Multi-UAVs Network Design Algorithms for Computed Rate Maximization

This paper considers a network design problem using Unmanned Aerial Vehicles (UAVs). It aims to create a network to provide communication and computation service to a set of source-destination ground node pairs. The main performance metric is the minimum amount of computed data among a set of source-destination pairs. To optimize this metric, we outline two mixed Integer Linear Programs (MILPs), namely S-MILP and NS-MILP, which are designed respectively for splittable and non-splittable traffic flow models. They jointly optimize the placement of UAVs, assignment of Virtualized Network Functions (VNFs), and routing of unprocessed and processed flow. Further, NS-MILP optimizes the path selection of each source-destination pair. A key challenge is that these MILPs require an exhaustive collection of network topologies. To this end, this paper outlines two heuristic algorithms, called Resource-Aware Location Selection (RALS) and Resource-Aware Path and Location Selection (RAPLS), respectively for each traffic flow model. The simulation results show that RALS and RAPLS achieve on average 83% and 80% of the amount of computed flow of S-MILP and NS-MILP, respectively. Lastly, RALS and RAPLS require 45% and 53% less computation time as compared to S-MILP and NS-MILP, respectively.