Lossy compression techniques are commonly used by long-term data-gathering applications that attempt to identify trends or other interesting patterns in an entire system, since a data packet need not always be completely and immediately transmitted to the sink. In the applications, sensors are instructed to periodically sense massive data, and the data collected by nearby sensor nodes is also highly correlated both in time and in space. To exploit the data correlation, a nonterminal sensor node jointly encodes its own sensed data and the data received from its nearby nodes. The tendency for these nodes to have a high spatial correlation means that these data packets can be efficiently compressed together using a rate-distortion strategy. In rate-distortion theory, the correlation can be captured using a rate-distortion function. If the data have a high spatial
correlation, as the coding rate increases its rate-distortion function will exponentially decrease rapidly due to higher compression. The rate-distortion slope indicates the reduction rate of the distortion with respect to the absolute rate. If the rate-distortion slope is steep, allocating few bits can already produce an acceptable compression quality, with allocating more bits only slightly further reducing the distortion. Based on
the above rate-distortion concept, this dissertation addresses the optimal rate-distortion allocation problem, which determines an optimal bit rate of each sensor based on the target overall distortion to minimize the network transmission cost. This dissertation proposes optimal rate-distortion allocation schemes for single-hop, three-tier clusterbased and general sensor networks. The optimal rate-distortion allocation for general sensor networks is also extended to a distributed version to meet the distributed nature of data generation. Based on the presented methods, a heuristic algorithm is proposed to build the most efficient data transmission structure to further reduce the transmission cost. The proposed methods are evaluated via simulations using real-world data datasets. The simulation results show that the optimal allocation strategy can reduce the transmission cost to 6∼15% of that for the uniform allocation scheme. Moreover, the proposed methods would provide a significant performance gain if the system has three properties: (i) sensors are densely deployed, (ii) sensors are irregularly distributed, and (iii) the data gathered at sensors exhibit large temporal and spatial variations

近年來，失真壓縮技術已被廣泛應用在長期收集大量連續資料的無線感知器網路系統以延長整體系統運作生命期，因為這些系統主要是收集長期的資料趨勢、分析有意義的統計數據、以及找出感興趣的特定資料樣板，所以通常可以允許少許的資料失真以及不需要及時的資料收集。在這些系統中，感知器被佈建來收集大量連續資料，而相鄰感知器的資料經常具備時間、空間的關連性，為了利用這些關聯性減少資料傳輸量，中間節點的感知器可將自己所收集的資料以及從鄰近節點所傳輸過來資料利用失真壓縮技術壓縮。過去的研究多著重在達到高壓縮率、有效省電以及分散式壓縮等議題，然而一個好的壓縮技術，均需輔以一個最佳化傳輸位元率分配的理論基礎，失真壓縮則是可以有效的透過Rate-distortion 的策略大量減少所需傳輸量。這篇論文研究透過rate-distortion最佳化分配，達到感知器系統省電的目的: 如何在滿足使用者對於資料失真度所能接受的需求下，最佳的決定如何分配各個感知器的壓縮位元率。一旦位元率能夠被準確的決定，感知器的資料就能夠很有效率的進行壓縮，達到傳輸最少的資料量，滿足使用者的查詢。本篇論文將rate-distortion 最佳化分配問題數學化，並且提出一個最佳化分配的數學解，為了要適應感知器網路的分散壓縮需求，我也根據數學最佳解提出一個分散式分配的策略，基於所提出的最佳化分配以及分散式分配策略，本篇論文也提出一個heuristic 演算法，從網路連結圖建立有效率的傳輸資料樹結構，更進一步的減少整體網路傳輸資料量。所提的方法均經由實際溫度資料驗證，實驗結果顯示最佳化分配以及分散式分配策略，的確能大量的減少整體網路傳輸資料量(跟均勻分配策略比較)。所提的方
法在下列網路條件下會更有效益: (1) 感知器分布密度高，(2) 感知器分布不均勻以及(3) 感知器所收集的資料變化大。

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