Wireless Sensor and Actuator Networks for Lighting Energy ...

sensors. The research in [14] presents an example of extending the mote-FVF algorithm
to applications on monitoring the structure health of space vehicles.
10.1.3 Autonomous Sensing with Adaptive Rate
The sensing rate adaptation algorithm employs prediction models to forecast the
next incoming sensor reading based on past readings and utilizes fuzzy logic to adapt
the sensing rate to the change of the environment according to the prediction error. The
algorithm serves as a useful mechanism when monitoring stimulus with changing
dynamics, especially if sensing is an expensive action. In the lighting application with
energy-constrained photosensors operating at an ultra-low duty cycle, bringing up the
processor and data acquisition unit for sensing can sacrifice the life span of a sensor
node. More importantly, since real time sensory feedback is critical for lighting control
purposes, frequent power-hungry wireless communication is inevitable. The adaptive
sensing strategy optimizes the timing for sensing and wireless communication, and
ensures the resolution of the sensed information for good control performance.
Three different prediction models were evaluated for the algorithm, and all
resulted in adequate prediction performance with minimal parameter tuning. The fuzzy
rules efficiently adapted the sensing rate in response to the prediction errors to catch
change in the stimulus. A test of daylight monitoring on a typical sunny day reveals that
only less than 20% of the sensing action, and hence wireless communication, was
required by adaptive sensing to catch all the daylight changing information compared to
that obtained with a fixed sensing rate at ten seconds per sample. The sensing rate was
dynamically shifted between ten seconds and over five minutes per sample.
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