Motif-based communication network formation for task specific collaboration in complex environments

Abstract

Networks of mobile autonomous vehicles rely heavily on wireless communications as well as sensing devices for distributed path planning and decision making. Designing energy efficient distributed decision making algorithms in these systems is challenging and requires that different task-oriented information becomes available to the corresponding agents in a timely and reliable manner. We develop a systems engineering oriented approach to the design of networks of mobile autonomous systems, in which a cross-layer design methodology determines what structures are to be used to satisfy different task requirements. We identify a three-tier organization of these networks consisting of connectivity, communication, and action graphs and study the interaction between them. It is expected that in each functionality of a network, there are certain topologies that facilitate better achievement of the agents' objectives. Inspired from biological complex networks, we propose a bottom-up approach in network formation, in which small efficient subgraphs (motifs) for different functionalities of the network are determined. The overall network is then formed as a combination of these sub-graphs. We show that the bottom-up approach to network formation is capable of generating efficient topologies for multi-tasking complex environments.