Journal Article10.1109/JIOT.2020.2982292
Reliable Computation Offloading for Edge-Computing-Enabled Software-Defined IoV
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TL;DR: Performance evaluation results validate that the proposed scheme is indeed capable of reducing the latency as well as improving the reliability of the EC-SDIoV.
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Abstract: Internet of Vehicles (IoV) has drawn great interest recent years. Various IoV applications have emerged for improving the safety, efficiency, and comfort on the road. Cloud computing constitutes a popular technique for supporting delay-tolerant entertainment applications. However, for advanced latency-sensitive applications (e.g., auto/assisted driving and emergency failure management), cloud computing may result in excessive delay. Edge computing, which extends computing and storage capabilities to the edge of the network, emerges as an attractive technology. Therefore, to support these computationally intensive and latency-sensitive applications in IoVs, in this article, we integrate mobile-edge computing nodes (i.e., mobile vehicles) and fixed edge computing nodes (i.e., fixed road infrastructures) to provide low-latency computing services cooperatively. For better exploiting these heterogeneous edge computing resources, the concept of software-defined networking (SDN) and edge-computing-aided IoV (EC-SDIoV) is conceived. Moreover, in a complex and dynamic IoV environment, the outage of both processing nodes and communication links becomes inevitable, which may have life-threatening consequences. In order to ensure the completion with high reliability of latency-sensitive IoV services, we introduce both partial computation offloading and reliable task allocation with the reprocessing mechanism to EC-SDIoV. Since the optimization problem is nonconvex and NP-hard, a heuristic algorithm, fault-tolerant particle swarm optimization algorithm is designed for maximizing the reliability (FPSO-MR) with latency constraints. Performance evaluation results validate that the proposed scheme is indeed capable of reducing the latency as well as improving the reliability of the EC-SDIoV.
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Citations
Resource Allocation in 5G IoV Architecture Based on SDN and Fog-Cloud Computing
TL;DR: A novel 5G IoV architecture is designed on the basis of fog-cloud computing and software-defined networking (SDN), and a many-objective optimization algorithm is proposed that outperforms the other state-of-the-art algorithms.
237
Edge Computing with Artificial Intelligence: A Machine Learning Perspective
TL;DR: The research results of using AI to optimize EC and applying AI to other fields under the EC architecture can serve as a guide to explore new research ideas in these two aspects while enjoying the mutually beneficial relationship between AI and EC.
Artificial Intelligence for Edge Service Optimization in Internet of Vehicles: A Survey
TL;DR: An exhaustive survey about utilizing AI in edge service optimization in IoV is conducted and a number of open issues in optimizing edge services with AI are discussed.
170
Priority-Aware Task Offloading in Vehicular Fog Computing Based on Deep Reinforcement Learning
TL;DR: A task offloading scheme is proposed in the context of VFC, where vehicles are incentivized to share their idle computing resource by dynamic pricing, which comprehensively considers the mobility of vehicles, the task priority, and the service availability of vehicles.
166
Deep Reinforcement Learning-Based Energy-Efficient Edge Computing for Internet of Vehicles
TL;DR: This article designs a joint computing and caching framework by integrating deep deterministic policy gradient (DDPG) algorithm for Internet of Vehicles scenario, which needs the support of mobile network provided by MNO.
111
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