Wireless Sensor and Actuator Networks for Lighting Energy ...

The daylighting system application in this research is a good example for the
dilemma of choosing a fixed optimal sensing rate. The available daylight in a room may
stay relatively steady for a long period of time during the day, but change rapidly at
dawn and dusk or when there are clouds passing by. Therefore, tuning the sensing rate
is a nontrivial problem. Since it is difficult to find a universally optimal sensing rate for
all situations, it is reasonable to ask, “Is there an efficient method by which to
dynamically adapt the sensing rate so that it is high when the system is at transient state
and low when at steady state?”
Marbini et al. proposed a generic mechanism for adaptive sampling as replicated
in Figure 5-1 [93]. However, no conclusive suggestion was made on how each critical
element – “model” and “control loop” blocks – should be implemented. Jain et al.
implemented the idea by applying a Kalman filter in the “model” block and proposed a
two-fold module – source side module and central server module – for the “control
loop” block in Figure 5-1 [94]. The source side module allows the sensor node to
dynamically adjust the sensing rate within a predetermined range according to the
prediction error of the model using a customized function. If the new sensing rate
determined by the source side module is beyond the predetermined range, the central
server module manages the requests for a new sensing rate in a centralized fashion. The
proposed adaptive strategy in [94] is suitable for query-based sensor network
configuration, but is probably not ideal for real-time feedback control applications.
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