Participatory Sensing or Participatory Nonsense?

Abstract

Citizen Science with mobile and wearable technology holds the possibility of unprecedented observation systems. Experts and policy makers are torn between enthusiasm and scepticism regarding the value of the resulting data, as their decision making traditionally relies on high-quality instrumentation and trained personnel measuring in a standardized way. In this paper, we (1) present an empirical behavior taxonomy of errors exhibited in non-expert smartphone-based sensing, based on four small exploratory studies, and discuss measures to mitigate their effects. We then present a large summative study (N=535) that compares instructions and technical measures to address these errors, both from the perspective of improvements to error frequency and perceived usability. Our results show that (2) technical measures without explanation notably reduce the perceived usability and (3) technical measures and instructions nicely complement each other: Their combination achieves a significant reduction in observed error rates while not affecting the user experience negatively.

Optimising Citizen-Driven Air Quality Monitoring Networks for Cities

“…The wide-spread VGI campaign sources can be useful for overcoming the issues connected to data quality, such as field duplication, data duplication and irregular spread of sensors, as pointed out by Clements et al [22] and Budde et al [37]. The optimisation method also helps answer the research question that need to be considered for planning deployment of sensor network (LUR in our case) to drive the data collection process.…”

“…In general (and as indicated by Lisjak et al [36]), the involvement of citizens not only provides an opportunity to close data gaps but also brings the policy-making process closer to people. Citizens are willing to get involved in air pollution monitoring studies and get aware of the ambient environment [22,37]. With the help of citizen participation, hundreds of low-cost sensors can be dispersed in an urban environment that can facilitate data collection simultaneously.…”

“…As discussed in previous studies related to low-cost sensors deployment [22,37], the field of low-cost sensors for environmental monitoring is in transition, and more work is needed to continue exploring the potential of low-cost sensors for air pollution monitoring. With the help of low-cost sensor systematic deployment initiatives by using citizen participation approaches, it is possible to bring forward a whole new system which anticipates the development of open data platforms (e.g., OK Labs [60]).…”

Credibility on Crowdsensing Data Acquisition

“…Finally, we identified studies that try to determine the incorrectness sources on crowdsensing ecosystems. Budde et al [165] presented an empirical study of errors exhibited in non-expert smartphone-based sensing by using four small exploratory studies. They analyzed and compiled ways in which human-errors may lead to incorrect data submission.…”

Analyzing Spatial and Temporal User Behavior in Participatory Sensing

“…In the second step, to realize the multi-purpose design principle, campaign authors define the sequence of atomic sensing tasks to be completed by the data collectors (see Figure 3). Each task contains a textual description, an optional picture (which can guide data collectors to improve the quality of the submission [66]) and other necessary parameters, depending on the task type (e.g., the different options in case of a multiple choice question). At the time of writing, the Citizense framework supports 12 different task types, grouped into sensory input (noise measurement, GPS location and WiFi fingerprint), multimedia input (picture) and human input (text input, bounded and unbounded numeric input, multiple choice question, date input and time input).…”