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An energy-efficient data processing scheme for wireless sensor networks

Efficiently utilizing the power available to increase service life of sensors is one of the key challenges in the design and operation of a wireless sensor network for system health monitoring. This paper addresses energy-efficient computation on the sensor node level by presenting an in-network data processing scheme. The scheme is motivated by the concept of Dynamic Voltage Scheduling (DVS), which minimizes energy consumption through dynamically adjusting the voltage supply and clock frequency of the individual sensors. Unlike the traditional approach where a uniform data processing speed is employed for all the sensors, the proposed scheme adjusts the speed of each sensor individually to utilize the processor idle time for prolonged computation latency. The advantage of such a scheme becomes increasingly evident when a large amount of raw data needs to be processed locally at each sensor to reduce the amount of overall data communication. An application model using vibration-base sensory nodes for machine health monitoring was constructed to test the new data processing scheme. Simulation has shown that energy saving of up to 29% could be achieved.