An extension of regression-based automatic calibration method for sensor networks

Self-calibration methods for uncontrolled environments in sensor networks: A reference survey

“…Evaluation metric Data set Application [6] Linear regression Mean error Real (thermocouple) Temperature [7] Splines/optimization Confidence interval Real (photovoltaic) Point-lights [8] Distributed consensus Mean error real Light [11] Linear regression Mean error Real Light, temp., humidity [18] Linear regression Mean & median error Real (thermistor) Temp., humidity [35] Nonlinear/splines Mean squared error Real Light [36] Hidden Markov model Recognition accuracy Real Motion (accelerometer) [41] Support vector regression (SVR) Mean squared error Real Light [43] Bayesian Root mean squared error Synthetic Temperature [44] Maximum likelihood Absolute error Synthetic Temperature [45] Kriging Root mean squared error Real Temp., humidity, light [46,47,48] Gaussian process K-L divergence Real Temperature [49] Linear/nonlinear optimization Mean absolute error Real Vibration (water flow) [50] Distributed consensus Mean squared error Synthetic Temp., humidity, sound [51] PCA + compressive sensing Mean squared error Real Temperature Table 3: Localization, synchronization, and target location applications.…”

“…[35] X X X X X X X X Light [36] X X X X X X X Motion (accelerometer) [41] X X X X X X X Light [43] X X X X X X X Temperature [44] X X X X X X X Temperature [45] X X X X X X X X Temp. Hum., Light [46,47,48] X X X X X X X Temperature [49] X X X X X X X Vibration (Water Flow) [50] X X X X X X X Temp., Hum., Sound [51] X X X X X X X Temperature Attributes used in localization, synchronization and target detection applications [5] X X X X X X X Localization [17] X X X X X X X X X Target Detection [37] X X X X X X X Target Detection [38] X X X X X X X Localization [39,40] X X X X X X X Localization [52] X X X X X X X Localization [53,10] X X X X X X X Synchronization [54] X X X X X X X Synchronization Attributes used in air pollution and water chemistry applications [12] X X X X X X X Water Chemistry [15] X X X X X X X X X O 3 [16,27] X X X X X X X O 3 ,CO,CO 2 ,NO,NO 2 [28] X X X X X X X CO,CH 4 ,C 3 H 8 ,CeO 2 ,NiO [29] X X X X X X X O 3 ,CO [30] X X X X X X X O 3 ,CO,NO 2 [31] X X X X X X X O 3 , NO, NO 2 [32] X X X X X X X X X O 3 , CO, NO 2 [33] X X X X X X X O 3 [55] X X X X X X X Photosynthetically Active Radiation (PAR) [56] X X X X X X X Air pollution CaliBree [8] is a model-based calibration network in which ground-truth calibrated nodes inform non-calibrated nodes, using a beacon protocol, that they will participate in the calibration process. These nodes, then, use a discrete consensus distributed algorithm to calibrate the uncalibrated node by computing the degree of disagreement between the reference nodes and t...…”

“…Evaluation metric Data set Application [6] Linear regression Mean error Real (thermocouple) Temperature [7] Splines/optimization Confidence interval Real (photovoltaic) Point-lights [8] Distributed consensus Mean error real Light [11] Linear regression Mean error Real Light, temp., humidity [18] Linear regression Mean & median error Real (thermistor) Temp., humidity [35] Nonlinear/splines Mean squared error Real Light [36] Hidden Markov model Recognition accuracy Real Motion (accelerometer) [41] Support vector regression (SVR) Mean squared error Real Light [43] Bayesian Root mean squared error Synthetic Temperature [44] Maximum likelihood Absolute error Synthetic Temperature [45] Kriging Root mean squared error Real Temp., humidity, light [46,47,48] Gaussian process K-L divergence Real Temperature [49] Linear/nonlinear optimization Mean absolute error Real Vibration (water flow) [50] Distributed consensus Mean squared error Synthetic Temp., humidity, sound [51] PCA + compressive sensing Mean squared error Real Temperature Least squares Mean error Real (acoustic) Localization [17] Nonlinear (maximum likelihood) Detection probability Synthetic/real Target detection [38] Bayesian (maximum a posteriori) Cramèr-Rao bound Real (acoustic) Localization [39,40] Bayesian (maximum a posteriori) K-L divergence Synthetic Localization [37] Nonlinear (maximum likelihood) Bayes risk Synthetic Target detection [52] Distributed consensus Mean, avg. residual Real (RSSI) Localization [53,10] Distributed macro-calibration Mean squared error Synthetic Synchronization [54] Bayesian (iterative) Error variance Synthetic Synchronization…”