform recognized for its whole-body Guided Selling system since 2019 disclosed that it.
Devices in Internet of Things (IoT) networks are required to execute tasks such as sensing, computation and communication. These devices, however, have energy limitation, which in turn bounds the number of tasks they can execute and their tasks execution time. To this end, this paper considers energy delivery, tasks assignment and execution in a Radio Frequency (RF) IoT network with a Hybrid Access Point (HAP) and RF-powered devices. We outline a novel Mixed-Integer Linear Program (MILP) to assign tasks to devices, and also to optimize the HAP’s charging duration. We also propose a heuristic algorithm called Energy Saving Task Assignment (ESTA), and two Model Predictive Control (MPC) approaches called MPC-MILP and MPC-ESTA; both of which use channel estimates over a given window or time horizon. Our results show that MPC-MILP and MPC-ESTA respectively consume up to 74.27% and 63.71% less energy as compared to competing approaches. Moreover, MPC-MILP with a small window has better per