Architecture for Resource Allocation in the Internet of Vehicles for Cooperating Driving System

Kalsoom, Nafeesa; Ahmad, Ishfaq; Alroobaea, Roobaea; Raza, Muhammad Ahsan; Khalid, Samina; Ahmed, Zaheed; Ali, Ihsan

doi:10.1155/2021/6637568

Cited by 5 publications

(18 citation statements)

References 32 publications

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“…Effective work related to resource allocation for vehicular networks is depicted in [19]. These works are listed in [20]- [24]. In [20], kalsoom et al proposed an architecture based on fifth-generation network technology for the Cooperative Driving System (CDS) that includes a resource allocation scheme based on D2D technology.…”

Section: B Resource Allocationmentioning

confidence: 99%

“…These works are listed in [20]- [24]. In [20], kalsoom et al proposed an architecture based on fifth-generation network technology for the Cooperative Driving System (CDS) that includes a resource allocation scheme based on D2D technology. It improves the quality of service for CDS and hence road traffic efficiency.…”

Section: B Resource Allocationmentioning

confidence: 99%

“…Different from the existing works [10]- [16], [18], [20]- [24], our work considers cost optimization issue in cars with fairness constraints. The cost takes into account the transmission latency and local computing latency that is minimized by optimizing local devices operating frequencies, resource allocation, and association.…”

Section: B Resource Allocationmentioning

confidence: 99%

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Resource-Optimized Vehicular Edge Networks With Fairness Constraints

Rashid,

Khan,

Khan

et al. 2024

IEEE Access

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Intelligent transportation systems (ITSs) have witnessed a rising interest from researchers because of their promising features. These features include lane change assistance, infotainment, and collision avoidance, among others. To effectively operate ITSs for these functions, there is a need for edge computing. One can install edge computing servers at the roadside units (RSUs). There must be seamless communication between the edge servers and the cars. Additionally, there will be some cars that experience higher delays and thus, are not preferable because they will highly degrade the performance. Therefore, in this work, we consider a vehicular network scenario and define a cost function that takes into account the latency that is determined by the car's computing frequency, association, and resource allocation while considering fairness constraints. Our cost function is to minimize the total latency (i.e., both local computing latency and transmission latency). The cost of the optimization problem is minimized by optimizing the car's local frequency allocation, resource allocation, and association. The problem is separable, therefore, we first compute the local frequencies of the cars using a convex optimizer. Then, due to the NP-hard nature of the remaining formulated problem (i.e., joint association and resource allocation), we decompose the main problem into two sub-problems: (a) resource allocation and (b) association. Then, we propose an iterative solution. Finally, we provide numerical results for validating the proposed solution.

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Section: B Resource Allocationmentioning

confidence: 99%

Section: B Resource Allocationmentioning

confidence: 99%

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Resource-Optimized Vehicular Edge Networks With Fairness Constraints

Rashid,

Khan,

Khan

et al. 2024

IEEE Access

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“…It should be emphasized that the success of IoV mainly depends on secure and reliable connectivity and network setup. Due to the lack of a practical framework to develop IoV applications, many researchers from academia and industry try to develop the appropriate network architectures based on domain-specific and serviceoriented methods [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Edge AI-Assisted IoV Application for Aggressive Driver Monitoring: A Case Study on Public Transport Buses

SOY

2023

International Journal of Automotive Science and Technology

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With increasing adoption of digital technologies to automotive industry, the revo-lution of the vehicles opens new doors for many advanced applications to improve the driver safety and comfort. Thanks to Advanced Driver Assistance Systems (ADAS), no doubt that the future driving experience will be safer than today. De-spite the emergence of new trends, road accidents caused by aggressive driving are still a major problem in many countries. This study presents an edge AI-assisted ag-gressive driver monitoring system based on Internet of Vehicles (IoV) model. In the proposed system, the kNN algorithm and dynamic time warping method are used to recognize the signal patterns of aggressive drivers. The hardware platform is built on the RP2040 microcontroller-based Raspberry Pi Pico board and the Waveshare Quad Expander used for sensor extensions. The MPU-9250 9-axis motion tracking sensor is used as an inertial measurement unit (IMU) to identify the patterns of driv-ers who did sudden lane changes, heavy acceleration, and harsh braking on the roads. Besides, the required software is created using the MicroPython scripting language via Thonny IDE. The proposed method is tested on public transport vehi-cles to determine the drivers engaging in dangerous driving behavior for passengers. The obtained results show that the proposed method can provide satisfactory success to support for recognizing the aggressive behavior of drivers.

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“…On a deeper level, if the Internet of vehicles is used to realize driverless, the car is not only a vehicle, but also a mobile space. In addition, the Internet of vehicles can also realize vehicle to everything (V2X), where x can be an entity or nonentity [8,9]. However, with the rapid development of the Internet of vehicles, the data of the Internet of vehicles has also experienced a blowout growth in recent years [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Internet of Vehicles Resource Scheduling Based on Blockchain and Game Theory

Chen

2022

Mathematical Problems in Engineering

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With the popularity of on-board intelligent devices, the number of vehicle computing intensive applications is also increasing rapidly. Due to the high mobility and limited computing power of the vehicle, and the extensive and changing demand for computing resources of the vehicle terminal, the vehicle often has insufficient computing power. In order to meet the needs of intensive computing applications of vehicle terminals, the computing tasks of vehicles can be unloaded to edge cloud servers (ECSs) with rich resources and high performance to enhance the computing power of vehicle terminals. However, the resource charge of ECS is high, and the scheme needs to consider such issues as ECS signal coverage, long task transmission delay caused by network congestion, and insufficient computing capacity of ECS. Therefore, this study considers unloading the computing tasks of vehicles with insufficient computing resources to nearby vehicles with redundant computing capacity and proposes a resource scheduling method for the Internet of vehicles based on blockchain and game theory. The scheduling of computing resources can reduce the computing workload of buyers’ vehicles and improve the operation efficiency of the whole vehicle network.

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Architecture for Resource Allocation in the Internet of Vehicles for Cooperating Driving System

Cited by 5 publications

References 32 publications

Resource-Optimized Vehicular Edge Networks With Fairness Constraints

Resource-Optimized Vehicular Edge Networks With Fairness Constraints

Edge AI-Assisted IoV Application for Aggressive Driver Monitoring: A Case Study on Public Transport Buses

Internet of Vehicles Resource Scheduling Based on Blockchain and Game Theory

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