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Crowdsensing smart ambient environments and services

机译：众包智能环境和服务

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摘要

Whether it be Smart Cities, Ambient Intelligence, or the Internet of Things, current visions for future urban spaces share a common core, namely the increasing role of distributed sensor networks and the on-demand integration of their data to power real-time services and analytics. Some of the greatest hurdles to implementing these visions include security risks, user privacy, scalability, the integration of heterogeneous data, and financial cost. In this work, we propose a crowdsensing mobile-device platform that empowers citizens to collect and share information about their surrounding environment via embedded sensor technologies. This approach allows a variety of urban areas (e.g., university campuses, shopping malls, city centers, suburbs) to become equipped with a free ad-hoc sensor network without depending on proprietary instrumentation. We present a framework, namely the GeoTracer application, as a proof-of-concept to conduct multiple experiments simulating use-case scenarios on a university campus. First, we demonstrate that ambient sensors (e.g. temperature, pressure, humidity, magnetism, illuminance, and audio) can help determine a change in environment (e.g. moving from indoors to outdoors, or floor changes inside buildings) more accurately than typical positioning technologies (e.g. global navigation satellite system, Wi-Fi, etc.). Furthermore, each of these sensors contributes a different amount of data to detecting events. for example, illuminance has the highest information gain when trying to detect changes between indoors and outdoors. Second, we show that through this platform it is possible to detect and differentiate place types on a university campus based on inferences made through ambient sensors. Lastly, we train classifiers to determine the activities that a place can afford at different times (e.g. good for studying or not, basketball courts in use or empty) based on sensor-driven semantic signatures.
机译：无论是智慧城市，环境智能还是物联网，当前对未来城市空间的愿景都具有一个共同的核心，即分布式传感器网络的作用日益增强以及按需集成其数据以支持实时服务和分析。实现这些愿景的最大障碍包括安全风险，用户隐私，可伸缩性，异构数据的集成以及财务成本。在这项工作中，我们提出了一个拥挤的移动设备平台，该平台使公民能够通过嵌入式传感器技术收集和共享有关周围环境的信息。这种方法使各种市区（例如大学校园，购物中心，市中心，郊区）都可以配备免费的临时传感器网络，而不必依赖专有仪器。我们提出了一个框架，即GeoTracer应用程序，作为概念验证来在大学校园内进行多个模拟用例场景的实验。首先，我们证明环境传感器（例如温度，压力，湿度，磁性，照度和音频）可以比典型的定位技术更准确地帮助确定环境的变化（例如从室内移动到室外或建筑物内的地板变化）（例如全球导航卫星系统，Wi-Fi等）。此外，这些传感器中的每一个都贡献不同数量的数据来检测事件。例如，在尝试检测室内和室外之间的变化时，照度具有最高的信息增益。其次，我们表明，通过该平台，可以基于通过环境传感器做出的推断来检测和区分大学校园中的场所类型。最后，我们训练分类器，根据传感器驱动的语义特征来确定某个地方在不同时间可以进行的活动（例如，是否适合学习，使用中的篮球场或空旷的篮球场）。

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