In Internet of Things (IoT) networks, Unmanned Aerial Vehicles (UAVs) play a critical role as mobile nodes that can be deployed to carry out data collection and computation. In this respect, this paper considers an operator that deploys UAVs to satisfy requests from IoT applications that require Virtual Network Functions (VNFs) that may communicate with one another to be executed at different geographical locations. To this end, this paper formulates a novel Mixed Integer Linear Program (MILP) to determine the optimal assignments of UAVs and VNFs over a planning horizon that maximizes a given performance metric, e.g., revenue. It also outlines a heuristic method named MPopLoc that chooses requests according to popular requested locations and traveling cost of UAVs. The results show that MPopLoc achieved approximately 95.14% of the optimal result.
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), respectiv
For the first time CAE exhibited a digital F-16 cockpit integrated with the Simulators Common Architecture Requirements and Standards (SCARS) marking a major step forward in training equipment virtualisation.