Adaptive-learning-based vehicle-to-vehicle opportunistic resource-sharing framework

Chopra, Arpita and Rahman, Anis Ur and Malik, Asad Waqar and Ravana, Sri Devi (2021) Adaptive-learning-based vehicle-to-vehicle opportunistic resource-sharing framework. IEEE Internet of Things Journal, 9 (14). pp. 12497-12504. ISSN 2327-4662, DOI

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With an ever-increasing number of connected devices on roads, it becomes unsustainable to provide nearby specialized execution resources (compute and storage) for servicing innovative applications. Moreover, the vehicular environment being inherently ad hoc and opportunistic, not to mention highly mobile, makes it unsuitable to use traditional cloud computing due to delayed and interrupted services. Thus, there is a possibility to introduce potential collaboration among nearby connected vehicles. However, the underlying decision model for the selection of the most suitable vehicle for task offloading is challenging in such a dynamic environment. In this study, we propose a collaborative vehicular computing framework that adopts online learning for efficient task assignment between local and neighboring computing resources. The underlying workload adaptive task offloading intends to balance out the workload across neighboring vehicles. The framework is compared against three techniques including two adaptive learning techniques in terms of service delay, efficiency, task delivery rate, task failures, and learning regret. The results demonstrate the effectiveness of the proposed resource-sharing network, improving service quality and throughput for servicing innovative intelligent transportation applications.

Item Type: Article
Funders: Fundamental Research Grant under Scheme (FRGS)[FP006-2020], Ministry of Education, Malaysia
Uncontrolled Keywords: Task analysis;Delays;Computational modeling;Adaptation models;Vehicular ad hoc networks;Internet of Things;Collaboration;Adaptive learning;Internet of Vehicles (IoV);Mobile computing;Task offloading;Vehicular network
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Ms Zaharah Ramly
Date Deposited: 18 Oct 2022 04:43
Last Modified: 18 Oct 2022 04:43

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