Large networks of ultra-low resolution sensors in buildings
Large networks of ultra-low resolution sensors in buildings
复制标题
建筑物中超低分辨率传感器的大型网络
DOI：
10.1109/kimas.2005.1427111
复制
发表时间：
2005
期刊：
影响因子：
--
通讯作者：
Christopher R. Wren
中科院分区：
文献类型：
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作者：
Christopher R. Wren
建筑物的居住者在从一个地方移动到另一个地方，站在角落里说话，或者在咖啡机旁闲逛时会产生模式。这些图案在建筑物的每一个物体上都留下了痕迹。即使是一张普通的地毯，最终也能通过它的磨损情况告诉你一些关于这些图案的信息。然而，我们的自动化系统在很大程度上对这些模式视而不见：电梯、供暖和制冷、照明、信息、安全和安保系统都依赖于人类将这些模式转化为行动。廉价的传感器网络可以感知这些模式，并为建筑物中的上下文敏感系统提供有用的信息。本文回顾了我们的一些工作系统，适应人们在建筑物中创建的模式。具体来说，我们将讨论室内传感器网络的自动几何校准，以及轻量级的行为模式发现。我们还提出了一些新的实验，说明了粗糙的，全球性的信息理解人类行为的重要性，在建筑物范围内。
The occupants of a building generate patterns as they move from place to place, stand at a corner talking, or loiter by the coffee machine. These patterns leave their mark on every object in a building. Even a lowly carpet will eventually be able to tell you something about these patterns by how it wears. However, our automated systems are largely blind to these patterns: elevator, heating and cooling, lighting, information, safety, and security systems all depend on humans to translate these patterns into action. A cheap network of sensors can sense these patterns and provide useful information to context sensitive systems in a building. This paper reviews some of our work on systems that adapt to the patterns that people create in a building. Specifically we will discuss automatic geometric calibration of indoor sensor networks, and light-weight discovery of behavior patterns. We also present some new experiments that illustrate the importance of coarse, global information for understanding human behavior on a building-wide scale.