Wireless sensor networks (WSNs) are an important means of collecting data in a variety of situations, such as monitoring large or hazardous areas. The retrieval of WSN data can yield better results with the use of unmanned aerial vehicles (UAVs), for example, concerning the increase in the amount of collected data and decrease in the time between the collection and use of the data. In particular, disaster areas may be left without communication resources and with great residual risk to humans, at which point a WSN can be quickly launched by air to collect relevant data until other measures can be put in place. Some studies present approaches to the use of UAVs for the collection of WSN data, focusing mainly on optimizing the path to be covered by a single UAV and relying on long-range communication that is always available; these studies do not explore the possibility of using several UAVs or the limitations on the range of communication. This work describes DADCA, a distributed scalable approach capable of coordinating groups of UAVs in WSN data collection with restricted communication range and without the use of optimization techniques. The results show that the amount of data collected by DADCA is similar or superior, by up to 1 percent, to path optimization approaches. In the proposed approach, the delay in receiving sensor messages is up to 46 percent shorter than in other approaches, and the required processing onboard UAVs can reach less than 75 percent of those using optimization-based algorithms. The results indicate that the DADCA can match and even surpass other approaches presented, while also adding the advantages of a distributed approach.