Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Open Issues in Calibration Models <s> Air quality monitoring is extremely important as air pollution has a direct impact on human health. Low-cost gas sensors are used to effectively perceive the environment by mounting them on top of mobile vehicles, for example, using a public transport network. Thus, these sensors are part of a mobile network and perform from time to time measurements in each others vicinity. In this paper, we study three calibration algorithms that exploit co-located sensor measurements to enhance sensor calibration and consequently the quality of the pollution measurements on-the-fly. Forward calibration, based on a traditional approach widely used in the literature, is used as performance benchmark for two novel algorithms: backward and instant calibration. We validate all three algorithms with real ozone pollution measurements carried out in an urban setting by comparing gas sensor output to high-quality measurements from analytical instruments. We find that both backward and instant calibration reduce the average measurement error by a factor of two compared to forward calibration. Furthermore, we unveil the arising difficulties if sensor calibration is not based on reliable reference measurements but on sensor readings of low-cost gas sensors which is inevitable in a mobile scenario with only a few reliable sensors. We propose a solution and evaluate its effect on the measurement accuracy in experiments and simulation. <s> BIB001 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Open Issues in Calibration Models <s> Abstract The performances of several field calibration methods for low-cost sensors, including linear/multi linear regression and supervised learning techniques are compared. A cluster of ozone, nitrogen dioxide, nitrogen monoxide, carbon monoxide and carbon dioxide sensors was operated. The sensors were either of metal oxide or electrochemical type or based on miniaturized infra-red cell. For each method, a two-week calibration was carried out at a semi-rural site against reference measurements. Subsequently, the accuracy of the predicted values was evaluated for about five months using a few indicators and techniques: orthogonal regression, target diagram, measurement uncertainty and drifts over time of sensor predictions. The study assessed if the sensors were could reach the Data Quality Objective (DQOs) of the European Air Quality Directive for indicative methods (between 25 and 30% of uncertainty for O 3 and NO 2 ). In this study it appears that O 3 may be calibrated using simple regression techniques while for NO 2 a better agreement between sensors and reference measurements was reached using supervised learning techniques. The hourly O 3 DQO was met while it was unlikely that NO 2 hourly one could be met. This was likely caused by the low NO 2 levels correlated with high O 3 levels that are typical of semi-rural site where the measurements of this study took place. <s> BIB002 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Open Issues in Calibration Models <s> The diffusion of the Internet of Things paradigm, in the last few years, has led to the need of deploying and managing large-scale Wireless Sensor Networks (WSNs), composed by a multitude of geographically distributed sensors, like the ones needed for Smart City applications. The traditional way to manage WSNs is not suitable for this type of applications, because manually managing and monitoring every single sensor would be too expensive, time consuming and error prone. Moreover, unattended sensors may suffer of several issues that progressively make their measures unreliable and consequently useless. For this reason, several automatically techniques have been studied and implemented for the detection and correction of measurements from sensors which are affected by errors caused by aging and/or drift. These methods are grouped under the name of self-calibration techniques. This paper presents a distributed system, which combines an incremental machine learning technique with a non-linear Kalman Filter estimator, which allows to automatically re-calibrate sensors leveraging the correlation with measurements made by neighbor sensors. After the description of the used model and the system implementation details, the paper describes also the proof-of-concept prototype that has been built for testing the proposed solution. <s> BIB003 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Open Issues in Calibration Models <s> Abstract In this work the performances of several field calibration methods for low-cost sensors, including linear/multi linear regression and supervised learning techniques, are compared. A cluster of either metal oxide or electrochemical sensors for nitrogen monoxide and carbon monoxide together with miniaturized infra-red carbon dioxide sensors was operated. Calibration was carried out during the two first weeks of evaluation against reference measurements. The accuracy of each regression method was evaluated on a five months field experiment at a semi-rural site using different indicators and techniques: orthogonal regression, target diagram, measurement uncertainty and drifts over time of sensor predictions. In addition to the analyses for ozone and nitrogen oxide already published in Part A [1], this work assessed if carbon monoxide sensors can reach the Data Quality Objective (DQOs) of 25% of uncertainty set in the European Air Quality Directive for indicative methods. As for ozone and nitrogen oxide, it was found for NO, CO and CO 2 that the best agreement between sensors and reference measurements was observed for supervised learning techniques compared to linear and multilinear regression. <s> BIB004 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Open Issues in Calibration Models <s> Machine learning techniques will take an increasingly central role in the distributed sensing realm and specifically in smart cities scenarios. Pervasive air quality monitoring as one of the primary city service requires a significant effort in term of data processing for extracting the needed semantic value. In this paper, after briefly reviewing the emerging relevant literature, we compare several machine learning tools for the purpose of devising intelligent calibration components to be run on board or in cloud computing architectures for pollutant concentration estimation. Two cities field experiments provide the needed on field recorded datasets to validate the approaches. Results are discussed both in terms of performance and computational impact for the specific application. <s> BIB005
In the case of calibration using linear and nonlinear models in centralized off-line approaches, there is not a great knowledge on the size of the data set needed to calibrate the sensors. In general, the data set is divided into a training and a validation data set. There are few works that analyze what the minimum size of the data set should be in order to correctly calibrate a sensor in a real deployment. The minimum size of the data set depends on the kind of sensors and sometimes on the environmental conditions. The size of the training set is also important in mobile networks in which the sensors are in contact with reference nodes during a window of time BIB001 . The time window refers to the number of samples obtained during an interval of time. What should be the quality of the calibration in opportunistic or periodic calibration in mobile networks is another challenge that has to be addressed. In the past years, there has been an increasing number of papers researching more complex calibration techniques based on linear and nonlinear models for centralized, micro, non-blind, collocated, and off-line approaches. As previously mentioned, the initial models were based on linear models when the sensors were for temperature, humidity, or vibration. Light-point sensors were modeled with splines, and air pollution with multiple linear regression models. Some sensors present nonlinearities, because their response function is nonlinear or because of other causes such as drift, aging, or achieving their maximum dynamic range. Some recent works including BIB002 BIB004 BIB005 BIB003 proposed using deep learning models such as artificial neural networks or support-vector regression. These models are said to characterize better the nonlinearities than splines but at the cost of greater computational complexity. Comparative studies on the complexity of these techniques and models, which ones are appropriate, and how they perform on different families of sensors are needed. Most calibration techniques have high computational complexity that makes an on-line distributed approach difficult. High-performance light calibration techniques that can be implemented on-line in low-computational or in low-capability nodes are needed. Moreover, if the sensors have nonlinear responses, it is necessary to research how nonlinear models can be applied to on-line distributed approaches. Many times there is a lack of ground-truth nodes in the network. One of the possibilities in calibration in uncontrolled environment networks is to calibrate the sensors using an already calibrated node or interpolating (e.g., by kriging or other interpolation schemes) the ground-truth values in a point or location and calibrate using these values as reference values. A better knowledge of how the error produced by these techniques against a collocated ground-truth is needed. Finally, there is no specific repository dedicated to the calibration of low-cost sensors where one can find data sets or code used by the calibration research community to allow the comparison of algorithms, approaches, and calibration methodologies.