Wireless charging through directed radio frequency (RF) waves and ambient RF energy sources is an attractive solution to power small wireless devices. RF-based wireless charging can potentially realize battery-less sensor networks and eliminate the need for external power cables or periodic battery replacements. However, the coexistence of data communication and energy comes at the cost of new challenges, which this research tackles holistically through a combination of system level design, experimentation, analysis and protocol formulation.
First, a stochastic tool for analyzing nodal residual energy and lifetime distribution is proposed. Our analytical framework models an energy harvesting sensor as a stochastic semi-Markov process and introduces a new analysis technique, called energy transient analysis. The framework returns, with fine granularity, the energy consumed during various protocol-related functions, as well as the incoming energy via harvesting. As a second contribution, the opportunities and challenges of RF energy harvesting are identified through extensive practical testing. This study lays out essential network design guidelines for separating energy transmitter (ET) operations and those of the RF energy harvesting nodes in the spatio-temporal and frequency domains. It also examines the energy interference among concurrent RF waves that may result in destructive combinations of the net signal energy if the ETs are randomly placed. To verify the experimental findings, a set of closed form equations for omni-directional ETs is developed that accurately captures the locations where the ET action is cumulative. Next, a medium access control called RF-MAC is designed for ET and sensor coordination that jointly selects energy transmitters and their frequencies based on the collective impact on charging time and energy interference, sets the maximum energy charging threshold, requests and grants energy, and decides the access priority of both data and energy.
Finally, a planning and network resource management framework for powering small form factor sensor node called HYDRA is introduced that uses distributed ad hoc beamforming-capable ETs and leverages cognitive ambient energy harvesting from cellular and TV spectrum bands. HYDRA proposes a new strategy for supplying energy called energy-in-advance transfer (EIA) which provides nodes in advance with the energy they will need for current and future operations. In the planning phase, it determines the locations of ETs that jointly maximizes energy from ambient sources and minimizes the number of ETs to reduce costs and overhead. In the resource management phase, through novel mathematical formulations for optimal radiating power levels and phases of the ETs, HYDRA determines the spatial scheduling of the energy beams mapping ET operations to changing network requirements and available ambient energy, while minimizing the energy expenditures of ETs.
The results of this work can all be integrated to address real-world wireless powered systems, such as the design of the next generation RF-powered Internet of the Things (IoT).
Advisors: Professor Kaushik Chowdhury & Professor Stefano Basagni
Professor Tommaso Melodia