System-Level Calibration for Fusion-Based Wireless Sensor Networks

Abstract

Wireless sensor networks are typically composed of lowcost 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 devicelevel approach that becomes intractable for moderate-to large-scale networks. In this paper, 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. 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 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 the scenarios with low signal-to-noise ratios.

An Estimation Algorithm for Missing Data in Wireless Sensor Networks

“…For simple calculation, it is assumed that ΔT can be divisible by Tth and the error process is a second-order moment, that is, expectation and variance functions exist in the event procedure. Sensor networks are commonly used to detect specific errors, so we can assume that the expectation function Expe(t) and the square root of variance function VarSRe(t) can be stored in the sensor memory before deployment or the Expe(t) and VarSRe(t) can be distributed to various sensors through the meeting points (sink node) by message after deployment [17][18][19][20][21][22][23].…”

“…Expe(t) and the square root of variance function VarSRe(t) can be stored in the sensor memory before deployment or the Expe(t) and VarSRe(t) can be distributed to various sensors through the meeting points (sink node) by message after deployment[17][18][19][20][21][22][23].INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS VOL. 6, NO.3, JUNE 2013 …”

A Dynamic Bayesian Nonparametric Model for Blind Calibration of Sensor Networks

“…However, this is a painstaking task, which may be impractical if the sensor network is large, and sensors may be placed in inaccessible locations. Macro calibration has thus been proposed to calibrate an entire sensor network based on observations from all sensors in the network [5], [6]. Furthermore, the calibration is done without first observing the underlying signals that the sensors are observing.…”

An Iterative Method of Sensor Calibration in Participatory Sensing Network

“…Nevertheless, the untrained and uncontrollable participants will not cooperate for sensor calibration. Although several uncooperative calibration methods [4], [5] have been proposed, they need an explicit and particular calibration process, such as controlling the behaviors of the interesting sources.…”