Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Calibration Challenges <s> We describe an ad-hoc localization system for sensor networks and explain why traditional calibration methods are inadequate for this system. Building upon previous work, we frame calibration as a parameter estimation problem; we parameterize each device and choose the values of those parameters that optimize the overall system performance. This method reduces our average error from 74.6% without calibration to 10.1%. We propose ways to expand this technique to a method of autocalibration for localization as well as to other sensor network applications. <s> BIB001 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Calibration Challenges <s> Numerous factors contribute to errors in sensor measurements. In order to be useful, any sensor device must be calibrated to adjust its accuracy against the expected measurement scale. In large-scale sensor networks, calibration will be an exceptionally difficult task since sensor nodes are often not easily accessible and manual device-by-device calibration is intractable. In this paper, we present a two-phase post-deployment calibration technique for large-scale, dense sensor deployments. In its first phase, the algorithm derives relative calibration relationships between pairs of co-located sensors, while in the second phase, it maximizes the consistency of the pair-wise calibration functions among groups of sensor nodes. The key idea in the first phase is to use temporal correlation of signals received at neighboring sensors when the signals are highly correlated (i.e. sensors are observing the same phenomenon) to derive the function relating their bias in amplitude. We formulate the second phase as an optimization problem and present an algorithm suitable for localized implementation. We evaluate the performance of the first phase of the algorithm using empirical and simulated data. <s> BIB002 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Calibration Challenges <s> Calibration is the process of mapping raw sensor readings into corrected values by identifying and correcting systematic bias. Calibration is important from both off-line and on-line perspectives. Major objectives of calibration procedure include accuracy, resiliency against random errors, ability to be applied in various scenarios, and to address a variety of error models. In addition, a compact mapping function is attractive in terms of both storage and robustness. We start by introducing the nonparametric statistical approach for conducting off-line calibration. After that, we present the non-parametric statistical percentile method for establishing the confidence interval for a particular mapping function. Furthermore, we propose the first model-based on-line procedure for calibration. The calibration problem is formulated as an instance of nonlinear function minimization and solved using the standard conjugate gradient approach. A number of trade-offs between the effectiveness of calibration and noise level, latency, size of network and the complexity of phenomena are analyzed in a quantitative way. As a demonstration example, we use a system consisting of photovoltaic optical sensors. <s> BIB003 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Calibration Challenges <s> Sensornet systems research is being conducted with various applications and deployment scenarios in mind. In many of these scenarios, the presumption is that the sensornet will be deployed and managed by users who do not have a background in computer science. In this paper we describe the "tiny application sensor kit" (TASK), a system we have designed for use by end-users with minimal sensornet sophistication. We describe the requirements that guided our design, the architecture of the system and results from initial deployments. Based on our experience to date we present preliminary design principles and research challenges that arise in delivering sensornet research to end users. <s> BIB004 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Calibration Challenges <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> BIB005 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Calibration Challenges <s> Abstract Sensors play an important role in guiding building systems to achieve desired operation and efficiency. However, sensors are subject to continuous degradation and failures over time. Although a periodical calibration is needed, it is exceptionally difficult and/or impractical to many sensors with a conventional manual approach. Uncalibrated problematic sensors could significantly compromise the systems' performance and lead to unintended loss of energy efficiency in buildings. We propose a methodology, termed virtual in-situ calibration, to solve this critical issue. It is developed by mathematically extracting the characteristics of essential aspects involved in a calibration, including the environment assessment, benchmark establishment, and uncertainty quantification. A case study of a supply air temperature sensor in rooftop units illustrates the implementation process; the erratic uncertainty is reduced from ± 19.2 °C to ± 0.7 °C after the virtual in-situ calibration. The calibration method can be implemented online to significantly improve the reliability of sensor networks in buildings. <s> BIB006 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Calibration Challenges <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> BIB007
Calibration in sensor networks is challenging because of several reasons BIB001 BIB002 BIB003 BIB004 . First, the sensor system network consists of a large number of devices typically with no calibration interface. Therefore, in-place sensor calibration schemes become impractical, time consuming, and difficult to achieve. Moreover, sensor nodes are exposed to environmental noise and hardware failures, and the mismatch between factory calibration conditions and in-field conditions makes the calibration of sensors challenging. In addition, different sensors require different calibration procedures, and the reference values might not be readily available. So, in general, even when the response function is known, calibration in uncontrolled environments is difficult. An example is an ozone (O 3 ) sensor whose output value depends not only on the O 3 concentrations but also on the environmental conditions -temperature and relative humidity -of the place in which the sensor is deployed. Conducting a multiple linear regression with respect to ground-truth O 3 concentrations in a place with different environmental conditions than those of the place in which the sensor node will be deployed can produce large errors in the predicted O 3 concentrations BIB005 . Yamamoto et al. BIB007 have recently observed a similar behavior in temperature sensors. Temperature sensors calibrated in a place behave differently when placed in another location because of the difference in environmental conditions (such as solar radiation, humidity, wind speed, rainfall, and azimuth) between the two locations. In large-scale networks, two more challenges add to the inherent difficulties of calibrating a sensor: (i) the need to calibrate a massive number of sensors and (ii) the inconveniences of physically accessing the sensors, as they may be deployed in far and harsh, or even hostile, environments. Additionally, the sensors are presumed to stay active for long periods of time after deployment; therefore, they are expected to be checked regularly against standard instruments to ensure the measurements' quality and to allow periodic recalibration necessary because of the loss of accuracy caused by environmental conditions or internal defects. However, calibration is expected to minimize systematic and random errors; increase the accuracy of sensor readings with respect to the reference model; and manage the  Henceforth, throughout the paper, whenever we speak of calibration, we refer to self-calibration in uncontrolled environments in WSNs. We stress that accurate instrumentation can be used in the calibration process in uncontrolled environments. The difference with respect to controlled environments is that in controlled environments, the calibration conditions can be manipulated manually (in a controlled chamber) to match the manufacturer's data-sheet conditions, while in non-controlled environments, there may be instrumentation that provides reference information but depends on conditions that cannot be manipulated. aforementioned constraints, or requirements, in addition to the limited capability of available sensors to provide accurate data at a low cost and without overhead mechanisms. Hence, a good calibration process should consider the following aspects BIB006 : i) time and monetary cost, ii) disruption to normal operation, iii) access to sensors in places difficult to access, and iv) calibration of a large number of sensors in the field. For example, in many large cities, sensors are installed in traffic lights or lamp-posts. Installing these sensors implies stopping vehicle traffic, and access to these sensors is difficult, costly, and time consuming.