Secure Communications for UAV-Enabled Mobile Edge Computing Systems

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

doi:10.1109/tcomm.2019.2947921

Cited by 227 publications

(116 citation statements)

References 30 publications

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“…Alzenad et al [8] proposed an optimal placement algorithm for maximizing the number of covered users using the minimum transmit power. Furthermore, UAV served as mobile edge computing (MEC) have been studied in [9], [10]. In [9], a secure UAV system has been studied where multiple ground users offload the tasks to the UAV-enabled MEC system in the presence of several eavesdropping UAVs.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, UAV served as mobile edge computing (MEC) have been studied in [9], [10]. In [9], a secure UAV system has been studied where multiple ground users offload the tasks to the UAV-enabled MEC system in the presence of several eavesdropping UAVs. In [10], large-scale mobile users was considered in the multi-UAV enabled MEC, where offloading decision and resource allocation were studied.…”

Section: Introductionmentioning

confidence: 99%

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Packet Error Probability and Effective Throughput for Ultra-Reliable and Low-Latency UAV Communications

Wang

Pan

Ren

et al. 2021

IEEE Trans. Commun.

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In this paper, we study the average packet error probability (APEP) and effective throughput (ET) of the control link in unmanned-aerial-vehicle (UAV) communications, where the ground central station (GCS) sends control signals to the UAV that requires ultra-reliable and low-latency communications (URLLC). To ensure the low latency, short packets are adopted for the control signal. As a result, the Shannon capacity theorem cannot be adopted here due to its assumption of infinite channel blocklength. We consider both free space (FS) and 3-Dimensional (3D) channel models by assuming that the locations of the UAV are randomly distributed within a restricted space. We first characterize the statistical characteristics of the signal-to-noise ratio (SNR) for both FS and 3D models. Then, the closed-form analytical expressions of APEP and ET are derived by using Gaussian-Chebyshev quadrature. Also, the lower bounds are derived to obtain more insights. Finally, we obtain the optimal value of packet length with the objective of maximizing the ET by applying one-dimensional search. Our analytical results are verified by the Monte-Carlo simulations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Packet Error Probability and Effective Throughput for Ultra-Reliable and Low-Latency UAV Communications

Wang

Pan

Ren

et al. 2021

IEEE Trans. Commun.

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“…Mobile Edge Computing (MEC) provides communication services and near-user processing facilities to users and has been a promising technology for the further UAV communication [82][83][84]. UAV-enabled MEC networks are promising to increase computing efficiency and reduce execution latency.…”

Section: Uav-enabled Mobile Edge Computingmentioning

confidence: 99%

Communication and networking technologies for UAVs: A survey

Sharma

Vanjani

Paliwal

et al. 2020

Journal of Network and Computer Applications

238

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With the advancement in drone technology, in just a few years, drones will be assisting humans in every domain. But there are many challenges to be tackled, communication being the chief one. This paper aims at providing insights into the latest UAV (Unmanned Aerial Vehicle) communication technologies through investigation of suitable task modules, antennas, resource handling platforms, and network architectures. Additionally, we explore techniques such as machine learning and path planning to enhance existing drone communication methods.Encryption and optimization techniques for ensuring long−lasting and secure communications, as well as for power management, are discussed.Moreover, applications of UAV networks for different contextual uses ranging from navigation to surveillance, URLLC (Ultra-reliable and low−latency communications), edge computing and work related to artificial intelligence are examined. In particular, the intricate interplay between UAV, advanced cellular communication, and internet of things constitutes one of the focal points of this paper. The survey encompasses lessons learned, insights, challenges, open issues, and future directions in UAV communications.

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“…A novel unmanned aerial vehicle (UAV) flight trajectory optimization was studied in [12] to maximize the minimum secrecy rate of ground terminals. In [13], a secure mobile edge computing (MEC) system was proposed where ground users can offload computing tasks to the legitimate UAV in the presence of multiple eavesdropping UAVs. A novel secure transmission scheme was developed in [14] to fight against the eavesdropping in downlink multi-input single-output non-orthogonal multiple access (NOMA) networks.…”

mentioning

confidence: 99%

Resource Allocation for Secure URLLC in Mission-Critical IoT Scenarios

Ren

Pan

Deng

et al. 2020

IEEE Trans. Commun.

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117

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Ultra-reliable low latency communication (URLLC) is one of three primary use cases in the fifth-generation (5G) networks, and its research is still in its infancy due to its stringent and conflicting requirements in terms of extremely high reliability and low latency. To reduce latency, the channel blocklength for packet transmission is finite, which incurs transmission rate degradation and higher decoding error probability. In this case, conventional resource allocation based on Shannon capacity achieved with infinite blocklength codes is not optimal. Security is another critical issue in mission-critical internet of things (IoT) communications, and physical-layer security is a promising technique that can ensure the confidentiality for wireless communications as no additional channel uses are needed for the key exchange as in the conventional upper-layer cryptography method. This paper is the first work to study the resource allocation for a secure mission-critical IoT communication system with URLLC. Specifically, we adopt the security capacity formula under finite blocklength and consider two optimization problems: weighted throughput maximization problem and total transmit power minimization problem. Each optimization problem is non-convex and challenging to solve, and we develop efficient methods to solve each optimization problem. Simulation results confirm the fast convergence speed of our proposed algorithm and demonstrate the performance advantages over the existing benchmark algorithms.

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Secure Communications for UAV-Enabled Mobile Edge Computing Systems

Cited by 227 publications

References 30 publications

Packet Error Probability and Effective Throughput for Ultra-Reliable and Low-Latency UAV Communications

Packet Error Probability and Effective Throughput for Ultra-Reliable and Low-Latency UAV Communications

Communication and networking technologies for UAVs: A survey

Resource Allocation for Secure URLLC in Mission-Critical IoT Scenarios

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