Trajectory optimization for data retrieval applications: it is all about where/who loads the mule

This work analyses the problem of data collection in sensor networks with disconnected clusters of sensors leveraging autonomous vehicles and intra-cluster sensor cooperation. The later allows for the vehicle's trajectory to be planned in terms of visiting each cluster instead of visiting individual sensors with important repercussions on the computational complexity of the problem. This paper proposes a dynamic programming methodology to solve the data collection problem in optimal time assuming that the cost of sensor cooperation is negligible. Our approach is novel in that it considers (i) intra-cluster cooperation, allowing communication to a subset of the nodes in each cluster, (ii) a communication rate to each cluster depends on the location of the vehicle, i.e., the data acquisition rate is state dependent, and (iii) that the amount of information to be collected on each sensor cluster may be either known or unknown in advance. Although the problem is cast in the context of data collection in a set of isolated sensor networks, the developed methodology may be used in other applications of resource acquisition or consumption. Simulation results are provided for data collection scenarios with 3 sensor clusters.