無線感測網路 (WSN)中的暫存器可以通過減少網路範圍的傳輸來節省可用資源。 對感測器收集的數據進行暫存有助於用戶訪問訊息並減少網路流量，但當數據大小隨著時間快速增長或暫存容量有限時可能會面臨些挑戰。然而，在無線感測網路中，感測器節點經常被部署來實現一個共同的目標，例如環境監測中的溫度估計或入侵檢測。在這種情況下，暫存器不需要存取感測器收集的原始數據，而是可以只存與底層應用程序相關的訊息。在本論文中，為了在無線感測網路中進行分散式估計，我們提出了一種跨層暫存策略。
具體而言，在無線感測網路的分散式參數估計應用中，感測器首先收集有關常見現象的訊息，然後將訊息轉傳到計算最終估計的融合中心。通過假設參數具有時間相關性，融合中心的估計品質可以通過結合現在和過去的訊息來提高，而過去訊息可以從暫存器中的數據獲得。另一方面，在網路中，隨著時間的推移不斷產生大量數據，暫存所有來自於感測器的收集數據可能成本很高。選擇最佳感測器是克服這種過度的網路內資源消耗的關鍵思想之一。與傳統的暫存問題不同，在本論文中，我們的目標是重建感測器的觀察結果且我們的暫存策略旨在通過最佳化選擇必要的感測器和暫存資訊來最小化最終估計的長期平均均方誤差 (MSE)。具體來說，我們提出了一種貪婪的單步提前 (OSA)暫存策略，其只會最小化下一個時段中的預期MSE。首先，考慮單一暫存器和單一伺服器系統，然後通過使用交替優化 （AO）方法優化訪問係數和暫存策略來解決由此產生的OSA MSE最小化問題。此外，我們考慮了具有訪問和存儲成本的多個暫存和多個伺服器系統，研究了這些成本對選擇最佳感測器的影響，以及由此產生的最小化問題屬於具有成本約束的非凸優化問題，其中涉及可以使用交替優化和逐次凸逼近 (SCA)方法有效求解的指標函數。最後，通過模擬證明了所提出方案的有效性。

關鍵字 - 分散式估計、暫存、感測器選擇、無線感測網路

Caching in wireless sensor networks (WSNs) enables in conserving the available resources by reducing the network-wide transmissions. The caching of the data gathered by the sensors facilitates users' access to the information and reduces network traffic, but can be challenging when the data size grows too rapidly over time or when the cache size is limited. However, in WSNs, sensor nodes are often deployed to achieve a common objective, such as the estimation of temperature in environmental monitoring or the intrusion detection. In this case, the cache need not contain the raw data gathered by the sensors, but can instead contain only information that is relevant to the underlying application. In this dissertation, a cross-layered caching policies are examined for the purpose of distributed estimation in WSNs.

Specifically, in distributed parameter estimation applications of WSNs, sensors first gather information about a common phenomenon and then forward the information to a fusion center where the final estimate is computed. By assuming that the parameters are correlated over time, the estimation quality at the fusion center can be improved by combining both present and past information, where the latter can be obtained from cached data. On the other hand, in WSNs, large amounts of data are produced continuously over time, and caching all the data collected from the sensors can be costly. Optimal sensor selection is one of the key ideas to overcome this excess in-network resource-draining. Different from conventional caching problems, in this dissertation, our goal is to reconstruct the sensors’ observations and our caching strategy is designed to minimize the long-term average mean-square error (MSE) of the final estimate by optimally selecting necessary sensors and cached information. Specifically, we propose a greedy one-step-ahead (OSA) caching strategy, which only minimizes the expected MSE in the next time slot. At first, a single-cache and a single-server system are considered and the resultant OSA MSE minimization problem is then solved by using the alternating optimization (AO) method for optimal accessing coefficients and caching strategies. Furthermore, we consider multi-cache and multi-server systems with accessing and storage costs. The impact of these costs in the selection of optimal sensors is studied and the resultant minimization problem falls under the non-convex optimization problem with cost constraints involving indicator functions that can be solved efficiently using alternating optimization and successive convex approximation (SCA) methods. The effectiveness of the proposed scheme is demonstrated through numerical simulations.

Keywords - Distributed estimation, caching, sensor selection, wireless sensor networks

Contents
Abstract i
Contents iii
1 Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Main Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Related Works 6
2.1 Caching for Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . .6
2.2 Distributed Parameter Estimation in Wireless Sensor
Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3 Optimized Caching and Data Access Strategies for Single-Cache and Single-Server System 13
3.1 System Model and Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . 13
3.2 One-Step-Ahead MSE-Minimization Problem for Caching and Data Access in a Single-Cache and Single-Server System . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4 Optimized Data Access and Caching Strategies for Multi-Cache and Multi-Server System with Cost Constraints 22
4.1 System Model and Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . 22
4.2 OSA MSE-Minimization for Caching and Data Access Problem in a Multi-Cache and Multi-Server System with Cost Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
5 Numerical Simulations 31
5.1 MSE Performance Comparisons of Single-Cache and Single-Server System with Numerical Simulations . . . . . . . . . . . . . . . . . . . . . .. . . . . . 32
5.2 MSE Performance Comparisons of Multi-Cache and Multi-Server System with Numerical Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . .38
6 Conclusion 43
Appendices 45
A Derivation of (3.19) 46
B Derivation of (4.11) 48
Bibliography 51
iv

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