For further information, please contact the General chair: Pierre-Emmanuel Gaillardon
The University of Tokyo, Japan
Reinforcement Learning-Based Adaptive Power Management for Energy Harvesting IoT Devices
Energy harvesting IoT (Internet of Things) devices are expected to operate perpetually and reliably without any regular maintenance by users. Energy autonomy is a necessary condition for such operation and must be addressed by the power manager integrated in IoT nodes. The power manager inherently needs adaptivity to adjust the behavior of the IoT nodes according to expected energy harvesting opportunities. This is not an easy task since there are a wide variety of IoT devices and their working environments. In this talk, we present an adaptive power management strategy using Reinforcement Learning for energy harvesting IoT nodes which train themselves from historical data. We show that our power manager is capable of adapting to changes in weather, climate and battery degradation while ensuring maximum performance without depleting or overcharging its battery.
Masaaki Kondo received the B.E. degree in College of Information Sciences and the M.E degree in Doctoral Program in Engineering from University of Tsukuba in 1998 and 2000 respectively, and the Ph.D. degree in Graduate School of Engineering from the University of Tokyo in 2003. He is currently an associate professor in the Graduate School of Information Science and Technology at the University of Tokyo. His research interests include computer architectures, high-performance computing, and cognitive computing.