Wireless Sensor and Actuator Networks for Lighting Energy ...

sensed data points as cross signs and the true illuminance with a dashed line. In plots (b)
to (e), the red dashed line displays the true illuminance and the black solid line shows
how the fusion algorithms follow the true illuminance. Plot (b) and (c) are the mote-
FVF algorithms with the two majority voting schemes, median value and the Gaussian
correlation, respectively. Both show good performances, even when facing sensor
failures. Plot (d) shows that without involving validation and prediction, a majority
voting algorithm is much more sensitive to sensor failures than the mote-FVF. Plot (e)
is the fusion algorithm without majority voting mechanism in the validation process,
which is clearly not capable of dealing with large discontinuous changes by fixing the
center of the validation curve at the predicted value.
The mote-FVF algorithm has shown its ability to extract pertinent readings from
multiple sensors in real time. Meanwhile, good sensor information also relies on a
proper sensing rate to capture the changes of the monitored environment. Sensing and
radio transmissions, especially the later, contribute to the largest portion of energy
consumption of the battery-powered wireless mote sensors. Therefore, it is unrealistic to
always implement a high sensing rate for good resolution of sensor information without
jeopardizing the sensors’ life span. The next chapter proposes an adaptive sensing
strategy to optimize the timing for sensing and radio transmission to ensure good
sensing resolution and long life span of the mote sensors.
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