Crowdsensing Smart Ambient Environments and Services

Publication Type:

Journal Article

Source:

Transactions in GIS, Volume 20, Issue 3, p.382–398 (2016)

URL:

http://grantmckenzie.com/academics/McKenzie_TGIS2016_2.pdf

Keywords:

crowdsensing, semantics, smart city, volunteered geographic 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 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