Sensing and Mining Urban Qualities in Smart Cities
Sensing and Mining Urban Qualities in Smart Cities
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DOI：
10.1109/aina.2017.14
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Danielle Griego;Varin Buff;Eric Hayoz;Izabela Moise;Evangelos Pournaras
中科院分区：
文献类型：
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作者：
Danielle Griego;Varin Buff;Eric Hayoz;Izabela Moise;Evangelos Pournaras
智能城市中物联网的出现质疑未来公民将如何看待他们的主要生活和工作环境，以及他们在其中可以体验到什么样的生活质量，例如日常压力水平。然而，感知和经历的压力水平是具有挑战性的度量来测量，并且在这种刺激丰富的环境中与潜在的神经元效应关系相关联甚至更具挑战性。物联网由智能手机和智能传感器等几种无处不在的设备支持，可以提供实时的上下文信息，这些信息可以被先进的数据科学方法用于生成有关智慧城市中城市质量的新见解以及如何改善这些信息。本研究的目的是显示主要因素，这影响居民的感知质量在智能城市配备了物联网的传感能力。为了实现这一目标，引入了一种用于智能城市的新型数据收集过程，该过程涉及（i）环境数据，例如噪声、灰尘、照度、温度、相对湿度，（ii）位置/移动性数据，例如GNSS和通过WiFi检测到的市民密度，以及（iii）通过智能手机中的市民响应收集的感知社交数据。这些细粒度的实时数据可以提供有关传感器测量的空间相关性以及所示的空间和公民相似性的宝贵见解。所示的数据分析揭示了压力水平和观察到的环境变化之间的显着联系。
The emergence of the Internet of Things in Smart Cities questions how the future citizens will perceive their predominant living and working environments and what quality of living they can experience within it, for instance the level of everyday stress. However, perception and experienced stress levels are challenging metrics to measure and are even more challenging to correlate with an underlying causal-effectual relationship in such stimulus abundant environments. The Internet of Things, enabled by several pervasive and ubiquitous devices such as smart phones and smart sensors, can provide real-time contextual information that can be used by advanced data science methodologies to generate new insights about urban qualities in Smart Cities and how they can be improved. The goal of this study is to show the predominant factors, which influence perceptual qualities of inhabitants in a Smart City equipped with sensing capabilities by the Internet of Things. To serve this goal, a novel data collection process for Smart Cities is introduced that involves (i) environmental data, such noise, dust, illuminance, temperature, relative humidity, (ii) location/mobility data, such as GNSS and citizens density detected via WiFi, and (iii) perceptual social data collected by citizens' responses in smart phones. These fine-grained real-time data can provide invaluable insights about the spatial correlations of the sensor measurements as well as the spatial and citizens' similarity illustrated. The data analysis illustrated reveals significant links between stress level and environmental changes observed.