Comparison of Accuracy Estimation Approaches for Sensor Networks

by Wen, Hongkai; Xiao, Zhuoling; Symington, Andrew C; Markham, Andrew; Trigoni, Niki
Abstract:
With sensor technology gaining maturity and becoming ubiquitous, we are experiencing an unprecedented wealth of sensor data. In most sensing applications, users receive sensor measurements, which are prone to error. As a result, they are often annotated with some measure of uncertainty, such as the distribution variance or a confidence interval, and will be hereafter referred to as probabilistic measurements. The question that we address in this paper is how to estimate the accuracy of these probabilistic measurements, that is, how far they lie from the ground truth of the measured attribute. Existing studies on estimating the accuracy of probabilistic measurements in real sensing applications are limited in three ways. First, they tend to be application-specific. Second, they typically employ learning techniques to estimate the parameters of sensor noise models, and ignore alternative approaches that rely on simple state estimation without learning. Third, they do not explore whether exploiting the dynamics of the monitored state can yield significant benefits in terms of accuracy estimation. In this paper, we address the above limitations as follows: We define the problem of accuracy estimation in a general way that applies to a wide spectrum of application scenarios. We then propose a taxonomy of accuracy estimation techniques, which include both state estimation and parameter learning. These techniques are further subdivided into static and dynamic, depending on whether they exploit knowledge of system dynamics. All different approaches in the taxonomy are then applied and compared with each other in the context of two real sensing applications. We discuss how they trade accuracy for computation cost, and how this tradeoff largely depends on the user’s knowledge of the application scenario.
Reference:
Comparison of Accuracy Estimation Approaches for Sensor Networks (Wen, Hongkai; Xiao, Zhuoling; Symington, Andrew C; Markham, Andrew; Trigoni, Niki), In Distributed Computing in Sensor Systems (DCOSS), 2013 IEEE International Conference on, 2013.
Bibtex Entry:
@InProceedings{wen2013comparison,
  Title                    = {Comparison of Accuracy Estimation Approaches for Sensor Networks},
  Author                   = {Wen, Hongkai and Xiao, Zhuoling and Symington, Andrew C and Markham, Andrew and Trigoni, Niki},
  Booktitle                = {Distributed Computing in Sensor Systems (DCOSS), 2013 IEEE International Conference on},
  Year                     = {2013},
  Organization             = {IEEE},
  Pages                    = {28--35},

  Abstract                 = {With sensor technology gaining maturity and becoming ubiquitous, we are experiencing an unprecedented wealth of sensor data. In most sensing applications, users receive sensor measurements, which are prone to error. As a result, they are often annotated with some measure of uncertainty, such as the distribution variance or a confidence interval, and will be hereafter referred to as probabilistic measurements. The question that we address in this paper is how to estimate the accuracy of these probabilistic measurements, that is, how far they lie from the ground truth of the measured attribute. Existing studies on estimating the accuracy of probabilistic measurements in real sensing applications are limited in three ways. First, they tend to be application-specific. Second, they typically employ learning techniques to estimate the parameters of sensor noise models, and ignore alternative approaches that rely on simple state estimation without learning. Third, they do not explore whether exploiting the dynamics of the monitored state can yield significant benefits in terms of accuracy estimation. In this paper, we address the above limitations as follows: We define the problem of accuracy estimation in a general way that applies to a wide spectrum of application scenarios. We then propose a taxonomy of accuracy estimation techniques, which include both state estimation and parameter learning. These techniques are further subdivided into static and dynamic, depending on whether they exploit knowledge of system dynamics. All different approaches in the taxonomy are then applied and compared with each other in the context of two real sensing applications. We discuss how they trade accuracy for computation cost, and how this tradeoff largely depends on the user's knowledge of the application scenario.},
  Url                      = {http://dx.doi.org/10.1109/DCOSS.2013.56}
}