As we are surrounded by an increased number of mobile devices equipped with wireless links and multiple microphones, e.g., smartphones, tablets, laptops and hearing aids, using them collaboratively for acoustic processing is a promising platform for emerging applications. These devices make up an acoustic sensor network comprised of nodes, i.e. distributed devices equipped with microphone arrays, communication unit and processing unit. Algorithms for speaker separation and localization using such a network require a precise knowledge of the nodes' locations and orientations. To aquire this knowledge, a recently introduced approach proposed a combined direction of arrival (DoA) and time difference of arrival (TDoA) target function for off-line calibration with dedicated recordings. This paper proposes an extension of this approach to a novel online method with two new features: First, by employing an evolutionary algorithm on incremental measurements, it is fast enough for real-time application. Second, by using the sparse spike representation computed in a cochlear model for TDoA estimation, the amount of information shared between the nodes by transmission is reduced while the accuracy is increased. The proposed approach is able to calibrate an acoustic senor network online during a meeting in a reverberant conference room.