Creating a room connectivity graph of a building from per-room sensor units
Creating a room connectivity graph of a building from per-room sensor units
复制标题
从每个房间的传感器单元创建建筑物的房间连接图
DOI：
10.1145/2422531.2422563
复制
发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Ellis C
中科院分区：
文献类型：
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作者：
Ellis C
传感器和执行器网络通常安装在建筑物中，用于照明和气候控制等能源相关应用。这样的系统需要关于所部署的硬件的元数据（例如，每个硬件在哪个房间中，每个房间的功能是什么）以便有效地操作。在本文中，我们提出了自动确定这样的元数据的方法，特别是房间连接图（即，哪些房间共用门口/内窗）。至关重要的是，我们的方法只适用于每个房间的一个传感器单元，不需要任何传感器的特殊放置，因此可以处理现有广泛部署的应用程序（如防盗报警器）的数据。我们将这种方法应用于30天的数据集，从单个每个房间的传感器单元部署在两个住宅在英国。房间连通性是基于以下因素确定的：房间之间的人造光的溢出;由于房间之间的移动而引起的占用检测;以及两者的融合。这两种技术的融合被证明比单独使用任何一种技术都更好，具有93%的真阳性率和0.5%的假阳性率（两家的合计），并且收敛时间不到一周。
Sensor and actuator networks are often installed in buildings for energy-related applications such as lighting and climate control. Such systems require metadata about the deployed hardware (e.g. which room each is in, what the function of each room is) in order to operate effectively. In this paper we present methods to automatically determine such metadata, in particular the room connectivity graph (i.e., which rooms share a doorway/interior window). Crucially, our method works with just one sensor unit per room, does not require special placement of any of the sensors, and can therefore work on data from existing widely-deployed applications (such as burglar alarms). We apply this method to a 30-day data set from single per-room sensor units deployed in two residential homes in the United Kingdom. Room connectivity is determined based on: spillover of artificial light between rooms; occupancy detections due to movement between rooms; and a fusion of the two. The fusion of both techniques is shown to work better than either technique alone, with a 93% true positive rate and 0.5% false positive rate (aggregate across both houses), and a convergence time of under a week.
用于室内环境拓扑测绘和定位的可穿戴接口
DOI：
10.1007/11752967_5
发表时间：
2006
期刊：
2009 IEEE 12th International Conference on Computer Vision
影响因子：
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作者：
Grant Schindler;Christian Metzger;Thad Starner
通讯作者：
Thad Starner
DOI：
10.1109/percom.2003.1192734
发表时间：
2003
期刊：
Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003).
影响因子：
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作者：
R. Harle;A. Hopper
通讯作者：
A. Hopper
智能蓝图：自动生成家庭及其内部设备的地图
DOI：
10.1007/978-3-642-31205-2_9
发表时间：
2012
期刊：
2009 IEEE 12th International Conference on Computer Vision
影响因子：
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作者：
Jiakang Lu;K. Whitehouse
通讯作者：
K. Whitehouse