Joint Task Offloading and Resource Allocation for Fog-Based Intelligent Transportation Systems: A UAV-Enabled Multi-Hop Collaboration Paradigm

Tong, Shi-Yuan; Liu, Yun; Mišić, Jelena; Chang, Xiaolin; Zhang, Zhenjiang; Wang, Chunyan

doi:10.1109/tits.2022.3163804

Cited by 14 publications

(3 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The task offloading was transformed into a two-sided matching problem, then the authors designed a distributed algorithm by the dynamic of UAVs to reduce task delay. The UAVs-assisted MEC technology was combined with intelligent transportation systems in [20]. The authors proposed a UAV-enabled multi-hop collaborative framework to maximize user experience and task delay in each time slot.…”

Section: Related Workmentioning

confidence: 99%

Joint Task Offloading, Resource Allocation, and Trajectory Design for Multi-UAV Cooperative Edge Computing With Task Priority

Hao,

Xu,

Zhang

et al. 2024

IEEE Trans. on Mobile Comput.

View full text Add to dashboard Cite

Mobile edge computing (MEC) has emerged as a solution to address the demands of computation-intensive network services by providing computational capabilities at the network edge, thus reducing service delay. Due to the flexible deployment, wide coverage and reliable wireless communication, unmanned aerial vehicles (UAVs) have been employed to assist MEC. This paper investigates the task offloading problem in a UAV-assisted MEC system with collaboration of multiple UAVs, highlighting task priorities and binary offloading mode. We defined the system gain based on energy consumption and task delay. The joint optimization of UAVs' trajectory design, binary offloading decision, computation resources allocation, and communication resources management is formulated as a mixed integer programming problem with the goal of maximizing the long-term average system gain. Considering the discretecontinuous hybrid action space of this problem, we propose a novel deep reinforcement learning (DRL) algorithm based on the latent space to solve it. The evaluation results demonstrate that our proposed algorithm outperforms four state-of-the-art alternative solutions in terms of task delay and system gain.

show abstract

Section: Related Workmentioning

confidence: 99%

Joint Task Offloading, Resource Allocation, and Trajectory Design for Multi-UAV Cooperative Edge Computing With Task Priority

Hao,

Xu,

Zhang

et al. 2024

IEEE Trans. on Mobile Comput.

View full text Add to dashboard Cite

show abstract

“…Different collaboration strategies have different optimization ideas. There are two types of collaboration in computation offloading, they are communication collaboration (the collaboration for communication between different devices) [139] and computing collaboration (the collaboration for computing between different devices) [35], [42]. Thus, the collaboration strategy for computation offloading can be divided into three different categories, they are non-collaboration, computing collaboration and communication collaboration.…”

Section: A Computation Offloading Processmentioning

confidence: 99%

“…Communication collaboration is also divided into two categories, they are communication collaboration between UEs [103] and communication collaboration between RPNs [105]. The communication collaboration between UEs refers to the collaboration between different UEs to transmit the data collaboratively, and it also be called multi-hop collaboration [139]. Many studies on computation offloading assume that all UEs can connect directly to the RPN through the wireless network, which is referred to as single-hop computation offloading [103], [144].…”

Section: A Computation Offloading Processmentioning

confidence: 99%

Correlation-Based Device Energy-Efficient Dynamic Multi-Task Offloading for Mobile Edge Computing

Zhang

2021

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)

View full text Add to dashboard Cite

Computation task offloading is one of the enabling technologies for computation-intensive applications and edge intelligence, which experiences the explosive growth of massive data generated. Different techniques, wireless technologies and mechanisms have been proposed in the literature for task offloading in order to improve the services provided to the users. Although there is a rich literature of computation task offloading, the role of data in the scope of it has not received much attention yet. This motivates us to propose a survey which classified the state-of-the-art (SoTA) of computation task offloading from the view point of data. First, a thorough literature review is conducted to reveal the SoTA from various aspects with the consideration of task generation, i.e., architecture, objective, offloading strategy, task types, etc. It is found that types of task offloading is related to the data and will affect the offloading procedure, which contains resource allocation, task allocation etc. Then computation offloading is classified into two categories based on task types, namely static task based offloading and dynamic task based offloading. Finally, our views on future computation offloading are provided with the corresponding challenges and opportunities.

show abstract

Service placement in fog–cloud computing environments: a comprehensive literature review

Sarkohaki,

Sharifi

2024

J Supercomput

View full text Add to dashboard Cite

Joint Task Offloading and Resource Allocation for Fog-Based Intelligent Transportation Systems: A UAV-Enabled Multi-Hop Collaboration Paradigm

Cited by 14 publications

References 30 publications

Joint Task Offloading, Resource Allocation, and Trajectory Design for Multi-UAV Cooperative Edge Computing With Task Priority

Joint Task Offloading, Resource Allocation, and Trajectory Design for Multi-UAV Cooperative Edge Computing With Task Priority

Correlation-Based Device Energy-Efficient Dynamic Multi-Task Offloading for Mobile Edge Computing

Service placement in fog–cloud computing environments: a comprehensive literature review

Contact Info

Product

Resources

About