Radio Frequency Energy Harvesting and Data Rate Optimization in Wireless Information and Power Transfer Sensor Networks

Wireless energy harvesting using radio-frequency (RF) energy is a growing area of research to power in- and/or on-body sensors. However, solutions currently proposed in literature are hard to realize in a dynamic environment representative of the real world. This paper proposes the use of multiple intended RF sources with a harvest-then-transmit protocol to maximize the harvested energy and optimize data rate in wireless information and power transfer sensor networks. The problem of optimizing system timings to simultaneously maximize the harvested energy and network-level achievable data rate is tackled using optimization theory in concert with an RF source selection algorithm for the energy harvesting sensor nodes. With the methods proposed in this paper, it was found that the system achievable data rate and throughput fairness when energy is harvested from up to 5 RF sources can increase by up to 87% and 50%, respectively, compared with solutions when energy is harvested from one source. The proposed algorithm can also increase the system achievable data rate and throughput fairness by up to 72% and 22%, respectively, compared with a system without the algorithm. The findings are significant for designing and realizing future generation sensors powered by energy from multiple intended RF sources in the real world.