Up-to-date information on urban air pollution is of great importance for health protection agencies to assess air quality and provide advice to the general public in a timely manner. In particular, ultrafine particles (UFPs) are widely spread in urban environments and may have a severe impact on human health. However, the lack of knowledge about the spatio-temporal distribution of UFPs hampers profound evaluation of these effects. In this paper, we analyze one of the largest spatially resolved UFP data set publicly available today containing over 25 million measurements. We collected the measurements throughout more than a year using mobile sensor nodes installed on top of public transport vehicles in the city of Zurich, Switzerland. Based on these data, we develop land-use regression models to create pollution maps with a high spatial resolution of 100m × 100 m. We compare the accuracy of the derived models across various time scales and observe a rapid drop in accuracy for maps with subweekly temporal resolution. To address this problem, we propose a novel modeling approach that incorporates past measurements annotated with metadata into the modeling process. In this way, we achieve a 26% reduction in the root-mean-square error-a standard metric to evaluate the accuracy of air quality models-of pollution maps with semi-daily temporal resolution. We believe that our findings can help epidemiologists to better understand the adverse health effects related to UFPs and serve as a stepping stone towards detailed real-time pollution assessment.

Testbeds are indispensable for debugging and evaluating wireless embedded systems. While existing testbeds provide ample opportunities for realistic, large-scale experiments, they are limited in their ability to closely observe and control the distributed operation of resource-constrained nodes - access to the nodes is restricted to the serial port. This paper presents FlockLab, a testbed that overcomes this limitation by allowing multiple services to run simultaneously and synchronously against all nodes under test in addition to the traditional serial port service: tracing of GPIO pins to record logical events occurring on a node, actuation of GPIO pins to trigger actions on a node, and high-resolution power profiling. FlockLab's accurate timing information in the low microsecond range enables logical events to be correlated with power samples, thus providing a previously unattained level of visibility into the distributed behavior of wireless embedded systems. In this paper, we describe FlockLab's design, benchmark its performance, and demonstrate its capabilities through several real-world test cases.

Up-to-date information on urban air pollution, such as reliable pollution maps, is of great importance for health protection agencies to timely assess the air quality situation and provide advice to the general public. Ultrafine particles (UFPs) are widely spread in urban environments and believed to have severe impact on the human health. However, the lack of spatially resolved data hampers profound evaluation of these effects. In this work, we introduce one of the largest spatially resolved UFP data set available today, with over 25 million measurements to build high-resolution pollution maps for an urban area of 100 km2. The data is collected throughout more than one year using mobile sensor nodes, which are installed on top of public transport vehicles in the city of Zurich, Switzerland. We develop land-use regression models to create pollution maps with a high spatial resolution and study their temporal resolution limit.

Developing, testing, debugging, and evaluating communication protocols for low-power wireless networks is a long and cumbersome task. Simulators can be helpful in the early stages of development, but their models of hardware components and the wireless channel are often rather simplistic and hence cannot substitute experiments on real sensor node platforms. The resources available on common platforms are however very limited, and so are the possibilities for non-intrusive debugging and testing. With most existing testbeds it is only possible to collect information from the serial port, which requires adding highly intrusive logging statements that alter the timing behavior of the software running on the nodes. This is particularly detrimental to the operation of time-critical components, such as radio drivers, media access control (MAC) protocols, and certain flooding protocols [2], hindering their testbed-assisted development.

A vital factor for a successful deployment of sensor nodes is testing of all system aspects in a realistic setup. This work presents a testbed architecture which allows for detailed monitoring and stimulation of a wireless sensor node. In particular, time-accurate state extraction and power measurements are provided in a distributed, yet synchronized context. The FlockLab testbed architecture provides a distributed lab instrument, where detailed observations of every sensor node enable thorough testing. Software services allow for formulating testcases and reliable test data collection.