Joint Offloading and IEEE 802.11p-based Contention Control in Vehicular Edge Computing

Nguyen, VanDung; Khanh, Tran Trong; Tran, Nguyen H.; Huh, Eui‐Nam; Hong, Choong Seon

doi:10.1109/lwc.2020.2978482

Cited by 13 publications

(9 citation statements)

References 15 publications

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“…Some state-of-art models are included for performance comparison, including Long Short-Term Memory (LSTM) [30], CNN, Naive Bayes [31], AlexNet, and Multi-Layer Perceptron (MLP) [32]. In the performance analysis, the proposed algorithm is compared with the model algorithm proposed by other scholars from the data transmission delay [33], throughput [34], average transmission power [35], and data transmission accuracy [36]. The modeling tools are summarized in Table I:…”

Section: Simulation Analysismentioning

confidence: 99%

Digital Twins in Unmanned Aerial Vehicles for Rapid Medical Resource Delivery in Epidemics

Chen

Feng

et al. 2022

IEEE Trans. Intell. Transport. Syst.

108

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The purposes are to explore the effect of Digital Twins (DTs) in Unmanned Aerial Vehicles (UAVs) on providing medical resources quickly and accurately during COVID-19 prevention and control. The feasibility of UAV DTs during COVID-19 prevention and control is analyzed. Deep Learning (DL) algorithms are introduced. A UAV DTs information forecasting model is constructed based on improved AlexNet, whose performance is analyzed through simulation experiments. As end-users and task proportion increase, the proposed model can provide smaller transmission delays, lesser energy consumption in throughput demand, shorter task completion time, and higher resource utilization rate under reduced transmission power than other state-of-art models. Regarding forecasting accuracy, the proposed model can provide smaller errors and better accuracy in Signal-to-Noise Ratio (SNR), bit quantizer, number of pilots, pilot pollution coefficient, and number of different antennas. Specifically, its forecasting accuracy reaches 95.58% and forecasting velocity stabilizes at about 35 Frames-Per-Second (FPS). Hence, the proposed model has stronger robustness, making more accurate forecasts while minimizing the data transmission errors. The research results can reference the precise input of medical resources for COVID-19 prevention and control.

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Section: Simulation Analysismentioning

confidence: 99%

Digital Twins in Unmanned Aerial Vehicles for Rapid Medical Resource Delivery in Epidemics

Chen

Feng

et al. 2022

IEEE Trans. Intell. Transport. Syst.

108

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show abstract

“…However, this mechanism has considered offloading data as one piece either to another node or to cloud, while the required data availability in each node has been ignored. Nguyen et al [12] have suggested an offloading mechanism for computation-intensive applications either in Vehicle Edge Computing (VEC) or in Roadside Units (RSUs). While this work considers making offloading decision for extensive computational applications (e.g., autonomous driving and vehicular video stream), data access in each node has not been considered.…”

Section: Related Workmentioning

confidence: 99%

“…EC nodes receive the data generated by application layer and store them locally until data accessing pattern are required [16]. The decision of either to execute these tasks locally, offload it to another node or in the cloud can be made based on different factors such as task size, time, popularity, upload/download data, and resources availability in nodes [12], [15]. The cloud layer has unlimited computation resources and thus tasks can be offloaded through e.g., Base Stations (BSs) from the EC layers due to limited resources or failure in completing tasks.…”

Section: B Service Architecturementioning

confidence: 99%

Data-Driven Analytics Task Management at the Edge: A Fuzzy Reasoning Approach

Aladwani

Alghamdi

Kolomvatsos

et al. 2022

2022 9th International Conference on Future Internet of Things and Cloud (FiCloud)

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Dynamic data-driven applications such as tracking and surveillance have emerged in Internet of Things (IoT) environments. Such applications rely heavily on data generated by connected devices (e.g., sensors). Consequently, leveraging these data in building data-driven predictive analytics tasks improves the Quality of Service (QoS) and, as a result, Quality of Experience (QoE). Such data support various data-driven tasks such as regression and classification. Analytics tasks require data and resources to be executed at the edge since transferring them to the cloud negatively affects response times and QoS. However, the network edge is characterized by limited resources compared to the cloud, being the subject of constraints that are violated upon offloading data-driven tasks to improper edge nodes. We contribute with an analytics task management mechanism based on the context of the requested data, the task delay sensitivity and the VM utilization. We introduce a novel Fuzzy inference mechanism for determining whether data-driven tasks should be executed locally, offloaded to peer edge servers, or sent to cloud. We showcase how our fuzzy reasoning mechanism efficiently derives such decisions by calculating the offloading probability per task. The derived optimal actions are compared against benchmark models in Edge Computing (EC) environments.

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“…Du et al 18 . minimized the cost of vehicles, and Nguyen et al 19 improved vehicular service and reduced energy consumption by offloading their task to the VEC server. However, the offloading efficiency of binary offloading will be affected by the limited computation resources.…”

Section: Related Workmentioning

confidence: 99%

Stackelberg game‐based task offloading in vehicular edge computing networks

Liu

Tian

Deng

et al. 2021

Int J Communication

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Summary With the emergence of intelligent vehicles, how to satisfy the demands of the vehicles with computing‐intensive and delay‐sensitive tasks has become a challenging issue. Vehicular edge computing (VEC) is proposed as an advanced paradigm to improve the service of vehicles through offloading the task to the VEC servers. Nevertheless, VEC servers always have limited computation resources and do not satisfy the offloading requirements of vehicles. To this end, in this paper, we propose a more flexible offloading scheme by jointly considering the offloading strategies and the price strategy. In the proposed scheme, where the task can be dynamically divided into two parts parallel executed at the vehicles and VEC servers. A multi‐leader and multi‐follower Stackelberg game ‐based distributed algorithm is proposed to maximize the utilities of the vehicles and the VEC servers under the delay constraint. Finally, the game equilibrium is analyzed and achieved. Extensive experiments demonstrate that the proposed offloading scheme converges fast and always outperforms the existing schemes in terms of the vehicular utility under different network conditions. For instance, the proposed scheme achieves the utility improvement over 56.62% compared to the fixed selection strategy and achieves the utility improvement up to 161.0% compared to the complete offloading with fixed price strategy when the number of vehicles is 10. Additionally, the effects of key parameters such as the offloading strategies and, the price strategy, and the computation resource on the average utility of vehicles are also discussed and analyzed based on the simulation results.

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Joint Offloading and IEEE 802.11p-based Contention Control in Vehicular Edge Computing

Cited by 13 publications

References 15 publications

Digital Twins in Unmanned Aerial Vehicles for Rapid Medical Resource Delivery in Epidemics

Digital Twins in Unmanned Aerial Vehicles for Rapid Medical Resource Delivery in Epidemics

Data-Driven Analytics Task Management at the Edge: A Fuzzy Reasoning Approach

Stackelberg game‐based task offloading in vehicular edge computing networks

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