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

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

Monitoring the Quality of Information (QoI) for low-cost sensor networks

Mantaining a low cost sensor network calibrated for a certain time having trustable information is a highly complicated task. Thanks to redundant sensors in sensing devices and a sensor network, statistical tests can be applied in order to know whenever a calibration or a replacement should be done

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

Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks

Spatial regression of random fields based on potentially biased sensing information is proposed in this paper. One major concern in such applications is that since it is not known a-priori what the accuracy of the collected data from each sensor is, the performance can be negatively affected if the collected information is not fused appropriately. For example, the data collector may measure the phenomenon inappropriately, or alternatively, the sensors could be out of calibration, thus introducing random gain and bias to the measurement process. Such readings would be systematically distorted, leading to incorrect estimation of the spatial field. To combat this detrimental effect, we develop a robust version of the spatial field model based on a mixture of Gaussian process experts. We then develop two different approaches for Bayesian spatial field reconstruction: the first algorithm is the Spatial Best Linear Unbiased Estimator (S-BLUE), in which one considers the quadratic loss function and restricts the estimator to the linear family of transformations; the second algorithm is based on empirical Bayes, which utilises a two-stage estimation procedure to produce accurate predictive inference in the presence of "misbehaving" sensors. In addition, we develop the distributed version of these two approaches to drastically improve the computational efficiency in large-scale settings. We present extensive simulation results using both synthetic datasets and semi-synthetic datasets with real temperature measurements and simulated distortions to draw useful conclusions regarding the performance of each of the algorithms

SCAN: Multi-hop calibration for mobile sensor arrays

Urban air pollution monitoring with mobile, portable, low-cost sensors has attracted increasing research interest for their wide spatial coverage and affordable expenses to the general public. However, low-cost air quality sensors not only drift over time but also suffer from cross-sensitivities and dependency on meteorological effects. Therefore calibration of measurements from low-cost sensors is indispensable to guarantee data accuracy and consistency to be fit for quantitative studies on air pollution. In this work we propose sensor array network calibration (SCAN), a multi-hop calibration technique for dependent low-cost sensors. SCAN is applicable to sets of co-located, heterogeneous sensors, known as sensor arrays, to compensate for cross-sensitivities and dependencies on meteorological influences. SCAN minimizes error accumulation over multiple hops of sensor arrays, which is unattainable with existing multi-hop calibration techniques. We formulate SCAN as a novel constrained least-squares regression and provide a closed-form expression of its regression parameters. We theoretically prove that SCAN is free from regression dilution even in presence of measurement noise. In-depth simulations demonstrate that SCAN outperforms various calibration techniques. Evaluations on two real-world low-cost air pollution sensor datasets comprising 66 million samples collected over three years show that SCAN yields 16% to 60% lower error than state-of-the-art calibration techniques.</jats:p

Urban air pollution modelling with machine learning using fixed and mobile sensors

Detailed air quality (AQ) information is crucial for sustainable urban management, and many regions in the world have built static AQ monitoring networks to provide AQ information. However, they can only monitor the region-level AQ conditions or sparse point-based air pollutant measurements, but cannot capture the urban dynamics with high-resolution spatio-temporal variations over the region. Without pollution details, citizens will not be able to make fully informed decisions when choosing their everyday outdoor routes or activities, and policy-makers can only make macroscopic regulating decisions on controlling pollution triggering factors and emission sources. An increasing research effort has been paid on mobile and ubiquitous sampling campaigns as they are deemed the more economically and operationally feasible methods to collect urban AQ data with high spatio-temporal resolution. The current research proposes a Machine Learning based AQ Inference (Deep AQ) framework from data-driven perspective, consisting of data pre-processing, feature extraction and transformation, and pixelwise (grid-level) AQ inference. The Deep AQ framework is adaptable to integrate AQ measurements from the fixed monitoring sites (temporally dense but spatially sparse), and mobile low-cost sensors (temporally sparse but spatially dense). While instantaneous pollutant concentration varies in the micro-environment, this research samples representative values in each grid-cell-unit and achieves AQ inference at 1 km \times 1 km pixelwise scale. This research explores the predictive power of the Deep AQ framework based on samples from only 40 fixed monitoring sites in Chengdu, China (4,900 {\mathrm{km}}^\mathrm{2}, 26 April - 12 June 2019) and collaborative sampling from 28 fixed monitoring sites and 15 low-cost sensors equipped with taxis deployed in Beijing, China (3,025 {\mathrm{km}}^\mathrm{2}, 19 June - 16 July 2018). The proposed Deep AQ framework is capable of producing high-resolution (1 km \times 1 km, hourly) pixelwise AQ inference based on multi-source AQ samples (fixed or mobile) and urban features (land use, population, traffic, and meteorological information, etc.). This research has achieved high-resolution (1 km \times 1 km, hourly) AQ inference (Chengdu: less than 1% spatio-temporal coverage; Beijing: less than 5% spatio-temporal coverage) with reasonable and satisfactory accuracy by the proposed methods in urban cases (Chengdu: SMAPE \mathrm{<} 20%; Beijing: SMAPE \mathrm{<} 15%). Detailed outcomes and main conclusions are provided in this thesis on the aspects of fixed and mobile sensing, spatio-temporal coverage and density, and the relative importance of urban features. Outcomes from this research facilitate to provide a scientific and detailed health impact assessment framework for exposure analysis and inform policy-makers with data driven evidence for sustainable urban management.Open Acces

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

Developing a Methodology for Monitoring Personal Exposure to Particulate Matter in a Variety of Microenvironments

Adverse health effects from exposure to air pollution, although at present only partly understood, are a global challenge and of widespread concern. Quantifying human exposure to air pollutants is challenging, as ambient concentrations of air pollutants at potentially harmful levels are ubiquitous and subject to high spatial and temporal variability. At the same time, individuals have their very own unique activity-patterns. Hence exposure results from intertwined relationships between environmental and human systems add complexity to the assessment process. It is essential to develop a deeper understanding of individual exposure pathways and situations occurring in people’s everyday lives. This is important especially with regard to exposure and health impact assessment which provide the basis for public health advice and policy development. This thesis describes the development and application of a personal monitoring method to assess exposure to fine particulate matter in a variety of microenvironments. Tools and methods applied are tested with respect to feasibility, intrusiveness, performance and potential for future applications. The development of the method focuses on the application in everyday environments and situations in an attempt to capture as much of the total exposure as possible, across a complete set of microenvironments. Seventeen volunteers took part in the pilot study, collected data and provided feedback on methodology and tools applied. The low-cost particle counter applied showed good agreement with reference instruments when studied in two different environments. Based on the assessment of the two instruments functions to derive particle mass concentration from the original particle number counts have been defined. The application of the devices and tools received positive feedback from the volunteers. Limitations are mainly related to the non-weatherproof design of the particle counter. The collection of time-activity patterns with GPS and time-activity diaries is challenging and requires careful processing. Resulting personal exposure profiles highlight the influence of individual activities and contextual factors. Highest concentrations were measured in indoor environments where people also spent the majority of time. Differences between transport modes as well as between urban and rural areas were identified