Wireless Sensor and Actuator Networks for Lighting Energy ...

utilizing a variant of a Kalman filter [73]. The performance of the methodology was
verified by monitoring the temperature in a gas turbine power plant. Durrant-Whyte et
al. derived a decentralized form of Kalman filter for multiple sensors to perform sensor
fusion locally with limited communication among the sensors [74]. The efficiency of
this decentralized sensor fusion architecture largely depends on the communication
topology. Alag et al. used a modified Kalman filtering approach for real time sensor
validation and PADF method for sensor fusion on the longitudinal control of automated
vehicles [75]. Data from the sonar, radar and optical sensors mounted on each car were
fused for accurate longitudinal distance between the cars. Bernardin et al. developed a
human hand grasp recognition technique by fusing a grasp-type measure from finger
joint angles and contact-point information from tactile sensors with a hidden Markov
model [76]. The HMM was trained using expectation maximization (EM) algorithm and
was able to attain 90% recognition accuracy with very little training data for twelve
grasp classes. Zhou et al. proposed a maximum likelihood approach for fusion of
multiple sensor data with correlated noise and unknown scaling factor mapping the
sensor readings to physical representations [77]. The approach was based on a
parametric modeling of the noise covariance and formulated in the transformed noise
subspace. Wu et al. built a sensor fusion architecture based on Dempster-Shafer theory
for context-aware computing applications by fusing together the evidence from both
video and audio sensors [78]. Goebel et al. developed a fuzzy logic based architecture
for real time sensor validation and fusion called FUSVAF. The performance of this
algorithm was demonstrated on vehicle-following tasks on automated highways [79],
and on the control of gas turbine power plants [80].
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