A survey on computation resource allocation in IoT enabled vehicular edge computing

Naren,; Gaurav, Abhishek; Sahu, Nishad; Dash, Abhinash Prasad; Chalapathi, G. S. S.; Chamola, Vinay

doi:10.1007/s40747-021-00483-x

Cited by 8 publications

(2 citation statements)

References 61 publications

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“…With the start of intelligent transportation and the development of the Internet of Things, the automotive industry has realized the connection between smart cars and the Internet, and gradually formed IoV [13]. In recent years, a lot of valuable research has been done on task offloading and resource allocation in IoV, and many researchers have used many underlying technologies to combine with IoV, such as Software Defined Networking (SDN), Blockchain or Artificial Intelligence (AI) [14]. Several studies have built a number of efficient resource allocation algorithms using fog computing techniques in IoV.…”

Section: Related Workmentioning

confidence: 99%

A Joint Task Offloading Method Based Edge-fog Computing in IoV

Sun

Meng

et al. 2023

Preprint

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Nowadays, with the gradual development of intelligent transportation and the widespread popularity of private cars, the Internet of Vehicles (IoV) technology is gradually coming to maturity. However, the development of smart cars has been accompanied by a concomitant increase in the amount of media as well as video games in the vehicle, and an explosion in the demand for computational resources. Since smart cars have limited computing resources of their own, they cannot store a large number of pending tasks in their own queues, so they cannot compute the large number of requests generated by the vehicles themselves. The lack of computing resources can be better solved with the help of edge servers, and the distribution of edge servers close to the user side of the road can also effectively achieve real-time for resource requests, but the high energy consumption generated during processing is also a challenge we must face. To address this challenge, a joint task offloading approach based on mobile edge computing and fog computing (EFTO) proposed in this paper. Technically, the location of the processing task is first obtained by getting the route of the computing task, thus finding the complete routing information of the task from the initial location to the target location. Then, a genetic algorithm is used to implement a multi-objective optimization problem to reduce the time and energy consumption during offloading and processing. Finally, the effectiveness of EFTO is demonstrated through comparative experiments, which shows a reduction in time consumption and an optimization of energy consumption compared to other offloading methods.

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Section: Related Workmentioning

confidence: 99%

A Joint Task Offloading Method Based Edge-fog Computing in IoV

Sun

Meng

et al. 2023

Preprint

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“…Different from [18], we reviewed different artificial intelligence approaches used to develop resource allocation models other than the approaches reviewed in [18]. The approaches include supervised learning, deep reinforcement learning, and unsupervised learning.…”

Section: Referencementioning

confidence: 99%

Deep Learning for Resource Management in Internet of Things Networks: A Bibliometric Analysis and Comprehensive Review

Olatinwo

Joubert

2022

IEEE Access

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In this study, we conducted a bibliometric analysis and comprehensive review of the studies published between the period of 2012 and 2022 on resource management in the internet of things (IoT) networks using the Scopus database to determine the current state of research and gain insight into the research challenges and opportunities in the field. The bibliometric analysis technique was employed to bibliometrically analyze the published studies that were collected from the Scopus database and this helped to discover the majority of research subjects in the field of resource management in IoT networks. Following this, we conducted a comprehensive review of the relevant studies to provide an insight into the recent progress and the research gaps in the field. According to the results of our bibliometric analysis and the comprehensive review, we discovered that resource management problems in IoT networks is still a growing challenge as a result of the limited available resources for operating IoT networks. Consequently, the resource management problem is a critical research area due to the advantages of IoT in terms of collecting vital data that could be used in analyzing and predicting human behavior as well as environmental conditions. Also, the results of our bibliometric analysis and comprehensive review further revealed that research on the use of artificial techniques, such as optimization approaches and game theory approaches, for resource management are common while research on the use of the modern artificial intelligence technique, like deep learning approaches, is less common. Therefore, this study aims to fill the research gap in the area of resource management in IoT networks by introducing the use of deep learning approaches. Deep learning is a powerful artificial intelligence method that is advantageous for obtaining low-complexity resource allocation solutions in a near real-time. Also, various open research issues that are associated with the use of deep learning approaches are highlighted as future research directions to enable the development of novel deep learning models for IoT networks.

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Distribution of resources beyond 5G networks with heterogeneous parallel processing and graph optimization algorithms

Khadidos,

Manoharan,

Selvarajan

et al. 2024

Cluster Comput

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In this paper, a design model for resource allocation is formulated beyond 5G networks for effective data allocations in each network nodes. In all networks, data is transmitted only after allocating all resources, and an unrestrained approach is established because the examination of resources is not carried out in the usual manner. However, if data transmission needs to occur, some essential resources can be added to the network. Moreover, these resources can be shared using a parallel optimization approach, as outlined in the projected model. Further the designed model is tested and verified with four case studies by using resource allocator toolbox with parallax where the resources for power and end users are limited within the ranges of 1.4% and 6%. Furthermore, in the other two case studies, which involve coefficient determination and blockage factors, the outcomes of the proposed approach fall within the marginal error constraint of approximately 31% and 87%, respectively.

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A survey on computation resource allocation in IoT enabled vehicular edge computing

Cited by 8 publications

References 61 publications

A Joint Task Offloading Method Based Edge-fog Computing in IoV

A Joint Task Offloading Method Based Edge-fog Computing in IoV

Deep Learning for Resource Management in Internet of Things Networks: A Bibliometric Analysis and Comprehensive Review

Distribution of resources beyond 5G networks with heterogeneous parallel processing and graph optimization algorithms

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