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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >Collaborative Scheduling in Dynamic Environments Using Error Inference
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Collaborative Scheduling in Dynamic Environments Using Error Inference

机译：动态环境中使用错误推理的协同调度

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摘要

Due to the limited power constraint in sensors, dynamic scheduling with data quality management is strongly preferred in the practical deployment of long-term wireless sensor network applications. We could reduce energy consumption by turning off (i.e., duty cycling) sensor, however, at the cost of low-sensing fidelity due to sensing gaps introduced. Typical techniques treat data quality management as an isolated process for individual nodes. And existing techniques have investigated how to collaboratively reduce the sensing gap in space and time domain; however, none of them provides a rigorous approach to confine sensing error is within desirable bound when seeking to optimize the tradeoff between energy consumption and accuracy of predictions. In this paper, we propose and evaluate a scheduling algorithm based on error inference between collaborative sensor pairs, called CIES. Within a node, we use a sensing probability bound to control tolerable sensing error. Within a neighborhood, nodes can trigger additional sensing activities of other nodes when inferred sensing error has aggregately exceeded the tolerance. The main objective of this work is to develop a generic scheduling mechanism for collaborative sensors to achieve the error-bounded scheduling control in monitoring applications. We conducted simulations to investigate system performance using historical soil temperature data in Wisconsin-Minnesota area. The simulation results demonstrate that the system error is confined within the specified error tolerance bounds and that a maximum of 60 percent of the energy savings can be achieved, when the CIES is compared to several fixed probability sensing schemes such as eSense. And further simulation results show the CIES scheme can achieve an improved performance when comparing the metric of a prediction error with baseline schemes. We further validated the simulation and algorithms by constructing a lab test bench to emulate actual environment monitoring appl- cations. The results show that our approach is effective and efficient in tracking the dramatic temperature shift in dynamic environments.
机译：由于传感器中的功率限制有限，因此在长期无线传感器网络应用的实际部署中，强烈建议使用带有数据质量管理的动态调度。我们可以通过关闭（即占空比）传感器来减少能耗，但是由于引入了感应间隙，因此以低保真度为代价。典型技术将数据质量管理视为单个节点的隔离过程。现有技术已经研究了如何协同缩小时空领域的感知差距。然而，当寻求优化能量消耗和预测准确性之间的权衡时，它们都没有提供严格的方法来将感测误差限制在期望的范围内。在本文中，我们提出并评估了基于协同传感器对之间的错误推断的调度算法，称为CIES。在节点内，我们使用感知概率来控制可容忍的感知错误。在推断的感测错误已累计超过容限后，在邻域内，节点可以触发其他节点的其他感测活动。这项工作的主要目的是为协作传感器开发一种通用的调度机制，以实现监视应用程序中的错误限制的调度控制。我们使用威斯康星州-明尼苏达州地区的历史土壤温度数据进行了模拟，以调查系统性能。仿真结果表明，当将CIES与几种固定概率检测方案（例如eSense）进行比较时，系统误差被限制在指定的误差容限范围内，并且最多可节省60％的能源。进一步的仿真结果表明，当将预测误差的度量与基线方案进行比较时，CIES方案可以提高性能。我们通过构建一个实验室测试台来模拟实际的环境监控应用程序，从而进一步验证了仿真和算法。结果表明，我们的方法在跟踪动态环境中剧烈的温度变化方面是有效且高效的。

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