Wireless Sensor and Actuator Networks for Lighting Energy ...

5.3 Simulation and Experiment Results
Each of the predictive models was integrated with the fuzzy sensing rate adaptor
and simulated using the same set of daylight data as that used in Figure 5-5. The
daylight data were sampled at a fixed rate of ten seconds/sample using one mote
photosensor. A total of 1851 consecutive data points were collected.
The fuzzy parameters were tuned as follows: m was tuned to 8 and max was set
to 20; SR min was arbitrarily set to 600 seconds/sample (10 minutes/sample); SR max was
set to the highest possible resolution of the daylight data – 10 seconds/sample, the rate
at which the daylight data was sampled; m SR was tuned to 250 seconds/sample; m damp
was tuned to 0.3 for the damped version of the algorithm.
Figure 5-9, Figure 5-10, and Figure 5-11 show the performance of the sensing
rate adaptation mechanism with Kalman filtering, adaptive Wiener filtering, and the
double exponential smoothing model respectively before applying any damping. The
solid blue line in (a) of each figure is the daylight data sampled uniformly at 10
seconds/sample, and the dashed green line is the daylight information reconstructed
from the adaptively sensed data. Compared to the original 1851 samples, only 164, 174,
and 262 points were sampled with respect to each predictive model during the entire
sensing task. Plot (b) of each figure details how the sensing rates were adapted over
time. All of the predictive models resulted in reasonably good abilities of preserving the
original daylight information.
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