近年來能量採集(energy harvesting )技術的演進使得無線感測網路(wireless sensor network, WSN)得以延展它們的生命週期，只會受到硬體裝置老化的限制。依照採集能量源的方式，能量採集無線感測網路大致上可分為兩大類型：一類為從周圍能量源採集能量，另一類型為被專用的能量源(例如射頻[radio-frequency, RF])所充電。對前者來說，決定如何根據周圍能量源來擺放感測器是重要的，而對後者來說則可進一步設計充電策略來最佳化感測網路的效能。在這篇論文中，我們在能量採集無線感測網路中檢視了以分散式估測(distributed estimation)為目的之跨層(cross-layer)感測器佈置和無線功率傳輸(wireless power transfer, WPT)策略。
針對周圍有穩定能量源的能量採集無線感測網路，我們檢視了以感測和重建空間相關(spatially correlated)高斯隨機場(Gaussian random field)為目的的大規模能量採集感測器之隨機佈置(random deployment)。感測器的能量完全來自於周圍能量源，而它們是依據每個位置的感測器密度(density)和與能量抵達統計特性有關的空間(spatially)異質卜松點程序(non-homogeneous Poisson point process)來做隨機佈置的。隨機佈置適合用於需要在廣大和(或)不友善的區域作佈置的應用。在一個觀測週期內，如果感測器的能量足夠用來作傳送，則它會對隨機場取樣並將資料傳到最近的資料收集節點(data-gathering node)，接著融合中心(fusion center)會根據感測器回傳的觀測值重建此隨機場。當以場重建(field reconstruction)為目標時，感測器一方面需要分散在區域中以取得更有資訊含量的觀測值，但一方面也需要集中在能量抵達速率高或與資料收集節點通道增益(channel gain)高的位置。此權衡被用在類比和數位系統中的隨機感測器佈置最佳化。更明確地說，給定每個位置的能量抵達速率和感測器平均數量限制，透過最小化一個平均均方(average mean-square)重建錯誤的上界，針對這兩個系統我們決定了空間依賴(spatially-dependent)的感測器密度以及在感測器端的能量感知(energy aware)傳輸策略。所提出的方法之功效也透過模擬來驗證。
針對具有專用能量源以無線功率傳輸來啟動感測器的能量採集無線感知網路，我們檢視了以分散式估測為目的之充電功率分配和波束場型(beam pattern)選擇的問題。充電運作程序由兩個階段所組成：探索階段(exploration phase)和充電及傳輸階段(replenishment-and-transmission phase)。在探索階段中，射頻充電器輪流以不同的波束場型發射射頻能量來掃描網路，而感測器則吸收這能量來傳送領航訊號(pilot signal)以便融合中心估測通道。在充電及傳輸階段中，會依據可用的通道狀態資訊(channel state information, CSI)來選擇波束場型和充電功率，而射頻能量以所選的波束場型與充電功率經由空氣傳輸給感測器。每個感測器各自用採集來的射頻能量對潛在的參數進行本地端觀測，並傳送量化的觀測值到融合中心，在此對參數的最後估測便可被計算出來。在本論文中，我們先在完美通道狀態資訊下，透過最小化最後估測值的均方誤差(mean-square error, MSE)來聯合最佳化(jointly optimize)充電功率分配和波束場型選擇。為了可處理性，我們考慮以均方誤差的上界(upper bound)為目標函數來進行最佳化。配合使用交替式最佳化(alternating optimization)和連續凸面近似(successive convex approximation, SCA)法，便可有效率的解決此問題。我們也檢視了探索臨界值和探索時間對於通道狀態資訊品質(此因素影響參數估測的均方誤差)的影響。模擬結果顯示我們所提出的方法的有效性。

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.

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