Optimizing radio frequency energy harvesting wireless sensor network using prediction modelling and energy management

The advent of energy harvesting technologies has paved the way for self-sustainable wireless sensor networks (WSNs). Lately, radio frequency (RF) energy harvesting WSN has been gaining attention due to its ability to power sensor nodes by tapping ambient RF energy. However, the uncertain availability and small amount of energy harvested from RF sources demand a prudent and robust energy management system for optimum network performance. Various works have focused on improving energy utilization for conventional energy harvesting networks. However, little effort has been put to address the energy management issues for RF powered WSNs. To address the issue, this work encompasses modelling of RF energy harvester, inception of a novel multi-node energy prediction technique and development of a fuzzy-based energy management system (FEMS) for RF energy harvesting WSNs. Results show that the proposed energy management system contributes in increasing the sensor node’s lifetime by 30% while reducing the energy buffer size by 10%.