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This work examines the large-scale deployment of energy harvesting sensors 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 nonhomogeneous 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.

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This work examines the charging power allocation and beam selection problem for distributed estimation in wireless passive sensor networks, where the sensors are charged over the air by RF-enabled energy sources. A two-phase replenishment and transmission cycle is considered. In the replenishment phase, each wireless charger emits power over the air through a carefully selected beam and power to replenish the wireless sensors. In the transmission phase, each sensor makes a local observation of the underlying parameter and transmits a quantized version of its measurement to the fusion center, where the final estimation is computed. The charging power allocation and beam selection are jointly determined by minimizing the mean-square error (MSE) of the final estimate. For tractability, the optimization is performed by considering an MSE upper bound as the objective function and is solved efficiently using alternating optimization and successive convex relaxation techniques. The simulation results demonstrate the effectiveness of the proposed scheme.Index Terms-Wireless power transfer, distributed estimation, beam pattern selection, wireless passive sensor networks.

In this paper, we propose a data-dependent transmission control policy over the slotted ALOHA MAC protocol and a cross-layered fusion rule that exploits MAC timing information for distributed detection in sensor networks. In this system, each sensor first makes a local decision at the beginning of each observation period and transmits the decision to the fusion center over a random access channel. Based on the slotted ALOHA random access protocol, we propose a class of data-dependent transmission control policies that assign to sensors their transmission probabilities according to the reliability of their local decisions. For the case with i.i.d. observations in each time slot, we show that the optimal transmission control function takes on the form of a thresholding function. That is, a sensor will transmit in a given time slot if and only if its local log-likelihood ratio exceeds a certain threshold. When observations are made only every several time slots, the message arrival time at the fusion center, which is spread over the observation period of duration K > 1, will embed the reliability of the received sensors' decisions as a result of the data-dependent transmission control. This timing information can be accounted for in the fusion rule to further enhance performance. Finally, we extend the proposed strategies to multicluster sensor network scenarios, where the sensors' local decisions are transmitted to the fusion center in a two-hop fashion. We show, through numerical simulations, that the proposed schemes outperform both conventional slotted ALOHA and TDMA-based schemes that do not adopt cross-layered transmission and fusion strategies.