Addressing the Challenges of Semantic Citizen-Sensing

The challenges of the sensor web have been well documented, and the use of appropriate semantic web technologies promises to offer potential solutions to some of these challenges (for example, how to represent sensor data, integrate it with other data sets, publish it, and reason with the data streams). To date a large amount of work in this area has focused on sensor networks based on “traditional” hardware sensors. In recent years, citizen sensing has became a relatively well-established approach for incorporating humans as sensors within a system. Often facilitated via some mobile platform, citizen sensing may incorporate observational data generated by hardware (e.g. a GPS device) or directly by the human observer. Such human observations can easily be imperfect (e.g. erroneous or fake), and sensor properties that would typically be used to detect and reason about such data, such as measurements of accuracy and sampling rate do not exist. In this paper we discuss our work as part of the Informed Rural Passenger project, in which the passengers themselves are our main source for transport related sensing (such as vehicle occupancy levels, available facilities). We discuss the challenges of incorporating and using such observational data in a real world system, and describe how we are using semantic web technologies, combined with models of provenance to address them.