A comprehensive survey on aerial mobile edge computing: Challenges, state-of-the-art, and future directions

Song, Zhengyu; Qin, Xintong; Hao, Yuanyuan; Hou, Tianwei; Wang, Jun; Sun, Xiaoying

doi:10.1016/j.comcom.2022.05.004

Cited by 28 publications

(8 citation statements)

References 199 publications

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“…The authors of [35] analyzed how edge computing and AI influence various technical aspects of UAVs, including power management, formation control, autonomous navigation, privacy and security, and communication. In [36], the authors provided an Sensors 2024, 24, 5 of 31 overview of an aerial MEC. The main focus of this study was the optimization of several challenges, such as resource management, UAV trajectory, and the computation offloading of aerial MEC.…”

Section: Related Surveysmentioning

confidence: 99%

Trajectory-Aware Offloading Decision in UAV-Aided Edge Computing: A Comprehensive Survey

Baidya,

Nabi,

Moh

2024

Sensors

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Recently, the integration of unmanned aerial vehicles (UAVs) with edge computing has emerged as a promising paradigm for providing computational support for Internet of Things (IoT) applications in remote, disaster-stricken, and maritime areas. In UAV-aided edge computing, the offloading decision plays a central role in optimizing the overall system performance. However, the trajectory directly affects the offloading decision. In general, IoT devices use ground offload computation-intensive tasks on UAV-aided edge servers. The UAVs plan their trajectories based on the task generation rate. Therefore, researchers are attempting to optimize the offloading decision along with the trajectory, and numerous studies are ongoing to determine the impact of the trajectory on offloading decisions. In this survey, we review existing trajectory-aware offloading decision techniques by focusing on design concepts, operational features, and outstanding characteristics. Moreover, they are compared in terms of design principles and operational characteristics. Open issues and research challenges are discussed, along with future directions.

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Section: Related Surveysmentioning

confidence: 99%

Trajectory-Aware Offloading Decision in UAV-Aided Edge Computing: A Comprehensive Survey

Baidya,

Nabi,

Moh

2024

Sensors

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“…The associate editor coordinating the review of this manuscript and approving it for publication was Haris Pervaiz . devices to execute their delay-sensitive and computationintensive applications locally, multi-access edge computing (MEC) has emerged as one of potential technologies to broaden the capability of ground IoT devices [3], [4]. Thanks to the low altitudes of LEO satellites, the propagation delay from a ground device to its visible LEO satellites can be reduced to 1-4 ms [5].…”

Section: Introductionmentioning

confidence: 99%

Joint Communication, Computing, and Caching Resource Allocation in LEO Satellite MEC Networks

et al. 2023

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Driven by the urgent requirement of ubiquitous and reliable coverage for global users, the low earth orbit (LEO) satellite network has attracted numerous attentions from the academic and industry circles. By deploying multi-access edge computing (MEC) servers in LEO satellites, computation offloading and content caching services can be provided for remote Internet-of-Things (IoT) devices without proximal servers. In this paper, the joint optimization of computation offloading, radio resource allocation and caching placement in LEO satellite MEC networks are investigated. The problem is formulated to minimize the total delay of all ground IoT devices while ensuring the energy, computing and caching constraints. To solve this mixed-integer and non-convex problem, a Lagrange dual decomposition (LDD)-based algorithm is proposed to obtain the closed-form optimal solution. Then, a heuristic algorithm is proposed to further reduce the computation complexity. Numerical results validate that both algorithms are effective compared to the optimal exhaustive search, the full local computing and the full MEC methods. Besides, the offloading ratio and the average delay of all IoT devices with different numbers and computing capacities of devices and satellites are also demonstrated.INDEX TERMS LEO satellites, multi-access edge computing, computation offloading, resource allocation, caching.

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“…Due to the high probability of line of sight (LoS) linkages with GUs, UAVs are less impacted by channel limitations. With the help of these features, UAVs can contribute significantly to MEC systems and overcomes terrestrial server deployment deficiencies [14][15][16][17]. UAV-enabled edge computing is an obvious and viable option for future networks.…”

Section: Introductionmentioning

confidence: 99%

“…DRL is widely adopted in the network research area for solving complex problems such as resource management, energy-saving, power allocation, efficient routing, traffic management, etc. More specifically, from the recent literature [8,17] on UAVs-enabled MEC, DRL has been used for trajectory planning, energy management, user scheduling, resource management, resource provisioning, bit allocation, and throughput maximization. However, the efficient and optimal path planning in UAVs to accommodate the processing and communication in a large variety of devices is still a crucial and challenging problem.…”

Section: Introductionmentioning

confidence: 99%

DQN-Based Proactive Trajectory Planning of UAVs in Multi-Access Edge Computing

Khan¹,

Zhang²,

Ahmad³

et al. 2023

Computers, Materials &Amp; Continua

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The main aim of future mobile networks is to provide secure, reliable, intelligent, and seamless connectivity. It also enables mobile network operators to ensure their customer's a better quality of service (QoS). Nowadays, Unmanned Aerial Vehicles (UAVs) are a significant part of the mobile network due to their continuously growing use in various applications. For better coverage, cost-effective, and seamless service connectivity and provisioning, UAVs have emerged as the best choice for telco operators. UAVs can be used as flying base stations, edge servers, and relay nodes in mobile networks. On the other side, Multi-access Edge Computing (MEC) technology also emerged in the 5G network to provide a better quality of experience (QoE) to users with different QoS requirements. However, UAVs in a mobile network for coverage enhancement and better QoS face several challenges such as trajectory designing, path planning, optimization, QoS assurance, mobility management, etc. The efficient and proactive path planning and optimization in a highly dynamic environment containing buildings and obstacles are challenging. So, an automated Artificial Intelligence (AI) enabled QoSaware solution is needed for trajectory planning and optimization. Therefore, this work introduces a well-designed AI and MEC-enabled architecture for a UAVs-assisted future network. It has an efficient Deep Reinforcement Learning (DRL) algorithm for real-time and proactive trajectory planning and optimization. It also fulfills QoS-aware service provisioning. A greedypolicy approach is used to maximize the long-term reward for serving more users with QoS. Simulation results reveal the superiority of the proposed DRL mechanism for energy-efficient and QoS-aware trajectory planning over the existing models.

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A comprehensive survey on aerial mobile edge computing: Challenges, state-of-the-art, and future directions

Cited by 28 publications

References 199 publications

Trajectory-Aware Offloading Decision in UAV-Aided Edge Computing: A Comprehensive Survey

Trajectory-Aware Offloading Decision in UAV-Aided Edge Computing: A Comprehensive Survey

Joint Communication, Computing, and Caching Resource Allocation in LEO Satellite MEC Networks

DQN-Based Proactive Trajectory Planning of UAVs in Multi-Access Edge Computing

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