A multi-aerial base station assisted joint computation offloading algorithm based on D3QN in edge VANETs

Chen, Geng; Zhou, Yonggang; Xu, Xianjie; Zeng, Qingtian; Zhang, Yudong

doi:10.1016/j.adhoc.2023.103098

Cited by 4 publications

(3 citation statements)

References 40 publications

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“…DDQN improves the performance by using two independent neural networks for selecting the action and evaluating the value of the action. (2) D3QN-based algorithm [41]. D3QN estimates the function of state values and advantage separately by introducing the Double Q-Learning mechanism while using the Dueling Network architecture.…”

Section: Experimental Parametersmentioning

confidence: 99%

Multi-Queue-Based Offloading Strategy for Deep Reinforcement Learning Tasks

Huang,

Xie,

Guo

2024

Electronics

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With the boom in mobile internet services, computationally intensive applications such as virtual and augmented reality have emerged. Mobile edge computing (MEC) technology allows mobile devices to offload heavy computational tasks to edge servers, which are located at the edge of the network. This technique is considered an effective approach to help reduce the burden on devices and enable efficient task offloading. This paper addresses a dynamic real-time task-offloading problem within a stochastic multi-user MEC network, focusing on the long-term stability of system energy consumption and energy budget constraints. To solve this problem, a task-offloading strategy with long-term constraints is proposed, optimized through the construction of multiple queues to maintain users’ long-term quality of experience (QoE). The problem is decoupled using Lyapunov theory into a single time-slot problem, modeled as a Markov decision process (MDP). A deep reinforcement learning (DRL)-based LMADDPG algorithm is introduced to solve the task-offloading decision. Finally, Experiments are conducted under the constraints of a limited MEC energy budget and the need to maintain the long-term energy stability of the system. The results from simulation experiments demonstrate that the algorithm outperforms other baseline algorithms in terms of task-offloading decisions.

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Section: Experimental Parametersmentioning

confidence: 99%

Multi-Queue-Based Offloading Strategy for Deep Reinforcement Learning Tasks

Huang,

Xie,

Guo

2024

Electronics

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“… Chen et al (2023) addressed the challenges of task offloading and resource scheduling in vehicular edge computing, proposing a Multi-Aerial Base Station Assisted Joint Computation Offload algorithm based on D3QN in Edge VANETs (MAJVD3). This algorithm utilized SDN Controllers to efficiently schedule resources and tackle issues such as latency, energy consumption, and QoS degradation.…”

Section: Heuristic Approaches For Task Schedulingmentioning

confidence: 99%

Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects

Alsadie

2024

PeerJ Computer Science

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Fog computing has emerged as a prospective paradigm to address the computational requirements of IoT applications, extending the capabilities of cloud computing to the network edge. Task scheduling is pivotal in enhancing energy efficiency, optimizing resource utilization and ensuring the timely execution of tasks within fog computing environments. This article presents a comprehensive review of the advancements in task scheduling methodologies for fog computing systems, covering priority-based, greedy heuristics, metaheuristics, learning-based, hybrid heuristics, and nature-inspired heuristic approaches. Through a systematic analysis of relevant literature, we highlight the strengths and limitations of each approach and identify key challenges facing fog computing task scheduling, including dynamic environments, heterogeneity, scalability, resource constraints, security concerns, and algorithm transparency. Furthermore, we propose future research directions to address these challenges, including the integration of machine learning techniques for real-time adaptation, leveraging federated learning for collaborative scheduling, developing resource-aware and energy-efficient algorithms, incorporating security-aware techniques, and advancing explainable AI methodologies. By addressing these challenges and pursuing these research directions, we aim to facilitate the development of more robust, adaptable, and efficient task-scheduling solutions for fog computing environments, ultimately fostering trust, security, and sustainability in fog computing systems and facilitating their widespread adoption across diverse applications and domains.

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“…Vehicle applications demand stringent QoS and unprecedented network capacity in internet technology. Vehicle applications have limited task-offloading schemes and flexibility issues [1]. The Base Station (BS) and Road-Side Units (RSUs) cooperate in taskoffloading problems while dynamically adapting to current network environments.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Modeling for Intelligent Software-Defined Vehicular Networks: A Survey

et al. 2023

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Digital twins and the Internet of Things (IoT) have gained significant research attention in recent years due to their potential advantages in various domains, and vehicular ad hoc networks (VANETs) are one such application. VANETs can provide a wide range of services for passengers and drivers, including safety, convenience, and information. The dynamic nature of these environments poses several challenges, including intermittent connectivity, quality of service (QoS), and heterogeneous applications. Combining intelligent technologies and software-defined networking (SDN) with VANETs (termed intelligent software-defined vehicular networks (iSDVNs)) meets these challenges. In this context, several types of research have been published, and we summarize their benefits and limitations. We also aim to survey stochastic modeling and performance analysis for iSDVNs and the uses of machine-learning algorithms through digital twin networks (DTNs), which are also part of iSDVNs. We first present a taxonomy of SDVN architectures based on their modes of operation. Next, we survey and classify the state-of-the-art iSDVN routing protocols, stochastic computations, and resource allocations. The evolution of SDN causes its complexity to increase, posing a significant challenge to efficient network management. Digital twins offer a promising solution to address these challenges. This paper explores the relationship between digital twins and SDN and also proposes a novel approach to improve network management in SDN environments by increasing digital twin capabilities. We analyze the pitfalls of these state-of-the-art iSDVN protocols and compare them using tables. Finally, we summarize several challenges faced by current iSDVNs and possible future directions to make iSDVNs autonomous.

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A multi-aerial base station assisted joint computation offloading algorithm based on D3QN in edge VANETs

Cited by 4 publications

References 40 publications

Multi-Queue-Based Offloading Strategy for Deep Reinforcement Learning Tasks

Multi-Queue-Based Offloading Strategy for Deep Reinforcement Learning Tasks

Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects

Stochastic Modeling for Intelligent Software-Defined Vehicular Networks: A Survey

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