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Accident avoidance and prediction system using adaptive probabilistic threshold monitoring technique

机译：使用自适应概率阈值监控技术的事故避免和预测系统

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摘要

Identifying abnormal occasions in an observed area has been of the significant applications in Wireless Sensor Networks (WSNs) and the Internet of Things. Accidents and property harm can be maintained a strategic distance from if precise alarms are informed on time. In conventional checking techniques, a predefined threshold is given, and a signal is activated when the sensor perusing surpasses this threshold. This Single Threshold based Monitoring (STM) experiences the substandard nature of detected data, bringing about numerous false alarms. This work proposes an Adaptive Probabilistic Threshold Monitoring (APTM) technique for WSNs, where a signal is activated if the likelihood of the checked esteem being more significant than a predefined threshold (alpha) is more impressive than time delay (tau). The tight upper limits of the likelihood that controlled sum is more significant than the predetermined threshold are given. As indicated by the breaking points, probabilistic threshold-based algorithms for conglomeration checking are proposed. Broad execution assessment shows the adequacy of the proposed algorithms the proposed design centers on observing the driver's level of diversion by checking optical parameters, health condition, driving example of the driver. It additionally screens street and activity conditions, the sudden entry of animal on the roadways. By a broad experimental assessment utilizing original dataset, the proposed algorithms beat the STM strategy in term of false alarm rate MSE is 2db. (C) 2019 Elsevier B.V. All rights reserved.
机译：识别被观察区域中的异常情况已经成为无线传感器网络（WSN）和物联网中的重要应用。如果及时发出准确的警报，则可以将事故和财产伤害保持在战略距离之外。在传统的检查技术中，给出了预定义的阈值，并且当传感器的读值超过该阈值时激活信号。这种基于单阈值的监视（STM）会遇到检测到的数据不合标准的性质，从而导致大量错误警报。这项工作提出了一种用于WSN的自适应概率阈值监视（APTM）技术，如果被检查的自尊比预定义阈值（alpha）更重要的可能性比时间延迟（tau）更令人印象深刻，则激活信号。给出了受控和大于预定阈值的可能性的严格上限。如断点所示，提出了基于概率阈值的聚类检查算法。广泛的执行评估表明，拟议设计的重点在于通过检查驾驶员的光学参数，健康状况和驾驶员驾驶实例来观察驾驶员的转向程度，从而证明所提出算法的充分性。它还屏蔽了街道和活动条件，以及动物在道路上的突然进入。通过利用原始数据集进行的广泛实验评估，提出的算法在误报率MSE为2db方面优于STM策略。 （C）2019 Elsevier B.V.保留所有权利。

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