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Sensor nodes compensate clock drift with guard times (GT), which results in idle listening. By applying prediction methods nodes can limit drift uncertainty for upcoming frames and shorten GT. However, a common solution based on linear regression requires floating-point arithmetic, i.e. large computation and memory overhead. We present an approach for drift prediction based on moving average, which works well with basic mathematical operations. It achieves similar accuracy to linear regression in indoor environments (the standard deviation of the drift prediction is less than a clock tick for 1-minute period) and even better results on some nodes outdoors. Moreover, it needs only 3 previous drift samples for accurate drift estimations. Our two-week drift experiments revealed that in outdoor scenarios nodes received 99% of frames with GT 8× shorter than the worst case. We exploit the idea of deliberately giving up the reception of approx. 1% of frames in order to use very short GT and to reduce idle listening. After applying our drift prediction approach we shortened GT by 95%. It results in 10% lifetime gain for IEEE 802.15.4.