Offloading Optimization for Low-Latency Secure Mobile Edge Computing Systems

Zhou, Yi; Yeoh, Phee Lep; Pan, Cunhua; Wang, Kezhi; Elkashlan, Maged; Wang, Zhongfeng; Vucetic, Branka; Li, Yonghui

doi:10.1109/lwc.2019.2959579

Cited by 40 publications

(18 citation statements)

References 10 publications

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“…We develop an iterative method that effectively solves problem (2). Specifically, first, we relax the integer variables in user association indicators (π) into continuous variables.…”

Section: Distributed Solution For Optimal System Resource Allocationmentioning

confidence: 99%

“…Mobile edge computing (MEC) has been recently considered as one of the promising solutions for Internet-of-Things (IoT) devices (e.g., sensors, smartphones, etc.) to reduce end-to-end latency by offloading their computing tasks to surrounding macro base stations (MBS) equipped with powerful computing resources [1], [2]. However, when large-scale scenarios with the heterogeneous deployment of IoT devices (UEs) and edge servers in the MEC system are considered, the challenges in designing the optimal offloading strategy with efficient resource allocation grow significantly due to the network size and dynamics.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Digital Twin-Aided Intelligent Offloading With Edge Selection in Mobile Edge Computing

Do‐Duy

Huynh

Dobre

et al. 2022

IEEE Wireless Commun. Lett.

101

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In this paper, we study a mobile edge computing (MEC) architecture with the assistance of digital twin (DT) applied for industrial automation where multiple Internet-of-Things (IoT) devices intelligently offload computing tasks to multiple MEC servers to reduce end-to-end latency. To do so, first we propose and formulate a practical end-to-end latency minimisation problem in the DT-assisted MEC model subject to the constraints of quality-of-services and computation resource at the IoT devices and MEC servers in industrial IoT networks. Then, we solve the proposed latency minimisation problem by iteratively optimising the transmit power of IoT devices, user association, intelligent task offloading, and estimated CPU processing rate of the devices. Finally, simulation results are conducted to prove the effectiveness of the proposed method in terms of the latency performance compared with some conventional methods.

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“…We develop an iterative method that effectively solves problem (2). Specifically, first, we relax the integer variables in user association indicators (π) into continuous variables.…”

Section: Distributed Solution For Optimal System Resource Allocationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Digital Twin-Aided Intelligent Offloading With Edge Selection in Mobile Edge Computing

Do‐Duy

Huynh

Dobre

et al. 2022

IEEE Wireless Commun. Lett.

101

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show abstract

“…In the field of communication, UAVs have been studied for their ability to act as aerial BSs and relays to serve the ground users or coordinate with terrestrial networks [23]. For example, Zhou et al [24], [25] designed a novel UAV-enabled mobile edge computing (MEC) system that innovatively uses UAV platform to provide computation offloading services with satisfactory security and latency for ground users. It is noteworthy that UAVs are also suitable for being employed as aerial anchor nodes to provide positioning services [26], especially in jamming environments.…”

Section: A Motivationmentioning

confidence: 99%

Feasibility Study of UAV-Assisted Anti-Jamming Positioning

Wang

Liu

et al. 2021

IEEE Trans. Veh. Technol.

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As the cost and technical difficulty of jamming devices continue to decrease, jamming has become one of the major threats to positioning service. Unfortunately, most conventional wireless positioning technologies are vulnerable to jamming attacks due to inherent shortcomings like weak signal strength and unfavorable anchor geometry. Thanks to their high operational flexibility, unmanned aerial vehicles (UAVs) could be a promising solution to the above challenges. Therefore, in this article, we propose a UAV-assisted anti-jamming positioning system, in which multiple UAVs first utilize time-difference-ofarrival (TDoA) measurements from ground reference stations and double-response two-way ranging (DR-TWR) measurements from UAV-to-UAV links to perform self-localization as well as clock synchronization, and then act as anchor nodes to provide TDoA positioning service for ground users in the presence of jamming. To evaluate the feasibility and performance of the proposed system, we first derive the Cramér-Rao lower bound (CRLB) of UAV self-localization. Then, the impacts of UAV position uncertainty and synchronization errors caused by jamming on positioning service are modeled, and the theoretical root-mean-square error (RMSE) of user position estimate is further derived. Numerical results demonstrate that the proposed system is a promising alternative to existing positioning systems when their services are disrupted by jamming. The most notable advantage of the proposed system is that it is fully compatible with existing user equipment terminals and positioning methods. Index Terms-Unmanned aerial vehicle (UAV), anti-jamming positioning, time-difference-of-arrival (TDoA), double-response two-way ranging (DR-TWR).

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“…For the edge computing, IoT device m will offload its task J m to the nearby MEC server. Like some studies such as [23], for simplifying the problem, only one MEC server is deployed in one block of the city. In addition, our study focuses on one block.…”

Section: Edge Computingmentioning

confidence: 99%

Distributed Learning Based Joint Communication and Computation Strategy of IoT Devices in Smart Cities

Liu

Luo

et al. 2020

Sensors

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With the development of global urbanization, the Internet of Things (IoT) and smart cities are becoming hot research topics. As an emerging model, edge computing can play an important role in smart cities because of its low latency and good performance. IoT devices can reduce time consumption with the help of a mobile edge computing (MEC) server. However, if too many IoT devices simultaneously choose to offload the computation tasks to the MEC server via the limited wireless channel, it may lead to the channel congestion, thus increasing time overhead. Facing a large number of IoT devices in smart cities, the centralized resource allocation algorithm needs a lot of signaling exchange, resulting in low efficiency. To solve the problem, this paper studies the joint policy of communication and computing of IoT devices in edge computing through game theory, and proposes distributed Q-learning algorithms with two learning policies. Simulation results show that the algorithm can converge quickly with a balanced solution.

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Offloading Optimization for Low-Latency Secure Mobile Edge Computing Systems

Cited by 40 publications

References 10 publications

Digital Twin-Aided Intelligent Offloading With Edge Selection in Mobile Edge Computing

Digital Twin-Aided Intelligent Offloading With Edge Selection in Mobile Edge Computing

Feasibility Study of UAV-Assisted Anti-Jamming Positioning

Distributed Learning Based Joint Communication and Computation Strategy of IoT Devices in Smart Cities

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