Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Introduction <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> Introduction <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> Introduction <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> Introduction <s> On-line fault detection in sensor networks is of paramount importance due to the convergence of a variety of challenging technological, application, conceptual, and safety related factors. We introduce a taxonomy for classification of faults in sensor networks and the first on-line model-based testing technique. The approach is generic in the sense that it can be applied on an arbitrary system of heterogeneous sensors with an arbitrary type of fault model, while it provides a flexible tradeoff between accuracy and latency. The key idea is to formulate on-line testing as a set of instances of a non-linear function minimization and consequently apply nonparametric statistical methods to identify the sensors that have the highest probability to be faulty. The optimization is conducted using the Powell nonlinear function minimization method. The effectiveness of the approach is evaluated in the presence of random noise using a system of light sensors. <s> BIB004 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Introduction <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> BIB005 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Introduction <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> BIB006 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Introduction <s> A vehicle suspension assembly for tandem drive axles has a suspension system for each axle. Each suspension system incorporates two lower torque beams on each side of the vehicle and a single central upper torque rod. A central hanger is mounted between the two axles. The lower torque beams are pivotally connected between the hanger and an axle seat. A straddle bracket connects the axle to the axle seat in a hinged fashion. The upper central torque rod is connected between the upper side of the axle housing and the chassis. The lower torque beams act as a parallelogram to stabilize the axle seat. They also act together with a portion of the axle seat as a composite lower torque rod cooperating with the upper torque rod to stabilize the axle. <s> BIB007 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Introduction <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> BIB008 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Introduction <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> BIB009 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Introduction <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> BIB010 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Introduction <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> BIB011 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Introduction <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> BIB012 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Introduction <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> BIB013 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Introduction <s> Wireless Sensor Networks (WSNs) have become a leading solution in many important applications such as intrusion detection, target tracking, industrial automation, smart building and so on. Typically, a WSN consists of a large number of small, low-cost sensor nodes that are distributed in the target area for collecting data of interest. For a WSN to provide high throughput in an energy-efficient way, designing an efficient Medium Access Control (MAC) protocol is of paramount importance because the MAC layer coordinates nodes' access to the shared wireless medium. To show the evolution of WSN MAC protocols, this article surveys the latest progresses in WSN MAC protocol designs over the period 2002-2011. In the early development stages, designers were mostly concerned with energy efficiency because sensor nodes are usually limited in power supply. Recently, new protocols are being developed to provide multi-task support and efficient delivery of bursty traffic. Therefore, research attention has turned back to throughput and delay. This article details the evolution of WSN MAC protocols in four categories: asynchronous, synchronous, frame-slotted, and multichannel. These designs are evaluated in terms of energy efficiency, data delivery performance, and overhead needed to maintain a protocol's mechanisms. With extensive analysis of the protocols many future directions are stated at the end of this survey. The performance of different classes of protocols could be substantially improved in future designs by taking into consideration the recent advances in technologies and application demands. <s> BIB014 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Introduction <s> This paper gives a state-of-art of wireless sensor network (WSN) technologies and solutions applied to precision agriculture. The paper first considers applications and existing experiences that show how WSN technologies have been introduced in to agricultural applications. Then, a survey in hardware and software solutions is related with special emphasis on technological aspects. Finally, the paper shows how five networking and technological solutions may impact the next generation of sensors. These are: (1) scalar wireless sensor networks; (2) wireless multimedia sensor networks; (3) mobility of nodes; (4) tag-based systems; and (5) smart-phone applications. <s> BIB015 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Introduction <s> Emily G. Snyder,*,† Timothy H. Watkins,† Paul A. Solomon,‡ Eben D. Thoma,† Ronald W. Williams,† Gayle S. W. Hagler,† David Shelow, David A. Hindin, Vasu J. Kilaru,† and Peter W. Preuss †U.S. Environmental Protection Agency, Office of Research and Development, Research Triangle Park, North Carolina, 27711, United States ‡U.S. Environmental Protection Agency, Office of Research and Development, Las Vegas, Nevada, 89119, United States U.S. Environmental Protection Agency, Office of Air and Radiation, Research Triangle Park, North Carolina, 27711, United States U.S. Environmental Protection Agency, Office of Enforcement and Compliance Assurance, Washington, District of Columbia, 20460, United States U.S. Environmental Protection Agency, Office of Research and Development, Washington, District of Columbia, 20460, United States <s> BIB016 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Introduction <s> This paper presents a novel low-cost instrument that uses a sensor based on conductivity changes of heated tungstic oxide, which is capable of accurately measuring ambient concentrations of ozone. A combination of temperature steps and air flow-rate steps is used to continually reset and re-zero the sensor. A two-stage calibration procedure is presented, in which a nonlinear transformation converts sensor resistance to a signal linear in ozone concentration, then a linear correlation is used to align the calibration with a reference instrument. The required calibration functions specific for the sensor, and control system for air flow rate and sensor temperature, are housed with the sensor in a compact, simple-to-exchange assembly. The instrument can be operated on solar power and uses cell phone technology to enable monitoring in remote locations. Data from field trials are presented here to demonstrate that both the accuracy and the stability of the instrument over periods of months are within a few parts-per-billion by volume. We show that common failure modes can be detected through measurement of signals available from the instrument. The combination of long-term stability, self-diagnosis, and simple, inexpensive repair means that the cost of operation and calibration of the instruments is significantly reduced in comparison with traditional reference instrumentation. These instruments enable the economical construction and operation of ozone monitoring networks of accuracy, time resolution and spatial density sufficient to resolve the local gradients that are characteristic of urban air pollution. <s> BIB017 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Introduction <s> Wireless sensor networks are typically composed of low-cost sensors that are deeply integrated in physical environments. As a result, the sensing performance of a wireless sensor network is inevitably undermined by biases in imperfect sensor hardware and the noises in data measurements. Although a variety of calibration methods have been proposed to address these issues, they often adopt the device-level approach that becomes intractable for moderate-to large-scale networks. In this article, we propose a two-tier system-level calibration approach for a class of sensor networks that employ data fusion to improve the sensing performance. In the first tier of our calibration approach, each sensor learns its local sensing model from noisy measurements using an online algorithm and only transmits a few model parameters. In the second tier, sensors' local sensing models are then calibrated to a common system sensing model. Our approach fairly distributes computation overhead among sensors and significantly reduces the communication overhead of calibration compared with the device-level approach. Based on this approach, we develop an optimal model calibration scheme that maximizes the target detection probability of a sensor network under bounded false alarm rate. Our approach is evaluated by both experiments on a testbed of TelosB motes and extensive simulations based on synthetic datasets as well as data traces collected in a real vehicle detection experiment. The results demonstrate that our system-level calibration approach can significantly boost the detection performance of sensor networks in scenarios with low signal-to-noise ratios. <s> BIB018 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Introduction <s> A novel distributed algorithm for blind macro-calibration of large sensor networks is introduced. The algorithm is in the form of a system of gradient-type recursions for estimating parameters of local sensor calibration functions. The method does not require any fusion center. The convergence analysis is based on diagonal dominance of the dynamical systems with block matrices. It is proved that the asymptotic consensus is achieved for all the equivalent sensor gains and offsets (in the mean square sense and with probability one) in lossy sensor networks with possible communication outages and additive communication noise. An illustrative simulation example is provided. <s> BIB019 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Introduction <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> BIB020 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Introduction <s> The widespread diffusion of sensors, mobile devices, social media and open data are reconfiguring the way data underpinning policy and science are being produced and consumed. This in turn is creating both opportunities and challenges for policy-making and science. There can be major benefits from the deployment of the IoT in smart cities and environmental monitoring, but to realize such benefits, and reduce potential risks, there is an urgent need to address current limitations, including the interoperability of sensors, data quality, security of access and new methods for spatio-temporal analysis. Within this context, the manuscript provides an overview of the AirSensEUR project, which establishes an affordable open software/hardware multi-sensor platform, which is nonetheless able to monitor air pollution at low concentration levels. AirSensEUR is described from the perspective of interoperable data management with emphasis on possible use case scenarios, where reliable and timely air quality data would be essential. <s> BIB021
In recent years there has been a growing interest in the use of networks of monitoring nodes based on low-cost sensor technology. The monitored parameters measured by sensors include air quality parameters (e.g., concentration of gases such as NO BIB014 , NO, CO, CO 2 , and O 3 , or concentration of particulate matter, such as PM2 or PM10), environmental parameters (e.g., temperature, humidity, radioactivity), noise, wind, tracking of objects or persons, presence of animals, and perimetric security. Many of these sensors are sold by manufacturers without individual calibration parameters except for some generic calibration values in the data-sheet description of sensor devices. In some situations, the sensing devices were individually calibrated by the manufacturer, usually under controlled conditions. In any case, calibration, either during the pre-installation phase or during the monitoring network operation, is necessary in order to achieve the data quality requirements. Sensor calibration in the deployment of large-scale cyberphysical IoT systems and ad hoc sensor networks is mandatory due to the inherent process of device imperfection and noise in the massive data collected. In recent years there has been a growing interest in applying all the theoretical knowledge acquired in wireless sensor networks (WSN) to commercial or research deployments. Much of this research has focused on challenges related to communication protocols suited to WSN and the management of their energy consumption BIB007 BIB014  BIB015 . From the very beginning, some works paid attention to the quality of the sensing data, BIB001 BIB002 BIB003 BIB009 but most of them focused on simple data applications, such as temperature or relative humidity, since these were the types of data measured by the first available sensors. Others BIB012 BIB019 used synthetic data generated with probability functions, such as Gaussian distribution functions, for validating calibration models. However, the concern for the quality of the data has increased with the growth of real wireless sensor deployments. For example, Buonadonna et al. BIB005 mentioned the disappointment when the values of light sensors (calibrated according to the manufacturer's specification) in a deployment were compared with the value from a calibrated sensor in an environmentally controlled chamber. Ramanathan et al. BIB006 described the difficulties in obtaining realistic readings of ion-selective electrode sensors used for monitoring water quality in a real deployment in Bangladesh. More recently, Snyder et al. BIB016 and Williams BIB017 mentioned the lack of knowledge on the performance and long-term reliability of lowcost air pollution sensors. The classical calibration process mainly consists of calibrating the sensor device in a controlled environment, for example, in a laboratory with high-cost instrumentation, where the sensor response is measured under different controlled conditions. When it is not possible to use such a laboratory, or when sensor devices are under operation after deployment, the sensor parameters can be self-calibrated and adjusted in reference to another sensor of the network, whether calibrated with a ground-truth 2 BIB013 BIB020 reference node, calibrated with respect to already calibrated sensor nodes (e.g., distributed calibration), BIB018 or with respect to not-calibrated sensor nodes (e.g., blind calibration) BIB008 . When the sensor response cannot be measured in a controlled environment and the sensor parameters have to be adjusted according to other sensor nodes in the network, the calibration is said to be in an uncontrolled environment. In this case, poor or incomplete calibration can lead to significant errors in sensor measurements. Moreover, even if the sensors are calibrated before deployment, it is not possible to prevent sensor drift after deployment, especially when the lifetime of sensor systems can be as long as years. In some cases, even costly high-accuracy sensors produce faulty data when aging. Therefore, it is necessary to automatically detect drifts and miscalibration in order to correct sensors' measurement after deployment to ensure the trustworthiness of long-term WSNs. While important, ensuring data quality in WSNs presents several challenges BIB021 . The quantity of data is large, but the quality is low, since the data can be subject to many types of faults. In a real sensor network deployment, Buonadonna et al. BIB005 observed that failures can occur in unexpected ways which provide inaccurate data. Koushanfar et al. BIB004 have identified several facets of faulty data and suggested five phases of testing sensor-based systems. Ni et al. BIB010 defined data faults to be data reported by a sensor that are inconsistent with the phenomenon of interest. Measurement faults can be produced by systematic and random errors. Each measurement has unpredictable random errors due to environmental noise, the precision of the equipment, or sensor manufacturing defects. This survey provides insight into existing calibration approaches, models, and methods that have been proposed for an efficient calibration process when expensive equipment for controlled calibration is not available, or when the sensor nodes are already deployed in the field. Calibration is a technically challenging task mainly due to the existence of random noise and the absence of suitable general models. Calibration techniques and models depend on the physical space of the signal measured, on the availability of ground-truth data, and on the capabilities of the sensor nodes. Although there is a rich variety of papers related to calibration models and algorithms for different sensors, we have not found any survey that has collected the definitions, approaches, and models used for calibrating sensors in WSNs. The closest research area in which part of this knowledge is collected is sensor faults research, BIB010 BIB011 where calibration is considered one of the many causes of wireless sensor node faults. Our main contributions are the following: • We first define calibration in uncontrolled environments, BIB014 The ground-truth value is the reference value obtained from a perfectly calibrated sensor. In machine learning, the ground-truth value refers to the accuracy of the training set's classification for supervised learning techniques. stressing that an uncontrolled environment involves calibration in the field and not necessarily in the presence of accurate instrumentation. Moreover, we state the main challenges in calibrating low-cost sensors in uncontrolled environments in WSNs. • We present calibration models and the difficulty in choosing a specific calibration model in the calibration process. • We state the quality of information (QoI) metric generally used to evaluate the performance of a calibration process. • We define calibration approaches and attributes in order to calibrate a sensor. These include specifying the conditions that the calibration process follows, specifically, what the measurement area is, how many sensors are involved in the calibration process, what the knowledge of the physical phenomenon is, what the position of the calibration sensor is with respect to reference data when reference data exist, how many times the calibration process is performed, how the information is processed with respect to the normal application operations, and where the calibration is performed. • Finally, we present guidelines on how to calibrate a sensor network. The aim of this survey, then, is to provide a systematically characterized taxonomy of approaches, attributes, and models for the calibration of sensors in WSN in uncontrolled environments. Sections 2, 3, and 4 present the basics of sensor calibration, calibration models, and different validation metrics used to evaluate the accuracy of a calibration model. Section 5 focuses on the different attributes and approaches of a calibration process. Section 6 gives guidelines in how to calibrate a low-cost sensor network. Section 7 discusses the existing research challenges in the field of calibration in uncontrolled environments. Finally, Section 8 summarizes and concludes the paper.