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Register a new userTowards Secure Wireless Mesh Networks for UAV Swarm Connectivity: Current Threats, Research, and Opportunities
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Added by
Martin Andreoni Lopez
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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UAVs are increasingly appearing in swarms or formations to leverage cooperative behavior, forming flying ad hoc networks. These UAV-enabled networks can meet several complex mission requirements and are seen as a potential enabler for many of the emerging use-cases in future communication networks. Such networks, however, are characterized by a highly dynamic and mobile environment with no guarantee of a central network infrastructure which can cause both connectivity and security issues. While wireless mesh networks are envisioned as a solution for such scenarios, these networks come with their own challenges and security vulnerabilities. In this paper, we analyze the key security and resilience issues resulting from the application of wireless mesh networks within UAV swarms. Specifically, we highlight the main challenges of applying current mesh technologies within the domain of UAV swarms and expose existing vulnerabilities across the communication stack. Based on this analysis, we present a security-focused architecture for UAV mesh communications. Finally, from the identification of these vulnerabilities, we discuss research opportunities posed by the unique challenges of UAV swarm connectivity.
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Unmanned aerial vehicles (UAVs), or say drones, are envisioned to support extensive applications in next-generation wireless networks in both civil and military fields. Empowering UAVs networks intelligence by artificial intelligence (AI) especially machine learning (ML) techniques is inevitable and appealing to enable the aforementioned applications. To solve the problems of traditional cloud-centric ML for UAV networks such as privacy concern, unacceptable latency, and resource burden, a distributed ML technique, textit(i.e.), federated learning (FL), has been recently proposed to enable multiple UAVs to collaboratively train ML model without letting out raw data. However, almost all existing FL paradigms are still centralized, textit{i.e.}, a central entity is in charge of ML model aggregation and fusion over the whole network, which could result in the issue of a single point of failure and are inappropriate to UAV networks with both unreliable nodes and links. Thus motivated, in this article, we propose a novel architecture called DFL-UN (underline{D}ecentralized underline{F}ederated underline{L}earning for underline{U}AV underline{N}etworks), which enables FL within UAV networks without a central entity. We also conduct a preliminary simulation study to validate the feasibility and effectiveness of the DFL-UN architecture. Finally, we discuss the main challenges and potential research directions in the DFL-UN.
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