Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Gaussian Process Regression <s> We propose a distributed solution for a canonical task in wireless sensor networks - the binary detection of interesting environmental events. We explicitly take into account the possibility of sensor measurement faults and develop a distributed Bayesian algorithm for detecting and correcting such faults. Theoretical analysis and simulation results show that 85-95 percent of faults can be corrected using this algorithm, even when as many as 10 percent of the nodes are faulty. <s> BIB001 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Gaussian Process Regression <s> Recently, large scale sensor networks are used in various service applications. In case of measuring the distribution of room temperature, the regular calibration of the sensors is necessary for obtaining reliable information. However it is not an easy task in the case of a large scale sensor network. The calibration in such a system is time consuming and costly. To solve this problem, we propose a new method which is based on the two concepts of regression analysis and cross validation. The new method is explained and the performance of the proposed method is verified by an experiment and a simulation. <s> BIB002 </s> Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey <s> Gaussian Process Regression <s> Internet-of-Things (IoT) is a concept referring to interconnected people and objects and smart city is one of the many applications of IoT. Wireless Sensor Network (WSN) is a specific technology that helps to create “Smart Cities”. It aims at creating a distributed network of intelligent sensor nodes which can measure various parameters for efficient management of the city. The data thus collected through a range of sensors is processed and is delivered wirelessly in real-time to the citizens or the appropriate authorities. Since the application framework for smart city application is huge, it would require a large number of different types of sensors for its implementation and the project could be viable only if we use low resolution, low precision but inexpensive sensors. The sensors in sensor network can suffer from random or systematic errors. Most common problem with inexpensive sensors used in WSNs for smart city applications is of drift and bias. They can be calibrated at the time of deployment, but they develop drift, which is the slow change in the reading of sensor from actual value as time progresses. In this paper we have proposed a framework to automatically detect and correct the drift of the sensor nodes to keep the WSN usable. Kriging based interpolation of the sensor readings of neighboring sensors is used to predict actual value at the sensor node and the measured drift is then kalman filtered to get correct drift estimates. We have demonstrated the results of this algorithm on real sensor data obtained from Intel Research Berkeley Laboratory deployment and shown that our system is able to detect and correct smooth drift and bias generated in the sensors. We have also shown that our system is robust with respect to the number of sensor nodes drifting and significantly outperforms the traditional averaging based interpolation methods. <s> BIB003
Fujino et al. BIB002  proposed to calibrate a network of N thermal sensors using Gaussian processes. All the sensors measure the same phenomenon, and then they are neighbors from the point of view of seeing similar correlated data, being the calibration form of type collocated or single-hop. The authors considered that a set of sensors acts as ground-truth nodes and proposed an on-line version of the architecture, instead of the classical off-line. The network can be represented 2 ), where θ consists of all sensors' parameters [a 1 ,..., a N , b 1 ,...,b N ] . The likelihood can be obtained from p(y i | θ), and the solution of the problem is found using the expectation-maximization (EM) algorithm. Gaussian process regression, also known as kriging, is a technique used in geostatistical models to predict the value of a function at a given point by computing a weighted average of the known values of the function in the neighborhood of the point. Kumar et al. BIB003 used simple kriging to predict the humidity value of a sensor from neighboring nodes, and the measured drift was then Kalman filtered to get the correct drift estimates. The method was tested in a wireless network of 54 real sensors, improving RMS error with respect to averaging-based prediction models BIB001 . Although BIB003 used kriging to estimate the value of neighboring sensors and then correct the drift, kriging can also be used as a model-based calibration. Given a set of reference calibrated nodes, the value of interest can be obtained by interpolation in a given location using kriging. This value can then be used as the ground-truth value for calibrating a sensor located in the same place. The accumulated errors now come from the kriging process and from the calibration model used.