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The problem of decentralized detection in a large wireless sensor network is considered. An adaptive decentralized detection scheme, group-ordered sequential probability ratio test (GO-SPRT), is proposed. This scheme groups sensors according to the informativeness of their data. Fusion center collects sensor data sequentially, starting from the most informative data and terminates the process when the target performance is reached. Wald's approximations are shown to be applicable even though the problem setting deviates from that of the traditional sequential probability ratio test (SPRT). To analyze the efficiency of GO-SPRT, the asymptotic equivalence between the average sample number of GO-SPRT, which is a function of a multinomial random variable, and a function of a normal random variable, is established. Closed-form approximations for the average sample number are then obtained. Compared with fixed sample size test and traditional SPRT, the proposed scheme achieves significant savings in the cost of data fusion.