Secure Aerial Computing: Convergence of Mobile Edge Computing and Blockchain for UAV Networks

Tang, Qingqing; Fei, Zesong; Zheng, Jianchao; Li, Bin; Wang, Jing

doi:10.1109/tvt.2022.3189818

Cited by 17 publications

(11 citation statements)

References 31 publications

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“…Tang et al [ 30 ] propose a secure mobile-edge computing system that ensures the privacy of user data during the computation offloading process. The authors combine a block coordinate descent algorithm with a successive convex approximation method to generate optimal trajectories for UAVs.…”

Section: Related Workmentioning

confidence: 99%

Secure UAV adhoc network with blockchain technology

Alqarni

2024

PLoS ONE

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Recent advances in aerial robotics and wireless transceivers have generated an enormous interest in networks constituted by multiple compact unmanned aerial vehicles (UAVs). UAV adhoc networks, i.e., aerial networks with dynamic topology and no centralized control, are found suitable for a unique set of applications, yet their operation is vulnerable to cyberattacks. In many applications, such as IoT networks or emergency failover networks, UAVs augment and provide support to the sensor nodes or mobile nodes in the ground network in data acquisition and also improve the overall network performance. In this situation, ensuring the security of the adhoc UAV network and the integrity of data is paramount to accomplishing network mission objectives. In this paper, we propose a novel approach to secure UAV adhoc networks, referred to as the blockchain-assisted security framework (BCSF). We demonstrate that the proposed system provides security without sacrificing the performance of the network through blockchain technology adopted to the priority of the message to be communicated over the adhoc UAV network. Theoretical analysis for computing average latency is performed based on queuing theory models followed by an evaluation of the proposed BCSF approach through simulations that establish the superior performance of the proposed methodology in terms of transaction delay, data secrecy, data recovery, and energy efficiency.

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Section: Related Workmentioning

confidence: 99%

Secure UAV adhoc network with blockchain technology

Alqarni

2024

PLoS ONE

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“…They designed a well-tailored alternating direction method of multipliers algorithm and applied Lyapunov optimization to solve the system's energy-efficiency-maximizing optimization problem. Q. Tang et al [32] integrated MEC and blockchain technology for UAV networks to establish a secure aerial computing architecture. On this basis, they minimized the weighted sum of energy consumption and delay.…”

Section: Joint Optimization Of Resource Allocation and Uav Trajectory...mentioning

confidence: 99%

“…The above shows that all neural networks were fully connected neural networks. On this basis, in the absence of a special statement, in the single-agent case, the VNF-Q-Network has no hidden layer; the hidden layers of the Trajectory Actor Network have [32,16,8] neurons; the hidden layers of the Critic Network have [32,8,2] neurons. In the multiagent case, the hidden layers of VNF-Q-Network m(m ∈ M) have [32,16] neurons; the hidden layers of the Trajectory Actor Network m have [32,16,8] neurons; the hidden layers of the Critic Network m have [32,8,2] neurons.…”

Section: Performance Evaluationmentioning

confidence: 99%

Online Joint Optimization of Virtual Network Function Deployment and Trajectory Planning for Virtualized Service Provision in Multiple-Unmanned-Aerial-Vehicle Mobile-Edge Networks

He,

Liang

2024

Electronics

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The multiple-unmanned-aerial-vehicle (multi-UAV) mobile edge network is a promising networking paradigm that uses multiple resource-limited and trajectory-planned unmanned aerial vehicles (UAVs) as edge servers, upon which on-demand virtual network functions (VNFs) are deployed to provide low-delay virtualized network services for the requests of ground users (GUs), who often move randomly and have difficulty accessing the Internet. However, VNF deployment and UAV trajectory planning are both typical NP-complete problems, and the two operations have a strong coupling effect: they affect each other. Achieving optimal virtualized service provision (i.e., maximizing the number of accepted GU requests under a given period T while minimizing the energy consumption and the cost of accepting the requests in all UAVs) is a challenging issue. In this paper, we propose an improved online deep reinforcement learning (DRL) scheme to tackle this issue. First, we formulate the joint optimization of the two operations as a nonconvex mixed-integer nonlinear programming problem, which can be viewed as a sequence of one-frame joint VNF deployment and UAV-trajectory-planning optimization subproblems. Second, we propose an online DRL based on jointly optimizing discrete (VNF deployment) and continuous (UAV trajectory planning) actions to solve each subproblem, whose key idea is establishing and achieving the coupled influence of discrete and continuous actions. Finally, we evaluate the proposed scheme through extensive simulations, and the results demonstrate its effectiveness.

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“…Recently, blockchain technology is considered a possible solution to address the above challenges because of its advantages of decentralization, anonymity, and trust [195]. Blockchain is a decentralized ledger-based storage method, which provides a unique tool for secure transactions in a distributed fashion without trusted agents [196]. Since the SE framework has distributed structure, blockchain technology can well suit this framework supporting high security and privacy for integrated VHetNets.…”

Section: A Core Challenges Of Integrating Ai/ml Solutions In Sei-vhet...mentioning

confidence: 99%