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Analyzing space-time sensor network data under suppression and failure in transmission

机译：在传输抑制和故障下分析时空传感器网络数据

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摘要

In this paper we present a fully model-based analysis of the effects of suppression and failure in data transmission with sensor networks. Sensor networks are becoming an increasingly common data collection mechanism in a variety of fields. Sensors can be created to collect data at very high temporal resolution. However, during periods when the process is following a stable path, transmission of such high resolution data would carry little additional information with regard to the process model, i.e., all of the data that is collected need not be transmitted. In particular, when there is cost to transmission, we find ourselves moving to consideration of suppression in transmission. Additionally, for many sensor networks, in practice, we will experience failures in transmission-messages sent by a sensor but not received at the gateway, messages sent but arriving corrupted. Evidently, both suppression and failure lead to information loss which will be reflected in inference associated with our process model. Our effort here is to assess the impact of such information loss under varying extents of suppression and varying incidence of failure. We consider two illustrative process models, presenting fully model-based analyses of suppression and failure using hierarchical models. Such models naturally facilitate borrowing strength across nodes, leveraging all available data to learn about local process behavior.
机译：在本文中，我们介绍了基于模型的传感器网络数据传输中抑制和故障影响的完全基于模型的分析。传感器网络正在成为各个领域中越来越普遍的数据收集机制。可以创建传感器以非常高的时间分辨率收集数据。但是，在过程遵循稳定路径的时间段内，这种高分辨率数据的传输将携带很少的关于过程模型的附加信息，即，无需传输收集的所有数据。尤其是在存在传输成本的情况下，我们发现自己开始考虑抑制传输。此外，对于许多传感器网络，在实践中，我们将遇到传感器发送但网关未收到的传输消息失败，消息已发送但到达损坏的消息。显然，抑制和失败都会导致信息丢失，这将反映在与我们的过程模型相关的推理中。我们在这里的工作是评估在不同程度的抑制和不同失败率下此类信息丢失的影响。我们考虑了两个说明性的过程模型，这些模型使用分层模型展示了完全基于模型的抑制和故障分析。这样的模型自然会促进节点之间的借用强度，从而利用所有可用数据来了解本地过程行为。

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