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The problem of decentralized changepoint detection in a distributed multisensor setting with binary quantization (BQ) is addressed. Attention is drawn to the case of composite post-change hypotheses when the post-change parameter is unknown. A multichart CUSUM detection procedure with binary quantization, called the M-BQ-CUSUM test, is proposed. The methodology is based on using Mges2 putative values of the parameter as ldquoreferencerdquo points. The data are optimally quantized at these points and then sent to the fusion center for making a final decision by running M BQ-CUSUM statistics in parallel. The M-BQ-CUSUM procedure is shown to be asymptotically optimal at the reference points and rather efficient elsewhere.