Authentication and Handover Challenges and Methods for Drone Swarms

Aydın, Yücel; Kurt, Gunes Karabulur; Ozdemır, Enver; Yanıkömeroğlu, Halim

doi:10.1109/jrfid.2022.3158392

Cited by 18 publications

(8 citation statements)

References 11 publications

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“…Regarding connectivity, a UAV must communicate its position anytime so that its route can be monitored by the ground, and possibly modified by providing new tasks while receiving real-time updates from the UAV [8]. Connectivity is indeed the way to extend the drone operation with the same level of dependability guaranteed by the VLoS operations.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Layer Framework for BVLoS Drone Operation: A Preliminary Study

Sorbelli¹,

Chatterjee²,

Coró³

et al. 2023

Preprint

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Drones have become increasingly popular in a variety of fields, including agriculture, emergency response, and package delivery. However, most drone operations are currently limited to within Visual Line of Sight (VLoS) due to safety concerns. Flying drones Beyond Visual Line of Sight (BVLoS) presents new challenges and opportunities, but also requires new technologies and regulatory frameworks, not yet implemented, to ensure that the drone is constantly under the control of a remote operator. In this preliminary study, we assume to remotely control the drone using the available ground cellular network infrastructure. We propose to plan BVLoS drone operations using a novel multi-layer framework that includes many layers of constraints that closely resemble realworld scenarios and challenges. These layers include information such as the potential ground risk in the event of a drone failure, the available ground cellular network infrastructure, and the presence of ground obstacles. From the multi-layer framework, a graph is constructed whose edges are weighted with a dependability score that takes into account the information of the multi-layer framework. Then, the planning of BVLoS drone missions is equivalent to solving the Maximum Path Dependability Problem on the constructed graph, which turns out to be solvable by applying Dijkstra's algorithm.

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Section: Introductionmentioning

confidence: 99%

A Novel Multi-Layer Framework for BVLoS Drone Operation: A Preliminary Study

Sorbelli¹,

Chatterjee²,

Coró³

et al. 2023

Preprint

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“…The experimental results show that the federated learning based method can effectively solve the communication and routing problems of unmanned aerial vehicles in heterogeneous networks. Yucel Aydin [5], [6] proposed a group based identity authentication method based on the uncertainty of drone movement, frequent ground to air communication, identity authentication between drone clusters, and communication overhead. This method solves the problem of high identity authentication delay caused by communication delay in ground base stations, and the inability of single drone shared identity authentication to expand in drone clusters.…”

Section: Introductionmentioning

confidence: 99%

“…Currently, federated learning methods can effectively solve the security issues of private data. [5], [6] Considered privacy issues between drones, but did not fundamentally address data security issues.…”

Section: Introductionmentioning

confidence: 99%

AHFL-UAVs: adaptive loss isomorphism federated learning algorithm for UAV swarms

Meng,

Nie,

et al. 2023

Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023)

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UAV swarms have crucial applications in modern military, geological exploration, and 5G/6G communication fields. As communication nodes, drones frequently exchange wireless data with other drones, and data privacy protection is currently one of the most urgent research topics. Based on this, this article proposes an efficient isomorphic federated learning algorithm for unmanned aerial vehicle clusters. First, set the adaptive loss differential adaptive parameter, with the initial value set to a floating point number not greater than 0.01, then activate it with the Tanh function, participate in the training as part of the loss function during the training process, and optimize it by gradient descent. When the UAV node uploads the gradient to the wingman, sum the adaptive loss differential parameter with the gradient matrix as a disturbance term. Then, based on the dynamic confidence matrix, high-quality drones are selected to participate in the gradient security aggregation of drones, achieving the selection of high-quality drones. The built-in global neural network of the drone transmits shared parameters indiscriminately to each drone through broadcast to achieve updates to the shared parameters. The comparative experiments of our algorithm on the Fashion and Cifar10 datasets show that our algorithm has higher accuracy, with the highest accuracy improvement of 4.42% on the Mnist dataset and 8.22% on the Cifar10 dataset.

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“…Several authentication mechanisms have been proposed in the literature to improve drone security. In [8], a low-latency solution for drone swarms in fifth-generation (5G) networks is proposed, operating with shared keys among the drones; a group authentication technique has been proposed in [9]; the work in [10] proposes a distributed delegation-based authentication mechanism to reduce the traffic overhead toward the 5G core network; a solution leveraging blockchain technology has been proposed in [11]. All these approaches are based on cryptography.…”

Section: Introductionmentioning

confidence: 99%