Crowdsensing smart ambient environments and services

Abstract

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 ondemand 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. K E Y W O R D S crowdsensing, smart city, semantics, volunteered geographic services 382 |

ISED: Constructing a high-resolution elevation road dataset from massive, low-quality in-situ observations derived from geosocial fitness tracking data

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PrivyTo: A privacy‐preserving location‐sharing platform

“…The number of platforms encouraging people to share their location has grown significantly over the past decade and so too has the body of literature pertaining to this topic. Much of this work discusses privacy concerns through the lenses of passive (Pagani & Malacarne, 2017; Regalia, McKenzie, Gao, &, Janowicz, 2016) or active (Kummer, Ryschka, & Bick, 2018; McKenzie, Janowicz, & Seidl, 2016) location data collection. A considerable literature investigates the reasons why people choose to share their personal location information (Lindqvist, Cranshaw, Wiese, Hong, &, Zimmerman, 2011).…”

GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond

“…The sheer amount of data collected via these devices, as well as the heterogeneity of the actual content, make these data particularly well suited to the analysis through novel techniques situated in GeoAI (Martin et al 2018). Social sensing and semantic signatures have contributed to fields such as public health (Chaix 2018), activity prediction (Regalia et al 2016), and privacy preservation (Khan et al 2019), to name a few.…”