Robust and Efficient Aggregate Query Processing in Wireless Sensor Networks
Wireless sensor networks have been widely used in many applications, such as soil temperature monitoring for plant growth and abnormal event detection of industrial parameters. Among these applications, aggregate queries, such as SUM, COUNT, AVERAGE, MIN and MAX are often used to collect statistical data. Due to the low quality sensing devices or random environmental disturbances, sensor data are often noisy. Hence, the idea of moving average, which computes the average over consecutive aggregate data, is introduced to offset the effect. The high link loss rate, however, makes the result after averaging still inaccurate. To address this issue, we propose a PCM-based data transmission scheme to make up the possibly lost data. Specifically, we focus on obtaining robust aggregate results under high link loss rate. In order to reduce the communication traffic that dominates the energy consumption of the sensor network, we also design an intelligent path selection algorithm for our scheme. Our extensive simulation results have shown that this technique outperforms its counterparts under various sensor network conditions.
aggregate query wireless sensor networks moving average