Mobile Sensor Networks for Air Quality Monitoring in Urban Settings

With urban populations rapidly increasing and millions of deaths being attributed annually to air pollution, there is a critical need for a deeper understanding of urban air quality. The locality of urban emissions sources, and the specific topography of cities lead to a very heterogeneous pollutant concentration landscape, the details of which cannot be captured by traditional monitoring stations. Although highly accurate, these systems are large, heavy and very expensive, which leads to a very sparse distribution of measurements. Mobile sensor networks hold the potential to allow a paradigm shift in our understanding of urban air pollution, through an augmentation of the spatial resolution of measurements. The road to achieving reliable high quality information from these type of systems is, however, full of challenges. These start from the system design, as the task of developing robust mobile sensing networks for continuous urban monitoring is arduous in itself. The limitations of existing sensor technology is another important source of hard problems. Chemical sensors suffer from many issues that make their use in a mobile scenario non-trivial. These include: instability, cross-sensitivity, low signal-to-noise ratios, and slow dynamic response. The latter problem, in particular, is a tough challenge when considering a mobile scenario, as it leads to significant measurement distortion. The question of maintaining the calibration of chemical sensors is another essential issue that derives from their instability. Finally, the development of appropriate modeling techniques that would enable us to generate high-resolution pollution maps based on mobile sensor network data is a highly difficult problem due to the inherently dynamic and partial coverage of such systems. The aim of this thesis is to show the feasibility of mobile sensor networks for monitoring air quality and their ability to achieve the goal of pushing our understanding of urban air pollution. We have taken a holistic approach, by studying the end-to-end system, and addressing each of the aforementioned challenges. Using public transportation vehicles for mobility, we have developed and deployed a full-scale mobile sensor network for monitoring the air quality in the city of Lausanne, Switzerland. We have carefully considered all steps of the system design process, starting from the choice of targeted pollutants, sensor selection, node design, server architecture, and system operation. For addressing the problem of mobility-caused distortion, we created a rigorous wind tunnel experimental set-up to study this effect and the techniques for mitigating it. We propose using deconvolution for recovering the underlying pollutant signal. Since the performance of this approach is limited by the signal-to-noise ratio of the measurements, we propose using an active sniffer to enhance the quality of the raw signal. On the topic of sensor calibration, we propose two improvements to online rendezvous calibration methodology. The first one is a model-based approach, which considers the use of more sophisticated sensor models, which are more faithful to the complex behavior of chemical sensors. The second one proposes the use of a pre-processing step, in which the mobile data is deconvolved. Finally, we study the problem of generating high-resolution maps based on mobile data. We propose five statistical modeling methods that use a heterogeneous list of explanatory variables

Teaching of an Acrobatic Maneuver to an Aerial Robot

In this project, it is desired to learn different acrobatic maneuvers through a set of demonstrations shown by an expert pilot. Due to the complexity of the vehicle's dynamics, at first step, it is necessary to find an appropriate set of states that can best represent an acrobatic maneuver. Next, a change of frame of reference from the East-North-Up Coordinates System to Aircraft-Body Coordinates System is applied on the whole demonstration dataset to give a more accurate definition of the maneuver and to handle the problem associated with different starting positions and orientations. To increase the performance of the algorithm, the demonstration data points are filtered and refined. After data preprocessing, the whole motion is encoded using Gaussian Mixture, and finally, an analysis of the model performance is made together with a discussion on the ways in which such a model could be used to control the aircraft

Enhancing Measurement Quality through Active Sampling in Mobile Air Quality Monitoring Sensor Networks

In recent years, a growing number of research groups have targeted the development and deployment of networks using low-cost chemical sensors for monitoring air quality. Due to economical reasoning, most of these systems make use of some sort of mobility to increase spatial coverage. The effect of mobility on measurement quality has, however, been largely neglected. The long response time of the chemical sensors typically used for this type of application, in conjunction with platform mobility, leads to significant signal distortion. While this problem can be addressed through signal deconvolution techniques, their effectiveness is limited by the typical poor Signal-to-Noise Ratio (SNR) of the measured signal. In this paper, we study the possibility of enhancing the measurement quality of chemical sensors through the use of active sampling (or sniffing). We propose different sniffer designs, employing both fans and pumps as actuators. Using a rigorous experimental framework, inside a wind tunnel, we study the ability of active samplers to increase measurement SNR, and thus indirectly to improve sensor dynamic response. We obtain a significant and consistent improvement in SNR for one of our pump-based sniffer designs. Finally, we validate the robustness of this signal enhancement in real-world conditions through an outdoor car-based experiment

Mitigating Slow Dynamics of Low-Cost Chemical Sensors for Mobile Air Quality Monitoring Sensor Networks

The last decade has seen a growing interest in air quality monitoring using networks of wireless low-cost sensor platforms. One of the unifying characteristics of chemical sensors typically used in real-world deployments is their slow response time. While the impact of sensor dynamics can largely be neglected when considering static scenarios, in mobile applications chemical sensor measurements should not be considered as point measurements (i.e. instantaneous in space and time). In this paper, we study the impact of sensor dynamics on measurement accuracy and locality through systematic experiments in the controlled environment of a wind tunnel. We then propose two methods for dealing with this problem: (i) reducing the effect of the sensor's slow dynamics by using an open active sampler, and (ii) estimating the underlying true signal using a sensor model and a deconvolution technique. We consider two performance metrics for evaluation: localization accuracy of specific field features and root mean squared error in field estimation. Finally, we show that the deconvolution technique results in consistent performance improvement for all the considered scenarios, and for both metrics, while the active sniffer design considered provides an advantage only for feature localization, particularly for the highest sensor movement speed

High Resolution Air Pollution Maps in Urban Environments Using Mobile Sensor Networks

We propose three modeling methods using a mobile sensor network to generate high spatio-temporal resolution air pollution maps for urban environments. In our deployment in Lausanne (Switzerland), dedicated sensing nodes are anchored to the public buses and measure multiple air quality parameters including the Lung Deposited Surface Area (LDSA), a state of the art metric for quantifying human exposure to ultrafine particles. In this paper, our focus is on generating LDSA maps. In particular, since the sensor network coverage is spatially and temporally dynamic, we leverage models to estimate the values for the locations and times where the data are not available. We first discretize the area topologically based on the street segments in the city and we then propose the following three prediction models: i) a log-linear regression model based on nine meteorological (e.g., temperature and precipitations) and gaseous (e.g., NO2 and CO) explanatory variables measured at two static stations in the city, ii) a novel network-based log-linear regression model that takes into account the LDSA values of the most correlated streets and also the nine explanatory variables mentioned above, iii) a novel Probabilistic Graphical Model (PGM) in which each street segment is considered as one node of the graph, and inference on conditional joint probability distributions of the nodes results in estimating the values in the nodes of interest. More than 44 millions of geo- and time- stamped LDSA measurements (i.e., more than 14 months of real data) are used in this paper to evaluate the proposed modeling approaches in various time resolutions (hourly, daily, weekly and monthly). The results show that the three approaches bring significant improvements in R2 , RMSE and FAC metrics compared to a baseline K-Nearest Neighbor method

Extending Urban Air Quality Maps Beyond the Coverage of a Mobile Sensor Network: Data Sources, Methods, and Performance Evaluation

Targeting the problem of generating high-resolution air quality maps for cities, we leverage four different sources of data: (i) in-situ air quality measurements produced by our mobile sensor network deployed on public transportation vehicles, (ii) explanatory air-quality and meteorological variables obtained from two static monitoring stations, (iii) land-use data of the city, and (iv) traffic statistics. We propose two novel approaches for estimating the targeted pollutant level at desired time-location pairs, extending also to areas of the city that are beyond the coverage of our mobile sensor network. The first is a log-linear regression model which is built over a virtual dependency graph based on land-use data. The second is a deep learning framework that automatically captures the dependencies of the data based on autoencoders. We have evaluated the two proposed approaches against three canonical modeling techniques considering metrics of coefficient of determination (R-squared), root mean square error (RMSE), and the fraction of predictions within a factor of two of observations (FAC2). Using more than 45 million real measurements in the models, the results show consistently superior performance in respect to the canonical techniques

Model-based Rendezvous Calibration of Mobile Sensor Networks for Monitoring Air Quality

Mobile Wireless Sensor Networks (WSNs) hold the potential to constitute a real game changer for our understanding of urban air pollution, through a significant augmentation of spatial resolution in measurement. However, temporal drift, crosssensitivity and effects caused by varying environmental conditions (e.g., temperature) in low-cost chemical sensors (typically used in mobile WSNs) pose a tough challenge for reliable calibration. Based on state-of-the-art rendezvous calibration methods, we propose a novel model-based method for automatically estimating the baseline and gain characteristics of low-cost chemical sensors taking temporal drift and temperature dependencies of the sensors into account. The performance of our algorithm is evaluated using data gathered by our long-term mobile sensor network deployment, developed within the Nano-Tera.ch OpenSense II project in Lausanne, Switzerland. We show that, in a realistic context of sparse and irregular rendezvous events, our method consistently improves rendezvous calibration performance for single-hop online calibration

Indoor Navigation Research with the Khepera III Mobile Robot: An Experimental Baseline with a Case-study on Ultra-wideband Positioning

Recent substantial progress in the domain of indoor positioning systems and a growing number of indoor location-based applications are creating the need for systematic, efficient, and precise experimental methods able to assess the localization and perhaps also navigation performance of a given device. With hundreds of Khepera III robots in academic use today, this platform has an important potential for single- and multi-robot localization and navigation research. In this work, we develop a necessary set of models for mobile robot navigation with the Khepera III platform, and quantify the robot’s localization performance based on extensive experimental studies. Finally, we validate our experimental approach to localization research by considering the evaluation of an ultra-wideband (UWB) positioning system. We successfully show how the robotic platform can provide precise performance analyses, ultimately proposing a powerful approach towards advancements in indoor positioning technology