Edge Caching Based on Deep Reinforcement Learning in Vehicular Networks

Choi, Yoonjeong; Lim, Yujin

doi:10.1109/ecice55674.2022.10042939

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…In ref. [176], authors investigate a buffering method with the objective of optimizing the data size received from MEC hosts located in RSUs rather than MBSs, taking into account the high mobility of cars and the overlapped coverage of RSUs. The main goal is to store the information as close as possible to cars.…”

Section: Video Streamingmentioning

confidence: 99%

A Comprehensive Survey Exploring the Multifaceted Interplay between Mobile Edge Computing and Vehicular Networks

Pashazadeh,

Nardini,

Stea

2023

Future Internet

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In recent years, the need for computation-intensive applications in mobile networks requiring more storage, powerful processors, and real-time responses has risen substantially. Vehicular networks play an important role in this ecosystem, as they must support multiple services, such as traffic monitoring or sharing of data involving different aspects of the vehicular traffic. Moreover, new resource-hungry applications have been envisaged, such as autonomous driving or in-cruise entertainment, hence making the demand for computation and storage resources one of the most important challenges in vehicular networks. In this context, Mobile Edge Computing (MEC) has become the key technology to handle these problems by providing cloud-like capabilities at the edge of mobile networks to support delay-sensitive and computation-intensive tasks. In the meantime, researchers have envisaged use of onboard vehicle resources to extend the computing capabilities of MEC systems. This paper presents a comprehensive review of the most recent works related to MEC-assisted vehicular networks, as well as vehicle-assisted MEC systems. We illustrate the MEC system architecture and discuss its deployment in vehicular environments, as well as the key technologies to realize this integration. After that, we review the recent literature by identifying three different areas, i.e.: (i) MEC providing additional resources to vehicles (e.g., for task offloading); (ii) MEC enabling innovative vehicular applications (e.g., platooning), and (iii) vehicular networks providing additional resources to MEC systems. Finally, we discuss open challenges and future research directions, addressing the possible interplays between MEC systems and vehicular networks.

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Section: Video Streamingmentioning

confidence: 99%

A Comprehensive Survey Exploring the Multifaceted Interplay between Mobile Edge Computing and Vehicular Networks

Pashazadeh,

Nardini,

Stea

2023

Future Internet

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“…The emergence of more compute-intensive mobile applications has put great pressure on current mobile network transmission [1]. Excessive latency and insufficient bandwidth are the problems that mobile edge computing (MEC), a novel computing paradigm, aims to address.…”

Section: Introductionmentioning

confidence: 99%

Joint Optimization of Task Caching and Computation Offloading for Multiuser Multitasking in Mobile Edge Computing

Zhu,

Jia,

Pang

et al. 2024

Electronics

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Mobile edge computing extends the capabilities of the cloud to the edge to meet the latency performance required by new types of applications. Task caching reduces network energy consumption by caching task applications and associated databases in advance on edge devices. However, determining an effective caching strategy is crucial since users generate numerous repetitive tasks, but edge devices and storage resources are limited. We aimed to address the problem of highly coupled decision variables in dynamic task caching and computational offloading for multiuser multitasking in mobile edge computing systems. This paper presents a joint computation and caching framework with the aim of minimizing delays and energy expenditure for mobile users and transforming the problem into a form of reinforcement learning. Based on this, an improved deep reinforcement learning algorithm, P-DDPG, is proposed to achieve efficient computation offloading and task caching decisions for mobile users. The algorithm integrates a deep and deterministic policy grading and a prioritized empirical replay mechanism to reduce system costs. The simulations show that the designed algorithm performs better in terms of task latencies and lower computing power consumption.

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Edge Caching Based on Deep Reinforcement Learning in Vehicular Networks

Cited by 2 publications

References 5 publications

A Comprehensive Survey Exploring the Multifaceted Interplay between Mobile Edge Computing and Vehicular Networks

A Comprehensive Survey Exploring the Multifaceted Interplay between Mobile Edge Computing and Vehicular Networks

Joint Optimization of Task Caching and Computation Offloading for Multiuser Multitasking in Mobile Edge Computing

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