Abstract
Recent advances in energy harvesting technology enable wireless sensor networks (WSNs) to prolong their lifetime, with limitations caused only by the aging of the hardware devices. Depending on the energy source, energy harvesting WSNs can be largely divided into two types: a type that gathers energy from ambient sources and a type that is charged by dedicated [e.g., radio-frequency (RF)] sources. In the former, it is important to determine how the sensor should be placed relative to the ambient sources and, in the latter, the charging policy can be further designed to optimize the sensor network performance. In this dissertation, cross-layer sensor deployment and wireless power transfer (WPT) policies are examined for the purpose of distributed estimation in energy harvesting WSNs. Specifically, for energy harvesting WSNs with stationary ambient energy sources, the random large-scale deployment of energy harvesting sensors is examined for the purpose of sensing and reconstruction of a spatially correlated Gaussian random field. The sensors are powered solely by energy harvested from the environment and are deployed randomly according to a spatially non-homogeneous Poisson point process whose density depends on the energy arrival statistics at different locations. Random deployment is suitable for applications that require deployment over a wide and/or hostile area. During an observation period, each sensor takes a local sample of the random field and reports the data to the closest data-gathering node if sufficient energy is available for transmission. The realization of the random field is then reconstructed at the fusion center based on the reported sensor measurements. For the purpose of field reconstruction, the sensors should, on the one hand, be more spread out over the field to gather more informative samples, but should, on the other hand, be more concentrated at locations with high energy arrival rates or large channel gains toward the closest data-gathering node. This tradeoff is exploited in the optimization of the random sensor deployment in both analog and digital forwarding systems. More specifically, given the statistics of the energy arrival at different locations and a constraint on the average number of sensors, the spatially-dependent sensor density and the energy-aware transmission policy at the sensors are determined for both cases by minimizing an upper bound on the average mean-square reconstruction error. The efficacy of the proposed schemes are demonstrated through numerical simulations. For energy harvesting WSNs with dedicated energy sources that employ WPT to empower sensors, the beam pattern selection and charging power allocation problems for distributed estimation applications are examined. The charging operation consists of two-phases: an exploration phase and a replenishment-and-transmission phase. In the exploration phase, the RF energy chargers first scan the network in turn using different beam patterns and sensors that harvest sufficient energy based on these beams emit pilot signals to enable channel estimation at the fusion center. In the replenishment-and-transmission phase, beam patterns and charging powers are chosen based on the available channel state information (CSI) and RF energy is emitted over the air using these choices. The sensors utilize the harvested RF energy to make local observations of the underlying parameter of interest and transmit them to the fusion center, where the final estimate is computed. In this dissertation, the beam pattern selection and charging power allocation are first jointly optimized by minimizing the mean-square error (MSE) of the final estimate under perfect CSI. For tractability, an MSE upper bound is used instead as the objective function and is solved efficiently using alternating optimization and successive convex approximation (SCA) techniques. The impact of the exploration threshold and duration on the CSI quality (and, thus, the MSE of the final estimate) is examined. Simulation results demonstrate the effectiveness of the proposed scheme.