Today’s sensor network implementations often comprise various types of nodes connected with different types of networks. These and various other aspects influence the delay of transmitting data and therefore of out-of-order data occurrences. This turns into a crucial problem in time-sensitive applications where data must be processed promptly and decisions must be reliable. In this article, we research dynamic buffer sizing algorithms for multiple, distributed, and independent sources, which reorder event streams, thus enabling subsequent time-sensitive applications to work correctly. To be able to evaluate such algorithms, we had to record datasets first. Five novel dynamic buffer sizing algorithms were implemented and compared to state-of-the-art approaches in this domain. The evaluation has shown that the use of a dynamic time-out buffering method is preferable over a static buffer. The higher the variation of the network or other influences in the environment, the more necessary it becomes to use an algorithm that dynamically adapts its buffer size. These algorithms are universally applicable, easy to integrate in existing architectures, and particularly interesting for time-sensitive applications. Dynamic time-out buffering is still a trade-off between reaction time and out-of-order event compensation.

中文翻译：

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用于时间敏感应用程序的无序事件补偿的动态缓冲区大小

今天的传感器网络实现通常包括与不同类型网络连接的各种类型的节点。这些和各种其他方面影响传输数据的延迟，因此影响乱序数据的发生。这在时间敏感的应用程序中变成了一个关键问题，在这些应用程序中，必须及时处理数据并且决策必须可靠。在本文中，我们研究了针对多个、分布式和独立源的动态缓冲区大小算法，这些算法对事件流进行重新排序，从而使后续对时间敏感的应用程序能够正常工作。为了能够评估此类算法，我们必须首先记录数据集。实现了五种新颖的动态缓冲区大小算法，并与该领域的最新方法进行了比较。评估表明，使用动态超时缓冲方法优于静态缓冲。网络的变化或环境中的其他影响越大，就越有必要使用动态调整其缓冲区大小的算法。这些算法普遍适用，易于集成到现有架构中，对于时间敏感的应用程序特别有趣。动态超时缓冲仍然是反应时间和乱序事件补偿之间的权衡。对于时间敏感的应用程序特别有趣。动态超时缓冲仍然是反应时间和乱序事件补偿之间的权衡。对于时间敏感的应用程序特别有趣。动态超时缓冲仍然是反应时间和乱序事件补偿之间的权衡。