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Authors
Advisor(s)
Abstract(s)
Sensor/actuator networks promised to extend automated monitoring and control into industrial processes. Avionic
system is one of the prominent technologies that can highly gain from dense sensor/actuator deployments. An
aircraft with smart sensing skin would fulfill the vision of affordability and environmental friendliness properties by
reducing the fuel consumption. Achieving these properties is possible by providing an approximate representation of
the air flow across the body of the aircraft and suppressing the detected aerodynamic drags. To the best of our
knowledge, getting an accurate representation of the physical entity is one of the most significant challenges that
still exists with dense sensor/actuator network. This paper offers an efficient way to acquire sensor readings from
very large sensor/actuator network that are located in a small area (dense network). It presents LIA algorithm, a
Linear Interpolation Algorithm that provides two important contributions. First, it demonstrates the effectiveness of
employing a transformation matrix to mimic the environmental behavior. Second, it renders a smart solution for
updating the previously defined matrix through a procedure called learning phase. Simulation results reveal that the
average relative error in LIA algorithm can be reduced by as much as 60% by exploiting transformation matrix.