Wireless Sensor and Actuator Networks for Lighting Energy ...

Among them, adaptive Wiener filtering approach generally results in the smallest
prediction error with the least effort tuning the parameters.
Fuzzy sensing rate adaptor
The adaptation of sensing rate is based on the simple logic that large prediction
errors indicate that the environment is undergoing changes, and thus the sensing rate
should be high to catch the changes. On the other hand, if the prediction errors are
small, then the changes of the environment are likely to fall into the region where the
predictive model is able to correctly predict. Hence, the sensing rate could be
reasonably lowered without compromising the resolution of the sensory information.
However, it is hard to quantitatively determine whether the prediction error at a
certain single instance is large or small without the context of what the overall
prediction errors look like. Therefore, a simple exponential moving average smoother is
implemented. The smoothed prediction error at time k is generated by smoothing over
all the prediction errors up to time k as shown in (5.15), where is the smoothing
constant, e(k) is the last prediction error and S k and S k-1 are the smoothed statistics at
time k and k-1 respectively.
ê(k + 1) e smoothed
(k) S k
= e(k) + (1 )S k 1
(5.15)
The prediction error at time k+1 is then compared to the previous smoothed prediction
error using (5.16) to determine if the prediction error is large or small.
(k + 1) = e(k + 1) ê(k + 1) (5.16)
A set of fuzzy rules is implemented to adapt the sensing rate:
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