Poland Michael P, Nugent Chris D, Wang Hui, Chen Liming
Computer Science Research Institute and School of Computing and Mathematics, Faculty of Computing and Engineering, University of Ulster, Newtownabbey, Northern Ireland, UK.
Technol Health Care. 2011;19(3):137-60. doi: 10.3233/THC-2011-0611.
Smart homes are living spaces facilitated with technology to allow individuals to remain in their own homes for longer, rather than be institutionalised. Sensors are the fundamental physical layer with any smart home, as the data they generate is used to inform decision support systems, facilitating appropriate actuator actions. Positioning of sensors is therefore a fundamental characteristic of a smart home. Contemporary smart home sensor distribution is aligned to either a) a total coverage approach; b) a human assessment approach. These methods for sensor arrangement are not data driven strategies, are unempirical and frequently irrational. This Study hypothesised that sensor deployment directed by an optimisation method that utilises inhabitants' spatial frequency data as the search space, would produce more optimal sensor distributions vs. the current method of sensor deployment by engineers. Seven human engineers were tasked to create sensor distributions based on perceived utility for 9 deployment scenarios. A Pure Random Search (PRS) algorithm was then tasked to create matched sensor distributions. The PRS method produced superior distributions in 98.4% of test cases (n=64) against human engineer instructed deployments when the engineers had no access to the spatial frequency data, and in 92.0% of test cases (n=64) when engineers had full access to these data. These results thus confirmed the hypothesis.
智能家居是配备了技术的居住空间，使个人能够在自己家中居住更长时间，而不是被安置在机构中。传感器是任何智能家居的基本物理层，因为它们生成的数据用于为决策支持系统提供信息，促进适当的执行器动作。因此，传感器的布置是智能家居的一个基本特征。当代智能家居传感器的分布要么采用a）全面覆盖方法；b）人工评估方法。这些传感器布置方法不是数据驱动的策略，缺乏经验且常常不合理。本研究假设，由一种利用居民空间频率数据作为搜索空间的优化方法指导的传感器部署，与当前工程师的传感器部署方法相比，将产生更优的传感器分布。七名人类工程师的任务是根据感知到的效用为9种部署场景创建传感器分布。然后，一个纯随机搜索（PRS）算法被用于创建匹配的传感器分布。当工程师无法获取空间频率数据时，在98.4%的测试用例（n=64）中，PRS方法生成的分布优于人类工程师指导的部署；当工程师完全可以获取这些数据时，在92.0%的测试用例（n=64）中也是如此。因此，这些结果证实了该假设。
