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Abstract. This paper presents a mobile, low-cost particulate matter sensing approach for the use in Participatory Sensing scenarios. It shows that cheap commercial o-the-shelf (COTS) dust sensors can be used in distributed or mobile personal measurement devices at a cost one to two orders of magnitude lower than that of current hand-held solutions, while reaching meaningful accuracy. We conducted a series of experiments to juxtapose the performance of a gauged high-accuracy measurement device and a cheap COTS sensor that we tted on a Bluetooth-enabled sensor module that can be interconnected with a mobile phone. Calibration and processing procedures using multi-sensor data fusion are presented, that perform very well in lab situations and show practically relevant results in a realistic setting. An on-the-y calibration correction step is proposed to address remaining issues by taking advantage of co-located measurements in Participatory Sensing scenarios. By sharing few measurement across devices, a high measurement accuracy can be achieved in mobile urban sensing applications, where devices join in an ad-hoc fashion. A performance evaluation was conducted by co-locating measurement devices with a municipal measurement station that monitors particulate matter in a European city, and simulations to evaluate the on-the-y cross-device data processing have been done.

Wireless Sensing and Radio Identification systems have undergone many innovations during the past years. This has led to short product lifetimes for both software and hardware compared to classical industries. However, especially industries dealing with long-term support of products, e.g. of industrial machinery, and product lifetime of 40+ years may especially profit from an Internet of Things. Motivated by a practical industrial servicing use case this paper shows how we hope to make equally sustainable IoT solutions by employing a model driven software development approach based on code generation for multi-protocol web service gateways.

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.