Secure Task Offloading for MEC-Aided-UAV System

Chen, Peipei; Xue-shan, Luo; Guo, Deke; Sun, Yuchen; Xie, Junjie; Zhao, Yawei; Zhou, Rui

doi:10.1109/tiv.2022.3227367

Cited by 12 publications

(7 citation statements)

References 44 publications

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“…To maximise computational efficiency and task queue stability, Ding et al propose a DRL-based scheme to optimise offloading and resource allocation [20,21]. Considering the limited computational resources of UAVs, Chen et al propose a strategy for offloading to ground-based base station servers to optimise transmit power and base station selection under the practical constraints of task completion latency and power consumption [22]. In a UAV-enabled mobile edge computing system based on device-to-device communication, the overall energy efficiency is maximised by optimising UAV and node transmit power and scheduling strategies in order to improve the balance between different types of nodes [23].…”

Section: Related Workmentioning

confidence: 99%

Resource Allocation and Offloading Strategy for UAV-Assisted LEO Satellite Edge Computing

Zhang

Jiang

et al. 2023

Drones

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In emergency situations, such as earthquakes, landslides and other natural disasters, the terrestrial communications infrastructure is severely disrupted and unable to provide services to terrestrial IoT devices. However, tasks in emergency scenarios often require high levels of computing power and energy supply that cannot be processed quickly enough by devices locally and require computational offloading. In addition, offloading tasks to server-equipped edge base stations may not always be feasible due to the lack of infrastructure or distance. Since Low Orbit Satellites (LEO) have abundant computing resources, and Unmanned Aerial Vehicles (UAVs) have flexible deployment, offloading tasks to LEO satellite edge servers via UAVs becomes straightforward, which provides computing services to ground-based devices. Therefore, this paper investigates the computational tasks and resource allocation in a UAV-assisted multi-layer LEO satellite network, taking into account satellite computing resources and device task volumes. In order to minimise the weighted sum of energy consumption and delay in the system, the problem is formulated as a constrained optimisation problem, which is then transformed into a Markov Decision Problem (MDP). We propose a UAV-assisted airspace integration network architecture, and a Deep Deterministic Policy Gradient and Long short-term memory (DDPG-LSTM)-based task offloading and resource allocation algorithm to solve the problem. Simulation results demonstrate that the solution outperforms the baseline approach and that our framework and algorithm have the potential to provide reliable communication services in emergency situations.

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Section: Related Workmentioning

confidence: 99%

Resource Allocation and Offloading Strategy for UAV-Assisted LEO Satellite Edge Computing

Zhang

Jiang

et al. 2023

Drones

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“…In UAV-assisted edge computing systems, security can be significantly improved by introducing artificial noise (AN) [17][18][19][20][21][22]. In [17], Xu et al divided the transmitted power of the UAV into two parts, that is, offloading power and AN, in order to enhance the security performance of the system.…”

Section: Introductionmentioning

confidence: 99%

“…In [18], Xu et al introduced a UAV for suppressing eavesdroppers to enhance the security of the system. Unlike the case of [18] where the eavesdropper information is known, the authors of [19] maximize the minimum secure computational bits of the system by optimizing the trajectory of the UAV and the transmit power of the AN in unknown locations and the number of eavesdroppers. In [20], Lu et al studied a dual UAV system in which a legitimate UAV provides computing services and a jammer emits AN to disrupt the eavesdropping UAV to improve system security.…”

Section: Introductionmentioning

confidence: 99%

“…However, the scenario of the eavesdropper movement has not been sufficiently investigated. To improve the communication security, AN is introduced to disrupt the eavesdropper or communication techniques such as beamforming are used to degrade the SNR at the eavesdropper's side [19,29]. Based on the AN technique, RIS is introduced to assist the secure computing system in order to enhance the secure computing capability of the system.…”

Section: Introductionmentioning

confidence: 99%

“…In UAV‐assisted edge computing systems, security can be significantly improved by introducing artificial noise (AN) [17–22]. In [17], Xu et al.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Resource and trajectory optimization for secure communication in RIS assisted UAV‐MEC system

Liu,

Huang,

et al. 2023

IET Communications

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The combination of unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) is considered as a promising approach to tackle soaring computing requirements. The broadcast nature of air‐to‐ground (A2G) links makes UAV communications vulnerable to eavesdroppers, so secure UAV communications remain an open question. This paper proposes a secure communication scheme for reconfigurable intelligent surface (RIS)‐assisted UAV‐MEC systems, in which the RIS assists the user in offloading data to the legitimate UAV, and the legitimate UAV provides computing services to the user. To fully expand the security computing capacity of the system, the communication link is improved by introducing RIS, and the jammer interferes with the eavesdropper. The secure computing capability of the system is maximized by optimizing communication resources and trajectories. Since the proposed problem is non‐convex, successive convex approximation (SCA) technique and block coordinate descent (BCD) technique is combined to solve the problem. The simulation results show that the proposed scheme in this paper can effectively improve the system secure computing bits compared with the benchmark scheme.

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A computation offloading strategy for multi-access edge computing based on DQUIC protocol

Yang,

Ma,

et al. 2024

J Supercomput

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Secure Task Offloading for MEC-Aided-UAV System

Cited by 12 publications

References 44 publications

Resource Allocation and Offloading Strategy for UAV-Assisted LEO Satellite Edge Computing

Resource Allocation and Offloading Strategy for UAV-Assisted LEO Satellite Edge Computing

Resource and trajectory optimization for secure communication in RIS assisted UAV‐MEC system

A computation offloading strategy for multi-access edge computing based on DQUIC protocol

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