本論文旨在能量採集的無線感測網絡環境中提出最大似然估測并通過設計量化比特數和傳輸能量改善最終的估測效果。能量採集技術讓無線傳感器可以從周圍環境中收集能量轉化成電能用作通訊傳輸。無線傳感器收集到包含待估測參數的信息後，先通過均勻量化器以數位傳送的方式把信息發送給處理中心，然後處理中心利用最大似然估測把感興趣的參數給估測出來。這篇論文探討了無線傳感器裝有電池和沒裝電池兩種情況。沒有裝電池時，當下時刻採集到的能量就得立刻用完不能保存，所以無法具有能量調節功能。當裝有電池時，每一時刻剩餘的能量都可以保存下來，所以具有能量調節功能。在裝有電池的情況下，我們考慮用固定能量去傳輸信息：當電池能量超過某一個固定值時，我們就使用此固定能量去傳輸訊息，當電池的能量沒有達到此固定值的時候就不做傳輸，等待下一時刻繼續收集周圍的能量。為了改善最終估測的效果，我們通過最大化費雪信息量來設計均勻量化器的最佳量化比特數。在裝有電池的情況下，我們還可以設計最佳的固定傳輸能量讓最終估測的效果達到最佳。

Distributed estimation is examined for energy-harvesting wireless sensor networks, where the energy available at the sensors are converted entirely from ambient sources.
Here, each sensor takes a local measurement of the common parameter of interest and forwards the information to the fusion center (FC) using a digital forwarding (DF) transmission strategy. In the DF system under consideration, local observations are first quantized using a uniform quantizer and transmitted to the FC using BPSK modulation. In this work, two cases are considered: the case with no batteries at the sensors and the case with finite-capacity batteries at the sensors. {In the case with no batteries, each sensor transmits with whatever energy it has accumulated in that transmission period; in the case with finite-capacity batteries, {each sensor transmits with a constant power} whenever its accumulated energy is sufficient to do so.
The maximum-likelihood estimator (MLE) is adopted at FC to combine the information received from all sensors and is derived based on the statistics of the energy arrivals. In the case with no batteries, an approximate MLE is derived by treating the transmission noise as Gaussian. The optimal number of quantization levels (i.e., the number of bits to be transmitted in each observation period) and the transmit power in the case with batteries} are determined by maximizing the Fisher information. The effectiveness of our proposed schemes is demonstrated through Monte Carlo simulations.

Abstract...1
Contents ii
1 Introduction...1
2 System Model...4
3 Maximum Likelihood Estimation with No Batteries at the Sensors...8
3.1 Derivation of the Maximum Likelihood Estimate...8
3.2 Optimization of the Number of Bits...11
4 Maximum Likelihood Estimation with Battery-Enabled Sensors...14
4.1 Derivation of the Maximum Likelihood Estimate...15
4.2 Optimization of the Number of Bits and the Transmission Powers...17
5 Simulation Results...19
5.1 Simulations in the Case with I.I.D. Sensors...19
5.2 Simulations in the Case with Non-I.I.D. Sensors...26
6 Conclusion...31
Appendices...32

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