Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Calibration Faults <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> BIB001 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Calibration Faults <s> Rapidly deployable sensor networks are portable, reusable, and can take advantage of a human user in the field attending to the deployment. Unfortunately, even small disruptions or problems in collected data must be addressed quickly, as the overall quantity of data gathered is small relative to longterm deployments. In this paper we describe a procedure for calibration and a system for online fault remediation. Care in the calibration process for ion selective electrodes used for water quality assists interpretation of the data. Scientists will have more confidence in the data obtained from a rapid deployment if in-field users can detect and compensate for problems as they occur. We have designed and implemented a tool for use in the field to detect potential faults and provide actions to remedy or validate the faulty data. In January of 2006 we deployed 48 sensors over a period of 12 days in Bangladesh in order to aid in validating a hypothesis on the mass presence of arsenic in the groundwater. Our system is based on the the approximately 25,000 measurements we collected. <s> BIB002 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Calibration Faults <s> This paper considers the problem of blindly calibrating sensor response using routine sensor network measurements. We show that as long as the sensors slightly oversample the signals of interest, then unknown sensor gains can be perfectly recovered. Remarkably, neither a controlled stimulus nor a dense deployment is required. We also characterize necessary and sufficient conditions for the identification of unknown sensor offsets. Our results exploit incoherence conditions between the basis for the signals and the canonical or natural basis for the sensor measurements. Practical algorithms for gain and offset identification are proposed based on the singular value decomposition and standard least squares techniques. We investigate the robustness of the proposed algorithms to model mismatch and noise on both simulated data and on data from current sensor network deployments. <s> BIB003 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Calibration Faults <s> Little of the work in sensor network studies related to data quality has presented a detailed study of sensor faults and fault models. We provide a comprehensive look at sensor network data fault types and a unified basis for describing sensor faults backed up by real world deployment examples. We also identify several considerations one must take into account when developing a fault detection or diagnosis system. We suggest a broad framework of important considerations when developing a data fault detection system and discuss some assumptions that can be made in this context. Based upon experience and previous work we define a series of features to consider when modeling sensor data for either fault detection or fault correction. We define three main headings of types of features and explore the effect that each type has on a sensor. Then we list all common faults that we have observed in actual sensor network deployments. We provide an example of how one may use such a taxonomy of faults. <s> BIB004 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Calibration Faults <s> Various sensor network measurement studies have reported instances of transient faults in sensor readings. In this work, we seek to answer a simple question: How often are such faults observed in real deploymentsq We focus on three types of transient faults, caused by faulty sensor readings that appear abnormal. To understand the prevalence of such faults, we first explore and characterize four qualitatively different classes of fault detection methods. Rule-based methods leverage domain knowledge to develop heuristic rules for detecting and identifying faults. Estimation methods predict “normal” sensor behavior by leveraging sensor correlations, flagging anomalous sensor readings as faults. Time-series-analysis-based methods start with an a priori model for sensor readings. A sensor measurement is compared against its predicted value computed using time series forecasting to determine if it is faulty. Learning-based methods infer a model for the “normal” sensor readings using training data, and then statistically detect and identify classes of faults. We find that these four classes of methods sit at different points on the accuracy/robustness spectrum. Rule-based methods can be highly accurate, but their accuracy depends critically on the choice of parameters. Learning methods can be cumbersome to train, but can accurately detect and classify faults. Estimation methods are accurate, but cannot classify faults. Time-series-analysis-based methods are more effective for detecting short duration faults than long duration ones, and incur more false positives than the other methods. We apply these techniques to four real-world sensor datasets and find that the prevalence of faults as well as their type varies with datasets. All four methods are qualitatively consistent in identifying sensor faults, lending credence to our observations. Our work is a first step towards automated online fault detection and classification. <s> BIB005 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Calibration Faults <s> Researchers are exploring the rendezvous concept - bringing sensors close to one another in space or time - as a way to make sense of disparate data collected by individuals. Applications such as participatory atmospheric sensing illustrate the potential of rendezvous to help create powerful mobile applications. <s> BIB006
Sensor error is defined as an unexpected value from the sensor device after its deployment. This type of error comprises precision degradation, reading bias, drift, noise, or sensor failure, which is generally due to miscalibration. The miscalibration error, also known as measurement error, can be defined as the difference between the values indicated by a sensor device and the true values provided by a reference model. Data faults due to miscalibration can manifest in different forms by lowering the accuracy of sensor measurement, its precision, or both. Usually, these errors are referred to as bias, gain, drift, or offset. The offset, gain, and drift are the well-known miscalibration errors that were first identified in BIB001 BIB002 BIB003 and discussed by BIB004 BIB005 as the main three different forms of sensor calibration faults in uncontrolled environments. Offset arises when the measured value Y differs from the true value X by a constant amount β 0 , and it can be determined by measuring the sensed value when the ground-truth value is zero. When offset error is present, the measured values follow similar patterns to those of the expected phenomenon. Gain refers to the rate or the amount of change of the measured value with respect to the change in the underlying ground-truth value. In the specific case of a linear response function, the measured value is expressed by the following equation: Here, the coefficients β 0 and β 1 represent the offset and gain parameters, respectively. The term bias refers to deviations that are systematic, not random, for example, because one can consistently over-or underestimate the measurements by X units. Bias, then, can be employed to express the gain, the offset, or both of them in sensor reading. Drift refers to the change over time of the gain and offset parameters associated with a sensor's original, or factory, calibration formulas, that causes the performance of the sensor to deviate from the real signal of the expected phenomenon. Honicky BIB006 observed that when a sensor is miscalibrated, its gain drift can often be bounded to a certain percentage range. For example, the gain of electrochemical sensors can drift no more than 5% per year, according to the data sheet  . Finally, noise refers to the random errors in sensor measurements that interfere with the calibration process and alter the calibration parameters. In calibration, the noise error is usually expressed as follows: where random noise often follows a probability distribution, for example, ∼ N(0, σ 2 ) normal distribution with zero mean and variance σ 2 , and most calibration approaches exploit such property to recover from random errors.