车载网络中对等云的可行性和可靠性:综合研究

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Xiaomei Zhang, Zack Stiltner
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引用次数: 0

摘要

现代车辆中嵌入的先进计算能力使其能够容纳各种智能交通系统和实际应用,从而有助于提高驾驶安全性和遵守道路法规。然而,其中一些应用对计算要求很高,而车辆的本地处理能力可能并不总是足以支持这些应用。为解决这一问题,现有研究建议将过多的工作量卸载到其他计算设施,如附近的基站、路边装置或远程云服务器。然而,这些设施仍有一些局限性,包括经常无法使用、拥堵和费用高昂。在本文中,我们将探索一种更普遍、更经济高效的解决方案:通过点对点连接将过多的工作负载卸载到附近的对等车辆上。这种方法被称为 "对等云-车辆",是对等云方法的延伸,在文献中已被提出用于移动社交网络。这项工作的目的是全面研究车对车卸载的可行性和可靠性。首先,我们分析了两个真实世界的车辆网络数据集,研究车辆接触的鲁棒性,并用基于深度学习的回归方法估计接触持续时间。其次,我们基于两个优化目标设计了可靠的车对车卸载方法:最小延迟任务卸载,以最小化整体执行延迟;成本感知任务卸载,以最小化任务卸载成本。基于真实世界数据集的实验结果表明,对等云-车辆的性能明显优于现有方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Feasibility and reliability of peercloud in vehicular networks: A comprehensive study

Advanced computing capabilities embedded in modern vehicles enable them to accommodate a variety of intelligent transportation systems and real-world applications that help improve driving safety and compliance with road regulations. However, some of these applications are computationally demanding, and the local processing capabilities of vehicles may not always be enough to support them. To address this issue, existing research has proposed offloading the excessive workload to other computing facilities, such as nearby base stations, roadside units, or remote cloud servers. Still, these facilities have several limitations, including frequent unavailability, congestion, and high fees. In this paper, we explore a more pervasive and cost-effective solution: offloading excessive workloads to nearby peer vehicles via peer-to-peer connections. This approach, referred to as peercloud-vehicle, is an extension of the peercloud approach, which has been proposed for mobile social networks in the literature. The objective of this work is to have a comprehensive study on the feasibility and reliability of vehicle-to-vehicle offloading. First, we analyze two real-world vehicular network datasets to study the robustness of the vehicle contacts and estimate contact durations with deep learning-based regression methods. Second, we design reliable vehicle-to-vehicle offloading approaches based on two optimization objectives: min-delay task offloading to minimize the overall execution delay, and cost-aware task offloading to minimize the cost of task offloading. Experimental results based on real-world datasets demonstrate that peercloud-vehicle significantly outperforms existing approaches.

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来源期刊
Pervasive and Mobile Computing
Pervasive and Mobile Computing COMPUTER SCIENCE, INFORMATION SYSTEMS-TELECOMMUNICATIONS
CiteScore
7.70
自引率
2.30%
发文量
80
审稿时长
68 days
期刊介绍: As envisioned by Mark Weiser as early as 1991, pervasive computing systems and services have truly become integral parts of our daily lives. Tremendous developments in a multitude of technologies ranging from personalized and embedded smart devices (e.g., smartphones, sensors, wearables, IoTs, etc.) to ubiquitous connectivity, via a variety of wireless mobile communications and cognitive networking infrastructures, to advanced computing techniques (including edge, fog and cloud) and user-friendly middleware services and platforms have significantly contributed to the unprecedented advances in pervasive and mobile computing. Cutting-edge applications and paradigms have evolved, such as cyber-physical systems and smart environments (e.g., smart city, smart energy, smart transportation, smart healthcare, etc.) that also involve human in the loop through social interactions and participatory and/or mobile crowd sensing, for example. The goal of pervasive computing systems is to improve human experience and quality of life, without explicit awareness of the underlying communications and computing technologies. The Pervasive and Mobile Computing Journal (PMC) is a high-impact, peer-reviewed technical journal that publishes high-quality scientific articles spanning theory and practice, and covering all aspects of pervasive and mobile computing and systems.
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