Multi-Access Edge Computing-Based Vehicle-Vehicle-RSU Data Offloading Over the Multi-RSU-Overlapped Environment

Lin, Shih-Yang; Huang, Chung‐Ming; Wu, Tzu-Yu

doi:10.1109/ojits.2022.3142065

Cited by 21 publications

(5 citation statements)

References 23 publications

(37 reference statements)

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“…The study suggests that data transfer via proximal vehicles can be a viable solution in such scenarios, allowing for more efficient use of resources and improving overall performance. Lin et al [123] proposed a k-hop vehicle to roadside unit (VVR) data offloading path to offload the LTE cellular network's data traffic to the vehicular network. The SDNbased proposed approach employs the idea of time-extended prediction inside the MEC server controller to determine the best VVR data offloading path, which exists during the timeextended prediction period.…”

Section: Discussionmentioning

confidence: 99%

Vehicular Communication Network Enabled CAV Data Offloading: A Review

Ahmed

Mirza

Raza

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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The connected and autonomous vehicles (CAV) applications and services-based traffic make an extra burden on the already congested cellular networks. Offloading is envisioned as a promising solution to tackle cellular networks' traffic explosion problem. Notably, vehicular traffic offloading leveraging different vehicular communication network (VCN) modes is one of the potential techniques to address the data traffic problem in cellular networks. This paper surveys the state-of-the-art literature for vehicular data offloading under a communication perspective, i.e., vehicle to vehicle (V2V), vehicle to roadside infrastructure (V2I), and vehicle to everything (V2X). First, we pinpoint the significant classification of vehicular data/traffic offloading techniques, considering whether data is to download or upload. Next, for better intuition of each data offloading's category, we sub-classify the existing schemes based on their objectives. Then, the existing literature on vehicular data/traffic is elaborated, compared, and analyzed based on approaches, objectives, merits, demerits, etc. Finally, we highlight the open research challenges in this field and predict future research trends.

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Section: Discussionmentioning

confidence: 99%

Vehicular Communication Network Enabled CAV Data Offloading: A Review

Ahmed

Mirza

Raza

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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“…To solve the problem of communication delay and energy loss caused by the growth in IoV services, Ma [20] proposed that through a comprehensive analysis of the optoelectronic communication and computing model, the vehicle computing task should be encoded and transformed into a knapsack problem, where the genetic algorithm is used to solve the optimal resource allocation strategy. Lin et al [21] proposed a data offloading strategy called PKMR, which considers a predicted k-hop count limit and utilizes VVR paths for data offloading with neighboring Rsus. Sun et al [22] introduced the PVTO method, which offloads V2V tasks to MEC and optimizes the strategy using GA, SAW, and MCDM.…”

Section: Related Workmentioning

confidence: 99%

Traffic-Aware Optimization of Task Offloading and Content Caching in the Internet of Vehicles

Wang,

Qiao

et al. 2023

Applied Sciences

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Emerging in-vehicle applications seek to improve travel experiences, but the rising number of vehicles results in more computational tasks and redundant content requests, leading to resource waste. Efficient compute offloading and content caching strategies are crucial for the Internet of Vehicles (IoV) to optimize performance in time latency and energy consumption. This paper proposes a joint task offloading and content caching optimization method based on forecasting traffic streams, called TOCC. First, temporal and spatial correlations are extracted from the preprocessed dataset using the Forecasting Open Source Tool (FOST) and integrated to predict the traffic stream to obtain the number of tasks in the region at the next moment. To obtain a suitable joint optimization strategy for task offloading and content caching, the multi-objective problem of minimizing delay and energy consumption is decomposed into multiple single-objective problems using an improved Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) via the Tchebycheff weight aggregation method, and a set of Pareto-optimal solutions is obtained. Finally, the experimental results verify the effectiveness of the TOCC strategy. Compared with other methods, its latency is up to 29% higher and its energy consumption is up to 83% higher.

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“…Chen et al [ 24 ] focused on the allocation of computing resources and developed a task-offloading framework V2V to gain a shorter task execution time. Lin et al [ 25 ] proposed a predicted k-hop-limited multi-RSU-considered (PKMR) vehicle-to-vehicle-to-roadside unit (VVR) data offloading method inside a multi-access edge computing (MEC) server, which is able to consider the time-extended prediction mechanism to find the potential VVR paths and network conditions. However, the vehicle computation capacity is dynamic and the V2V link is unstable, which makes it difficult to choose a proper vehicle to offload.…”

Section: Related Workmentioning

confidence: 99%

QoS-Aware Joint Task Scheduling and Resource Allocation in Vehicular Edge Computing

Cao

Duan

et al. 2022

Sensors

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Vehicular edge computing (VEC) has emerged in the Internet of Vehicles (IoV) as a new paradigm that offloads computation tasks to Road Side Units (RSU), aiming to thereby reduce the processing delay and resource consumption of vehicles. Ideal computation offloading policies for VEC are expected to achieve both low latency and low energy consumption. Although existing works have made great contributions, they rarely consider the coordination of multiple RSUs and the individual Quality of Service (QoS) requirements of different applications, resulting in suboptimal offloading policies. In this paper we present FEVEC, a Fast and Energy-efficient VEC framework, with the objective of realizing an optimal offloading strategy that minimizes both delay and energy consumption. FEVEC coordinates multiple RSUs and considers the application-specific QoS requirements. We formalize the computation offloading problem as a multi-objective optimization problem by jointly optimizing offloading decisions and resource allocation, which is a mixed-integer nonlinear programming (MINLP) problem and NP-hard. We propose MOV, a Multi-Objective computing offloading method for VEC. First, vehicle prejudgment is proposed to meet the requirements of different applications by considering the maximum tolerance delay related to the current vehicle speed. Second, an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is adopted to obtain the Pareto-optimal solutions with low complexity. Finally, the optimal offloading strategy is selected for QoS maximization. Extensive evaluation results based on real and simulated vehicle trajectories verify that the average QoS value of MOV is improved by 20% compared with the state-of-the-art VEC mechanism.

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Multi-Access Edge Computing-Based Vehicle-Vehicle-RSU Data Offloading Over the Multi-RSU-Overlapped Environment

Cited by 21 publications

References 23 publications

Vehicular Communication Network Enabled CAV Data Offloading: A Review

Vehicular Communication Network Enabled CAV Data Offloading: A Review

Traffic-Aware Optimization of Task Offloading and Content Caching in the Internet of Vehicles

QoS-Aware Joint Task Scheduling and Resource Allocation in Vehicular Edge Computing

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