Distributed Systems Group, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, Groningen 9747 AG, The Netherlands.
Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Daerah Istimewa Yogyakarta 55281, Indonesia.
Sensors (Basel). 2018 Mar 6;18(3):796. doi: 10.3390/s18030796.
Smart spaces are those that are aware of their state and can act accordingly. Among the central elements of such a state is the presence of humans and their number. For a smart office building, such information can be used for saving energy and safety purposes. While acquiring presence information is crucial, using sensing techniques that are highly intrusive, such as cameras, is often not acceptable for the building occupants. In this paper, we illustrate a proposal for occupancy detection which is low intrusive; it is based on equipment typically available in modern offices such as room-level power-metering and an app running on workers' mobile phones. For power metering, we collect the aggregated power consumption and disaggregate the load of each device. For the mobile phone, we use the Received Signal Strength (RSS) of BLE (Bluetooth Low Energy) nodes deployed around workspaces to localize the phone in a room. We test the system in our offices. The experiments show that sensor fusion of the two sensing modalities gives 87-90% accuracy, demonstrating the effectiveness of the proposed approach.
智能空间能够感知自身状态并做出相应的反应。在这种状态下,存在的中心要素之一就是人类及其数量。对于智能办公大楼来说,这些信息可以用于节能和安全目的。虽然获取存在信息至关重要,但使用像摄像头这样高度侵入性的传感技术,往往是建筑物使用者无法接受的。在本文中,我们提出了一种低侵入性的占用检测方案;它基于现代办公室中通常可用的设备,如房间级电量计和运行在工作人员手机上的应用程序。对于电量计,我们收集汇总的功耗并对每个设备的负载进行分解。对于手机,我们使用部署在工作空间周围的 BLE(蓝牙低能耗)节点的接收信号强度 (RSS) 来定位手机在房间内的位置。我们在办公室进行了系统测试。实验表明,两种传感模式的传感器融合可以达到 87-90%的准确率,证明了所提出方法的有效性。