Task Selection and Scheduling in UAV-Enabled MEC for Reconnaissance With Time-Varying Priorities

Qin, Zhen; Wang, Hai; Wei, Zhenhua; Qu, Yuben; Xiong, Fei; Dai, Haipeng; Wu, Tao

doi:10.1109/jiot.2021.3078746

Cited by 31 publications

(24 citation statements)

References 50 publications

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“…Because usually, UAV-ground communications consist of more line-of-sight (LoS) links and some multiple paths due to scattering or reflection of ground buildings, trees, etc. [31]. So, Rician distribution fits well to a stronger line-of-sight environment while Nakagami-m distributions are utilized to model dense scatters.…”

Section: System Modelmentioning

confidence: 99%

AI-Empowered Fast Task Execution Decision for Delay-Sensitive IoT Applications in Edge Computing Networks

et al. 2023

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As the number of smart connected devices increases day by day, a massive amount of tasks are generated by various types of Internet of Things (IoT) devices. Intelligent edge computing is a promising enabler in next-generation wireless networks to execute these tasks on proximate edge servers instead of smart devices. Additionally, regarding the execution of tasks in edge servers, smart devices could provide a low-latency environment to the end users. Within this paper, an artificial intelligence (AI)-empowered fast task execution method in heterogeneous IoT applications is proposed to reduce decision latency by taking into account different system parameters such as the execution deadline of the task, battery level of devices, channel conditions between mobile devices and edge servers, and edge server capacity. In edge computing scenarios, the number of task requests, resource constraints of edge servers, mobility of connected devices, and energy consumption are the main performance considerations. In this paper, the AI-empowered fast task decision method is proposed to solve the multi-device edge computing task execution problem by formulating it as a multi-class classification problem. The extensive simulation results demonstrate that the proposed framework is extremely fast and precise in decision-making for offloading computation tasks compared to the conventional Lyapunov optimization-based algorithm results by ensuring the guaranteed quality of experience.

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Section: System Modelmentioning

confidence: 99%

AI-Empowered Fast Task Execution Decision for Delay-Sensitive IoT Applications in Edge Computing Networks

et al. 2023

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“…There are several existing task scheduling solutions for near-end services at the network edge. Qin 16 , et al have provided reconnaissance utility-based task scheduling in multi-access edge computing enabled by UAV. Tang 17 , et al have conceived functions (monitoring, scheduling) as microservices for data sharing among aircraft nodes.…”

Section: Proposed Solutionmentioning

confidence: 99%

Task Scheduling in Fog Node within the Tactical Cloud

Nair

Bhanu

2022

Def. Sc. J.

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Fog computing architecture competent to support the mission-oriented network-centric warfare provides the framework for a tactical cloud in this work. The tactical cloud becomes situation-aware of the war from the information relayed by fog nodes (FNs) on the battlefield. This work aims to sustain the network of FNs by maintaining the operational efficiency of the FNs on the battlefield at the tactical edge. The proposed solution monitors and predicts the likely overloading of an FN using the long short-term memory model through a buddy FN at the fog server (FS). This paper also proposes randomised task scheduling (RTS) algorithm to avert the likely overloading of an FN by pre-empting tasks from the FN and scheduling them to another FN. The experimental results demonstrate that RTS with linear complexity has a schedulability measure 8% - 26% higher than that of other base scheduling algorithms. The results show that the LSTM model has low mean absolute error compared to other time-series forecasting models.

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“…[30] has proposed the UAV assisted MEC framework for the time-sensitive IoT users, and the number of successful served IoT devices and the resource-efficient UAV trajectory has been coupled to been optimized. A reconnaissance task selection and scheduling by the UAV-based MEC structure has been investigated in [31], in which the reconnaissance task has time-varying priority, and the total reconnaissance utility has been maximized in the optimization problem. In [32], the authors have investigated the computation offloading optimization of UAVs in different layers by combining the channel allocation and position scheduling as well, in which the Stackelberg game has been employed to model the leader and follower relations between the two-layer UAVs.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In light of [31], denote ρ u as the computing resource consumed on UAVs to handle 1bit IoT data, i.e., the CPU cycles. Thus, the time cost by UAV to complete the computation for IoT i is…”

Section: ) Uav-based Computingmentioning

confidence: 99%

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Hierarchical Aerial Computing for Internet of Things via Cooperation of HAPs and UAVs

Jia¹,

Wu²,

Chen³

et al. 2022

Preprint

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With the explosive increment of computation requirements, the multi-access edge computing (MEC) paradigm appears as an effective mechanism. Besides, as for the Internet of Things (IoT) in disasters or remote areas requiring MEC services, unmanned aerial vehicles (UAVs) and high altitude platforms (HAPs) are available to provide aerial computing services for these IoT devices. In this paper, we develop the hierarchical aerial computing framework composed of HAPs and UAVs, to provide MEC services for various IoT applications. In particular, the problem is formulated to maximize the total IoT data computed by the aerial MEC platforms, restricted by the delay requirement of IoT and multiple resource constraints of UAVs and HAPs, which is an integer programming problem and intractable to solve. Due to the prohibitive complexity of exhaustive search, we handle the problem by presenting the matching game theory based algorithm to deal with the offloading decisions from IoT devices to UAVs, as well as a heuristic algorithm for the offloading decisions between UAVs and HAPs. The external effect affected by interplay of different IoT devices in the matching is tackled by the externality elimination mechanism. Besides, an adjustment algorithm is also proposed to make the best of aerial resources. The complexity of proposed algorithms is analyzed and extensive simulation results verify the efficiency of the proposed algorithms, and the system performances are also analyzed by the numerical results.

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Task Selection and Scheduling in UAV-Enabled MEC for Reconnaissance With Time-Varying Priorities

Cited by 31 publications

References 50 publications

AI-Empowered Fast Task Execution Decision for Delay-Sensitive IoT Applications in Edge Computing Networks

AI-Empowered Fast Task Execution Decision for Delay-Sensitive IoT Applications in Edge Computing Networks

Task Scheduling in Fog Node within the Tactical Cloud

Hierarchical Aerial Computing for Internet of Things via Cooperation of HAPs and UAVs

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