A relay-assisted parallel offloading strategy for multi-source tasks in internet of vehicles

Dun Cao; Ying Bao Zhang; Yi Fan Yang; Bao Feng Ji; Pradip Kumar Sharma; Osama Alfarraj; Amr Tolba; Jin Wang

doi:10.1016/j.seta.2024.103619

A relay-assisted parallel offloading strategy for multi-source tasks in internet of vehicles

Dun Cao, Ying Bao Zhang, Yi Fan Yang, Bao Feng Ji, Pradip Kumar Sharma, Osama Alfarraj, Amr Tolba, Jin Wang^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

Internet of Vehicles (IoV) is paving the road for the new generation of Intelligent Transportation Systems (ITS), and Mobile Edge Computing (MEC) is enabling IoV to efficiently handle the computation-intensive and time-sensitive tasks. However, this has introduced new challenges such as maximizing computing resources, allocating resources fairly for multi-source tasks concurrently, and dividing tasks for parallelly processing to minimize the latency. To face these challenges, a three-dimensional road vehicle mobility model is constructed, and the problem of offloading strategy and resource allocation among multiple vehicles served by one Road Side Unit (RSU) is investigates to minimize the average latency of multi-source tasks while satisfying the quality of service requirements. To address the Non-deterministic Polynomial-time hardness (NP-hardness) of the problem, we design a Relay-Assisted Parallel Offloading (RAPO) strategy to obtain the optimization solution. Extensive experimental results show that the RAPO strategy introducing relay-assisted nodes can enhance performance in poor scenarios and ensure low-latency multi-tasking under various conditions, especially reducing latency by 39% compared to local computing.

Original language	English
Article number	103619
Number of pages	8
Journal	Sustainable Energy Technologies and Assessments
Volume	62
Early online date	13 Jan 2024
DOIs	https://doi.org/10.1016/j.seta.2024.103619
Publication status	Published - Feb 2024

Bibliographical note

Funding Information:
This work was supported by the National Natural Science Foundation of China (Grant No. 62272063 , No. 61902041 and No. 62072056 ), the Natural Science Foundation of Hunan Province, China (No. 2022JJ30617 and No. 2020JJ2029 ), the Research Foundation of Education Bureau of Hunan Province, China (No. 23A0253 ), Hunan Provincial Key Research and Development Program (No. 2022GK2019 ), Standardization Project of Transportation Department of Hunan Province (No. B202108 ), Open Project of Xiangjiang Laboratory (No. 23XJ03003 ), the Researchers Supporting Project Number ( RSP2024R102 ), King Saud University, Riyadh, Saudi Arabia , and the Scientific Research Fund of Hunan Provincial Transportation Department (No. 202042 ).

Keywords

Computing offloading
Mobile edge computing
Mobility prediction
Relay assistance
Unequal splitting of tasks

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.seta.2024.103619Licence: Unspecified

Cite this

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title = "A relay-assisted parallel offloading strategy for multi-source tasks in internet of vehicles",

abstract = "Internet of Vehicles (IoV) is paving the road for the new generation of Intelligent Transportation Systems (ITS), and Mobile Edge Computing (MEC) is enabling IoV to efficiently handle the computation-intensive and time-sensitive tasks. However, this has introduced new challenges such as maximizing computing resources, allocating resources fairly for multi-source tasks concurrently, and dividing tasks for parallelly processing to minimize the latency. To face these challenges, a three-dimensional road vehicle mobility model is constructed, and the problem of offloading strategy and resource allocation among multiple vehicles served by one Road Side Unit (RSU) is investigates to minimize the average latency of multi-source tasks while satisfying the quality of service requirements. To address the Non-deterministic Polynomial-time hardness (NP-hardness) of the problem, we design a Relay-Assisted Parallel Offloading (RAPO) strategy to obtain the optimization solution. Extensive experimental results show that the RAPO strategy introducing relay-assisted nodes can enhance performance in poor scenarios and ensure low-latency multi-tasking under various conditions, especially reducing latency by 39% compared to local computing.",

keywords = "Computing offloading, Mobile edge computing, Mobility prediction, Relay assistance, Unequal splitting of tasks",

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note = "Funding Information: This work was supported by the National Natural Science Foundation of China (Grant No. 62272063 , No. 61902041 and No. 62072056 ), the Natural Science Foundation of Hunan Province, China (No. 2022JJ30617 and No. 2020JJ2029 ), the Research Foundation of Education Bureau of Hunan Province, China (No. 23A0253 ), Hunan Provincial Key Research and Development Program (No. 2022GK2019 ), Standardization Project of Transportation Department of Hunan Province (No. B202108 ), Open Project of Xiangjiang Laboratory (No. 23XJ03003 ), the Researchers Supporting Project Number ( RSP2024R102 ), King Saud University, Riyadh, Saudi Arabia , and the Scientific Research Fund of Hunan Provincial Transportation Department (No. 202042 ). ",

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AU - Alfarraj, Osama

AU - Tolba, Amr

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N2 - Internet of Vehicles (IoV) is paving the road for the new generation of Intelligent Transportation Systems (ITS), and Mobile Edge Computing (MEC) is enabling IoV to efficiently handle the computation-intensive and time-sensitive tasks. However, this has introduced new challenges such as maximizing computing resources, allocating resources fairly for multi-source tasks concurrently, and dividing tasks for parallelly processing to minimize the latency. To face these challenges, a three-dimensional road vehicle mobility model is constructed, and the problem of offloading strategy and resource allocation among multiple vehicles served by one Road Side Unit (RSU) is investigates to minimize the average latency of multi-source tasks while satisfying the quality of service requirements. To address the Non-deterministic Polynomial-time hardness (NP-hardness) of the problem, we design a Relay-Assisted Parallel Offloading (RAPO) strategy to obtain the optimization solution. Extensive experimental results show that the RAPO strategy introducing relay-assisted nodes can enhance performance in poor scenarios and ensure low-latency multi-tasking under various conditions, especially reducing latency by 39% compared to local computing.

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A relay-assisted parallel offloading strategy for multi-source tasks in internet of vehicles

Abstract

Bibliographical note

Keywords

UN SDGs

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