Strategic Deployment of Swarm of UAVs for Secure IoT Networks
Xavier Alejandro Flores Cabezas, Diana Pamela Moya Osorio

TL;DR
This paper proposes a game-theoretic and convex optimization framework for deploying UAVs as aerial base stations to enhance security in IoT networks, demonstrating superior secrecy performance compared to existing algorithms.
Contribution
It introduces a novel integrated approach combining game theory and convex optimization for UAV deployment, association, and power allocation to improve IoT network security.
Findings
Enhanced secrecy performance over state-of-the-art algorithms
Effective UAV positioning and association strategies
Improved security in IoT networks using UAVs
Abstract
Security provisioning for low-complex and constrained devices in the Internet of Things (IoT) is exacerbating the concerns for the design of future wireless networks. To unveil the full potential of the sixth generation (6G), it is becoming even more evident that security measurements should be considered at all layers of the network. This work aims to contribute in this direction by investigating the employment of unmanned aerial vehicles (UAVs) for providing secure transmissions in ground IoT networks. Toward this purpose, it is considered that a set of UAVs acting as aerial base stations provide secure connectivity between the network and multiple ground nodes. Then, the association of IoT nodes, the 3D positioning of the UAVs and the power allocation of the UAVs are obtained by leveraging game theoretic and convex optimization-based tools with the goal of improving the secrecy of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsUAV Applications and Optimization · Wireless Communication Security Techniques · Advanced Wireless Communication Technologies
MethodsBalanced Selection
