Throughput-Optimal Dynamic Broadcast for SINR-Based Multi-Hop Wireless Networks With Time-Varying Topology

Tian, Xiang; Zhang, Baoxian; Li, Cheng

doi:10.1109/tvt.2021.3110085

Cited by 5 publications

(1 citation statement)

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“…We used a BS-UE (Base Station-User Equipment) method based on the maximum signal to interference plus noise ratio (SINR) for this association process. In this case, a vehicle will associate with a BS which provides the Max-SINR [34]. The Max-SINR ensures that the vehicle is associated with only one BS [35].…”

Section: A Overviewmentioning

confidence: 99%

Mobility and Deadline-Aware Task Scheduling Mechanism for Vehicular Edge Computing

Costa

Souza

Meneguette³

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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Vehicular Edge Computing (VEC) is a promising paradigm that provides cloud computing services closer to vehicular users. In VEC, vehicles and communication infrastructures can form pools with computational resources to meet vehicular services with low-latency constraints. These resource pools are known as Vehicular Cloud (VC). The usage of VC resources requires a task scheduling process. In this case, depending on its complexity, a vehicular service can be divided into different tasks. An efficient task scheduling needs to orchestrate where and for how long such tasks will run, considering the available pools, the mobility of nodes, and the tasks deadline constraints. Thus, this article proposes an efficient VC task scheduler based on an approximation heuristic and resources prediction to select the best VC for each task, called MARINA. MARINA aims to analyze the behavior of vehicles that share their computational resources with the VC and make scheduling decisions based on the mobility (VC availability) of these vehicles. Simulation results under a realistic scenario demonstrate the efficiency of MARINA compared to existing state-of-the-art mechanisms in terms of the number of tasks scheduled, monetary cost, system latency, and Central Processing Unit (CPU) utilization.

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Section: A Overviewmentioning

confidence: 99%