環境監測通常使用智能資料分析演算法，如機器學習，通過分析環境資料來感知狀態，環境資料由間歇性網絡系統收集並傳遞到中央伺服器進行資料分析程序。由於收集的資料會隨時間變化，因此這些資料發送到中央伺服器的持續時間（稱為資料新鮮度）對於時間敏感的應用程式至關重要，例如空氣污染和廢水監測。雖然間歇性系統由間歇性環境能源供電並廣泛用於環境監測，但具有易於部署和維護成本低的優點，但與能源無關的性質使其難以處理對時間敏感的工作。在本論文中，我們提出了一種離線解決方案來估計傳輸收集到的資料的性能上限，並提出一種在線解決方案來處理間歇性網絡系統上的這些資料。所提出的解決方案通過一系列實驗進行評估，並優於兩種最先進的策略。

Environmental monitoring often uses smart data analysis algorithms, such as machine learning methods, to perceive the status by analyzing the environmental data, which are collected by the networked intermittent systems (NIS) and passed to a central server for the data analysis procedure. As the collected data would vary across time, the durations of these data being sent to the central server (referred to as data freshness) are critical for timing-sensitive applications, e.g., air pollution and wastewater monitoring. While intermittent systems, which are powered by intermittent ambient energy sources and are widely adopted in environmental monitoring, have the advantages of easy deployment with lower maintenance costs, the energy-agnostic nature makes it hard to handle the timing-sensitive works. In this thesis, we propose an offline solution to estimate the upper performance bound for transmitting the collected data and an online solution to handle these data on NIS. The proposed solutions are evaluated with a series of experiments and outperform the two state-of-the-art strategies.

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