The large number of sensor-rich mobile devices that are commonly available in urban environments bring new venues for the study of the environment and human interactions. Better approaches for the exploitation of the spatial-temporal properties of mobile communities in selfselected social-sensing networks are still needed, in particular, for very large participatory sensing systems where incentives and rewards create a cost for the system. We introduce an entropy-based criterion to evaluate the measurement diversity that can be achieved by a mobile group of sensors and propose a parallel randomised search algorithm to find the best group composition. The problem is non-trivial as the aggregated group entropy does not equal the sum of individual contributions. We discuss applications of the approach to achieve area coverage and anomaly tracking and show that the size of mobile sensor groups can be reduced to minimise costs without compromising the quality of the sensing objective.