The CEO problem of estimating a single binary data source using multiple observations is considered. A closed form equation for maximum achievable information capacity of the system is derived for different system parameters including number of sensors, observation accuracy and channel quality. A new criterion for sensor clustering is provided based on the minimum number of sensors required to achieve arbitrarily low estimation error using different coding rates. A distributed joint source-channel coding (D-JSCC) scheme is proposed as an implementation example. The proposed coding scheme is based on the parallel concatenated convolutional codes (PCCC) that achieves high BER performance utilizing the number of sensors determined by the derived criterion.