Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Open Issues in Calibrating Specific Sensor Technologies <s> The purpose for wireless sensor networks is to deploy low cost sensors with sufficient computing and communication capabilities to support networked sensing applications. Even when the sensors are properly calibrated at the time of their deployment, they develop drift in their readings leading to biased sensor measurements. Noting that a physical phenomenon in a certain area follows some spatio-temporal correlation, we assume that the sensors readings in that area are correlated. We also assume that the instantiations of drifts are uncorrelated. Based on these assumptions, and inspired by the resemblance of registration problem in radar target tracking with the bias error problem in wireless sensor networks, we follow a Bayesian framework to solve the Drift/Bias problem in wireless sensor networks. We present two methods for solving the drift problem in a densely deployed sensor network, one for smooth drifts and the other for unsmooth drifts. We also show that both methods successfully detect and correct sensor errors and extend the effective life time of the sensor network. <s> BIB001 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Open Issues in Calibrating Specific Sensor Technologies <s> The lifetime of wireless sensor networks (WSNs) has been significantly extended, while in long-term large-scale WSN applications, the increasing sensor drift has become a key problem affecting the reliability of sensory data. In this paper, we propose a blind online drift calibration framework based on subspace projection and sparse recovery for sensor networks in general-purpose monitoring. Temporal sparse Bayesian learning is used in the proposed method to estimate the sensor drift from under-sampled observations. The proposed method needs neither dense deployment nor the presence of a prior data model. Both simulated and real-world data set are used to evaluate the proposed method. Experimental results demonstrate that the proposed method can detect and recover the sensor drift when the number of drifted sensors are less than 20%, and when 40% sensors are drifted, the recovery rate is 80%. <s> BIB002 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Open Issues in Calibrating Specific Sensor Technologies <s> The potential of inexpensive Metal Oxide Semiconductor (MOS) gas sensors to be used for urban air quality monitoring has been the topic of increasing interest in the last decade. This paper discusses some of the lessons of three years of experience working with such sensors on a novel instrument platform (Small Open General purpose Sensor (SOGS)) in the measurement of atmospheric nitrogen dioxide and ozone concentrations. Analytic methods for increasing long-term accuracy of measurements are discussed, which permit nitrogen dioxide measurements with 95% confidence intervals of 20.0 μ g m - 3 and ozone precision of 26.8 μ g m - 3 , for measurements over a period one month away from calibration, averaged over 18 months of such calibrations. Beyond four months from calibration, sensor drift becomes significant, and accuracy is significantly reduced. Successful calibration schemes are discussed with the use of controlled artificial atmospheres complementing deployment on a reference weather station exposed to the elements. Manufacturing variation in the attributes of individual sensors are examined, an experiment possible due to the instrument being equipped with pairs of sensors of the same kind. Good repeatability (better than 0.7 correlation) between individual sensor elements is shown. The results from sensors that used fans to push air past an internal sensor element are compared with mounting the sensors on the outside of the enclosure, the latter design increasing effective integration time to more than a day. Finally, possible paths forward are suggested for improving the reliability of this promising sensor technology for measuring pollution in an urban environment. <s> BIB003 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Open Issues in Calibrating Specific Sensor Technologies <s> The measurement of air temperature is strongly influenced by environmental factors such as solar radiation, humidity, wind speed and rainfall. This is problematic in low-cost air temperature sensors, which lack a radiation shield or a forced aspiration system, exposing them to direct sunlight and condensation. In this study, we developed a machine learning-based calibration method for air temperature measurement by a low-cost sensor. An artificial neural network (ANN) was used to balance the effect of multiple environmental factors on the measurements. Data were collected over 305 days, at three different locations in Japan, and used to evaluate the performance of the approach. Data collected at the same location and at different locations were used for training and testing, and the former was also used for k-fold cross-validation, demonstrating an average improvement in mean absolute error (MAE) from 1.62 to 0.67 by applying our method. Some calibration failures were noted, due to abrupt changes in environmental conditions such as solar radiation or rainfall. The MAE was shown to decrease even when the data collected in different nearby locations were used for training and testing. However, the results also showed that negative effects arose when data obtained from widely-separated locations were used, because of the significant environmental differences between them. <s> BIB004 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Open Issues in Calibrating Specific Sensor Technologies <s> Abstract The emergence of low-cost, user-friendly and very compact air pollution platforms enable observations at high spatial resolution in near-real-time and provide new opportunities to simultaneously enhance existing monitoring systems, as well as engage citizens in active environmental monitoring. This provides a whole new set of capabilities in the assessment of human exposure to air pollution. However, the data generated by these platforms are often of questionable quality. We have conducted an exhaustive evaluation of 24 identical units of a commercial low-cost sensor platform against CEN (European Standardization Organization) reference analyzers, evaluating their measurement capability over time and a range of environmental conditions. Our results show that their performance varies spatially and temporally, as it depends on the atmospheric composition and the meteorological conditions. Our results show that the performance varies from unit to unit, which makes it necessary to examine the data quality of each node before its use. In general, guidance is lacking on how to test such sensor nodes and ensure adequate performance prior to marketing these platforms. We have implemented and tested diverse metrics in order to assess if the sensor can be employed for applications that require high accuracy (i.e., to meet the Data Quality Objectives defined in air quality legislation, epidemiological studies) or lower accuracy (i.e., to represent the pollution level on a coarse scale, for purposes such as awareness raising). Data quality is a pertinent concern, especially in citizen science applications, where citizens are collecting and interpreting the data. In general, while low-cost platforms present low accuracy for regulatory or health purposes they can provide relative and aggregated information about the observed air quality. <s> BIB005 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Open Issues in Calibrating Specific Sensor Technologies <s> The remarkable advances in sensing and communication technologies have introduced increasingly low-cost, smart and portable sensors that can be embedded everywhere and play an important role in environmental sensing applications such as air quality monitoring. These user-friendly wireless sensor platforms enable assessment of human exposure to air pollution through observations at high spatial resolution in near-realtime, thus providing new opportunities to simultaneously enhance existing monitoring systems, as well as engage citizens in active environmental monitoring. However, data quality from such platforms is a concern since sensing hardware of such devices is generally characterized by a reduced accuracy, precision, and reliability. Achieving good data quality and maintaining error free measurements during the whole system lifetime is challenging. Over time, sensors become subject to several sources of unknown and uncontrollable faulty data which comprise the accuracy of the measurements and yield observations far from the expected values. This paper investigates calibration of low-cost air quality sensors in a real sensor network deployment. The approach leverages on the availability of sensor arrays in a wireless node to estimate parameters that minimize the calibration error using fusion of data from multiple sensors. The obtained results were encouraging and show the effectiveness of the approach compared to a single sensor calibration. <s> BIB006
There is a lot of research in the communications area on wireless sensor lifetime. However, this does not take into account the lifetime of the sensor subsystem. Depending on the sensor application, sensors have different lifetimes. For example, air pollution low-cost sensors for WSNs based on metal-oxide technology are said to have an average lifetime no longer than a year and a half, and electro-chemical sensors no longer than a year. Moreover, the drift of these sensors is not well known after months of operation BIB003 . How these sensors drift, what their real lifetime is, after how long the sensors should be recalibrated are questions to be researched. Takruri et al. BIB001  have also investigated the drift in environmental sensors such as temperature sensors, explaining that detecting drifting sensors and correcting their measurements would increase the effective life of the network. Wang et al. BIB002 also stated the difficulty in separating the detection of drift and calibration process, and mentioned that more information such as temporal correlation of sensory data, statistical features, or some other prior knowledge can be employed to achieve a higher detection rate. Sensors from the same manufacturer in applications such as air pollution sometimes present high variability BIB006 BIB003 . For example, Peterson et al. BIB003 analyzed metal-oxide sensors and the effect of warm-up, time, and variability between the same sensors under the same environmental conditions in the calibration process, showing that sensors have to be calibrated regularly and highlighting the importance of calibration at a specific location. Learning which sensor technologies are more stable, what differences exist between sensors from different manufacturers for the same application, and under what conditions the sensors have to be calibrated will trigger the deployment of real applications. Studying temperature and relative humidity sensors, Yamamoto et al. BIB004 reported a mismatch of the calibration error produced in a calibration place and the error obtained in the prediction in another place with different environmental conditions. The same problem has been stated in air pollution low-cost sensors. Castell et al. BIB005 mentioned the effect of different environmental conditions when calibrating NO 2 and O 3 sensors in a real WSN deployment. Understanding the different cross-correlations between different phenomena and how these affect the calibration of a specific sensor still needs to be investigated.