Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Open Calibration Data Sets <s> We propose CaliBree, a self-calibration system for mobile wireless sensor networks. Sensors calibration is a fundamental problem in a sensor network. If sensor devices are not properly calibrated, their sensor readings are likely of little use to the application. Distributed calibration is challenging in a mobile sensor network, where sensor devices are carried by people or vehicles, mainly for three reasons: i)the sensing contact time, i.e., the amount of time nodes are within the sensing range of each other, can be very limited, requiring a quick and efficient calibration technique; ii)for scalability and ease of use, the calibration algorithm should not require manual intervention; iii)the computational burden must be low since some sensor platforms have limited capabilities. In this paper we propose CaliBree, a distributed, scalable, and lightweight calibration protocol that applies a discrete average consensus algorithm technique to calibrate sensor nodes. CaliBree is shown to be effective through experimental evaluation using embedded wireless sensor devices, achieving high calibration accuracy. <s> BIB001 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Open Calibration Data Sets <s> Low cost gas multisensor devices can represent an efficient solution for densifying the sparse urban air pollution monitoring mesh. In a previous work, we proposed and evaluated the calibration of such a device using short term on-field recorded data for the benzene pollution quantification. In this work, we present and discuss the results obtained for CO, NO2 and total NOx pollutants concentration estimation with the same set up. Conventional air pollution monitoring station is used to provide reference data. We show how a multivariate calibration can be achieved with the use of two weeks long on-field data recording and neural regression systems. Also for these pollutants, no significant performance boost was detectable when longer recordings were used. The influence of an appropriate feature selection for achieving optimal performances is also discussed comparing long term performance results of the obtained calibrations. Benefits and issues of multivariate correlation based calibration are evaluated during one year long measurement campaign. <s> BIB002 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Open Calibration Data Sets <s> We consider the problem of joint sensor calibration and target signature estimation using distributed measurements over a large-scale sensor network. Specifically, we develop a new Distributed Signature Learning and Node Calibration algorithm (D-SLANC) which simultaneously estimates source signal's signature and estimates calibration parameters local to each sensor node. We model the sensor network as a connected graph and make use of the gossip-based distributed consensus to update the estimates at each iteration of the algorithm. The algorithm is robust to link and node failures. We prove convergence of the algorithm to the centralized data pooling solution. We compare performance with the Cramer-Rao bound, and study the scaling performance of both the CR bound and the D-SLANC algorithm. The algorithm has application to classification, target signature estimation, and blind calibration in large sensor networks. <s> BIB003 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Open Calibration Data Sets <s> Abstract Sensor drift remains to be the most challenging problem in chemical sensing. To address this problem we have collected an extensive dataset for six different volatile organic compounds over a period of three years under tightly controlled operating conditions using an array of 16 metal-oxide gas sensors. The recordings were made using the same sensor array and a robust gas delivery system. To the best of our knowledge, this is one of the most comprehensive datasets available for the design and development of drift compensation methods, which is freely reachable on-line. We introduced a machine learning approach, namely an ensemble of classifiers, to solve a gas discrimination problem over extended periods of time with high accuracy rates. Experiments clearly indicate the presence of drift in the sensors during the period of three years and that it degrades the performance of the classifiers. Our proposed ensemble method based on support vector machines uses a weighted combination of classifiers trained at different points of time. As our experimental results illustrate, the ensemble of classifiers is able to cope well with sensor drift and performs better than the baseline competing methods. <s> BIB004 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Open Calibration Data Sets <s> Abstract We investigate optimal experiment selection to train a classifier on gas sensor arrays to get the maximal possible performance in a limited number of experiments. In gas sensing, while collecting data for a particular sensor array, one has to choose what gas and concentration level is going to be presented in the next experiment. It is an active decision by the operator selecting the experiments and training the classifiers. Can the algorithm be trained sooner rather than later? Can we minimize the costs of collecting the data in terms of the man-hour of the operator and the expenses of the experiment itself? Active control sampling provides a way to deal with the challenge of minimizing the calibration costs and is applicable to any situation where experimental selection is parameterized by an external control variable. Our results indicate that active sampling strategies can only improve a random selection of experiments over a wide range of concentration of gasses. However, random or uninformed selection is fairly close. Additionally, our active sampling methodology reveals that, when there is no prior knowledge about the range of concentrations to which the sensor will be exposed during real operation, sensor must be calibrated over the entire working range, not just high concentrations. In fact, our results show that it is especially important to include low concentrations in the calibration since the lack of these values would dramatically decrease the performance of the system. <s> BIB005 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Open Calibration Data Sets <s> To address drift in chemical sensing, an extensive dataset was collected over a period of three years. An array of 16 metal-oxide gas sensors was exposed to six different volatile organic compounds at different concentration levels under tightly-controlled operating conditions. Moreover, the generated dataset is suitable to tackle a variety of challenges in chemical sensing such as sensor drift, sensor failure or system calibration. The data is related to “Chemical gas sensor drift compensation using classifier ensembles”, by Vergara et al. [1], and “On the calibration of sensor arrays for pattern recognition using the minimal number of experiments”, by Rodriguez-Lujan et al. [2] ::: ::: The dataset can be accessed publicly at the UCI repository upon citation of: http://archive.ics.uci.edu/ml/datasets/Gas+Sensor+Array+Drift+Dataset+at+Different+Concentrations <s> BIB006 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Open Calibration Data Sets <s> Chemical multisensor devices need calibration algorithms to estimate gas concentrations. Their possible adoption as indicative air quality measurements devices poses new challenges due to the need to operate in continuous monitoring modes in uncontrolled environments. Several issues, including slow dynamics, continue to affect their real world performances. At the same time, the need for estimating pollutant concentrations on board the devices, espe- cially for wearables and IoT deployments, is becoming highly desirable. In this framework, several calibration approaches have been proposed and tested on a variety of proprietary devices and datasets; still, no thorough comparison is available to researchers. This work attempts a benchmarking of the most promising calibration algorithms according to recent literature with a focus on machine learning approaches. We test the techniques against absolute and dynamic performances, generalization capabilities and computational/storage needs using three different datasets sharing continuous monitoring operation methodology. Our results can guide researchers and engineers in the choice of optimal strategy. They show that non-linear multivariate techniques yield reproducible results, outperforming lin- ear approaches. Specifically, the Support Vector Regression method consistently shows good performances in all the considered scenarios. We highlight the enhanced suitability of shallow neural networks in a trade-off between performance and computational/storage needs. We confirm, on a much wider basis, the advantages of dynamic approaches with respect to static ones that only rely on instantaneous sensor array response. The latter have been shown to be best choice whenever prompt and precise response is needed. <s> BIB007
There is no centralized repository of open data sets for the calibration of low-cost sensors. Most of the data sets found on the Internet are data collected by sensors operating on real networks. For example, CRAWDAD BIB001 contains data from sensors such as temperature, accelerometers, location, or RF signal strength sensors. The major drawback of these repositories is that the data they contain are from applications and are not data collected in the calibration phase. This makes it difficult in most cases to use these data to investigate algorithms or calibration mechanisms. In the field of calibration, some authors have published data obtained from experiments or deployments designed for the calibration of sensors at WSNs. For example, the UCI machine learning repository BIB003 contains some data sets related to the machine learning algorithms used in the calibration of low-cost sensors. Examples are the data set published by Fonollosa et al. BIB006 used in their investigation of calibration of chemical sensors BIB004 BIB005 and the data set published by De Vito et al. BIB002 BIB007 and used in the calibration of air pollution sensors.