Hybrid Task Coordination Using Multi-Hop Communication in Volunteer Computing-Based VANETs

Waheed, Abdul; Shah, Munam Ali; Khan, Abid; Maple, Carsten; Ullah, Ikram

doi:10.3390/s21082718

Cited by 15 publications

(11 citation statements)

References 45 publications

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“…Resource-sharing strategies based on collaboration such as volunteer computing are becoming more popular to decrease repetitive work, speed up computing, and encourage the concept of decentralized open-source computing. In [22], a hybrid volunteer computing model in VANETs by utilizing spare resources of vehicles. The minimization of latency and maximization of system utility is taken into consideration.…”

Section: Related Workmentioning

confidence: 99%

Internet of Intelligent Vehicles (IoIV): An Intelligent VANET Based Computing via Predictive Modeling

2023

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With the significant research and advancements in technologies, arose new applications such as autonomous driving and augmented/virtual reality. These applications required massive computational resources for the execution of various tasks. Utilizing vehicles resources in a distributed manner and collectively with the help of volunteer computing for various computational tasks is an emerging research area. The appropriate and intelligent decision in selecting a volunteer vehicle is crucial in this opportunistic network where information is exchanged between vehicles. In this paper, we propose Intelligent Volunteer Computing-based VANETs architecture to fulfill the computational requirements of vehicles applications intelligently. We propose selection criteria to select volunteers' vehicles capable of the execution of the computationally intensive task. In this study to rightly identify the volunteer vehicle for task execution, we use a machine learning approach that predicts the capability of certain vehicles in completing the task. Extensive experimentation is conducted for the prediction of the computing capability of optimal volunteer vehicles. We used nine different regression techniques on publicly available datasets. The results show these techniques can efficiently predict the capability of volunteers. By comparing the regression techniques, the results indicate that the ridge regression and support vector regression can significantly reduce the mean square error, relative absolute error, and root mean square errors. Simulations are conducted to compare the proposed scheme with existing one.

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Section: Related Workmentioning

confidence: 99%

Internet of Intelligent Vehicles (IoIV): An Intelligent VANET Based Computing via Predictive Modeling

2023

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“…• Fog computing in which clusters of vehicles are utilized and no other infrastructure is needed. [50]. In this cluster of vehicles a head vehicle is appointed which organizes communication with RSUs and other vehicles.…”

Section: Vehicular Cloud Computingmentioning

confidence: 99%

Resource Allocation Methods in Vanets:a Systematic Literature Review

Trombly

Alsmadi

2023

Preprint

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Autonomous vehicles take on a more prominent part of our every day life activities. As the number of vehicles grows so does the need for resources to ensure safe and consistent operations of these vehicles. Today's networks and power grid are already used heavily just supplying resources for our day to day lives and leisure. The continuous increase in demand for such resources drives the need for more advanced resource management tools and techniques and more precise resource allocation schemes. This paper is a systematic literature review on the current methods and research trends for resource allocation in vehicular networks. The purpose of this review is to understand current research trends and motivations. Our hope is to provide a comparative analysis of resource allocation solutions, research trends and also challenges in vehicular networks.

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“…These elements are essential for understanding the varied computing requirements. Applying filters to an image, for example, uses fewer CPU cycles than applying a face detection method to a video [56]. The MDs require resources from the edge cloud to fulfil computation duties, but the edge cloud must keep its available CPU cycles for processing the entire offloaded data below its calculation capacity.…”

Section: -Computation and Communicationmentioning

confidence: 99%

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

Baktayan¹,

Al-Baltah²

2022

Sustainable Engineering and Innovation

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The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network.

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Hybrid Task Coordination Using Multi-Hop Communication in Volunteer Computing-Based VANETs

Cited by 15 publications

References 45 publications

Internet of Intelligent Vehicles (IoIV): An Intelligent VANET Based Computing via Predictive Modeling

Internet of Intelligent Vehicles (IoIV): An Intelligent VANET Based Computing via Predictive Modeling

Resource Allocation Methods in Vanets:a Systematic Literature Review

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

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