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Due to its compact and distributed nature, loopy belief propagation has been proved to be theoretically appropriate as a basis to systematically deal with uncertainties with incomplete or corrupted observations in the broad applications of wireless sensor networks. However, the transmissions of massive belief messages of loopy belief propagation could become a serious concern from the energy consumption perspective since sensor nodes are energy limited, and in many real-world wireless sensor network applications, the replacement of sensor node batteries is very difficult if not impossible. In this paper, we present a novel wavelet-based loopy belief propagation, which can effectively reduce 50% of belief messages communicated among sensor nodes with only minimal degradation of the inference performance. Our preliminary results using real-world soil moisture data from an environmental monitoring application demonstrate the great promise of the proposed approach.