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

Haris, Muhammad; Shah, Munam Ali; Maple, Carsten

doi:10.1109/access.2023.3244886

Cited by 11 publications

(3 citation statements)

References 33 publications

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“…To intelligently meet the processing needs of vehicle applications, Haris et al [18] have suggested a VANETs architecture based on Intelligent Volunteer Computing. We provide criteria for choosing volunteers whose cars can complete the computationally demanding assignment.…”

Section: Related Workmentioning

confidence: 99%

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An Improved ARO Model for Task Offloading in Vehicular Cloud Computing in VANET

Thirumalraj,

2023

Preprint

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Vehicle-to-vehicle (V2V) communication enables a network of automobiles to perform collaborative computing, giving rise to the concept of a "vehicular cloud" (VC). However, without the need for edge nodes or cloud servers, vehicles can carry out applications needing the massive amount of processing cooperatively on their own by creating a Vehicular Ad-Hoc Network (VANET). Managing the recurrent topology alteration caused by vehicle mobility is a significant challenge for VANET cooperative computing. In this research, we present a V2V-based cooperative computing approach. The suggested method takes into account the distance between vehicles while choosing which ones to collaborate with, and it waits task offloading until the last possible moment to ensure a stable and energy-efficient cooperative computing environment. Despite its competitive performance when compared to other MH algorithms, the artificial rabbits optimisation (ARO) algorithm still suffers from poor accuracy and the issue of becoming trapped in solutions. By antagonism methods, this research creates selective opposition version of the artificial rabbit procedure (LARO), which eliminates the negative consequences of these shortcomings. To begin, during the random concealment phase, a Lévy flight strategy is implemented to increase population diversity and dynamics. The algorithm's convergence accuracy is enhanced by the richness of its various population samples. The tracking efficiency is improved, and ARO is kept from getting stuck in its existing local solutions by adopting the selective opposition technique. In comparison to traditional static scheduling techniques, the suggested strategy improves upon both energy efficiency and network reliability.

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Section: Related Workmentioning

confidence: 99%

“…Step4: Using Equations ( 19)-( 22), determine R's value. Apply the approach of detour foraging by solving Equation (18). Then, using Equation (31), determine the new rabbit position's adaption value and update it.…”

Section: Detailed Implementation Of Laromentioning

confidence: 99%

An Improved ARO Model for Task Offloading in Vehicular Cloud Computing in VANET

Thirumalraj,

2023

Preprint

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“…In [58], to tackle the problem of scarcity of computational resources, a selection criteria is proposed to select volunteers' vehicles capable of executing the computationally intensive task. For the volunteer vehicle identification, the authors used various machine learning based regression techniques including LR, SVR, KNN, DT, RF, GB, XGBoosting, AdaBoost, and ridge regression.…”

Section: Ai For Resource Allocation In Iov Networkmentioning

confidence: 99%

A Survey on Artificial Intelligence based Internet of Vehicles utilizing Unmanned Aerial Vehicles

Ali Shah,

Fernando,

Kashef

2023

Preprint

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The vehicular ad hoc network (VANET) when augmented with internet is called internet of vehicles (IoV). In the IoV, vehicles have on board units (OBUs), use communication technologies and customized software to communicate with infrastructure around them. The vehicles can have permanent connectivity and global network awareness based on cloud computing. IoV technologies are still being assessed to ensure that the network is protected from attacks, supports a dynamic vehicular mobility scenarios and meets the low latency communication requirements between the vehicles and the surrounding infrastructure. To meet these strict Quality of Service (QoS) demands, Unmanned Aerial Vehicles (UAVs) play a vital role in IoV ecosystems. An UAV can serve as a mobile roadside unit (mRSU) to transmit data between vehicles, stationery RSUs and other UAVs. The UAVs have better line of sight (LOS) connections which result in better channel states and lower path loss than fixed RSUs. Although the inclusion of UAVs in vehicular networks has improved the system performance by providing better resource management and routing solutions, the traditional optimization techniques with their limitations are often not applicable. Therefore, in recent years, research community is integrating Artificial Intelligence (AI) and Machine Learning (ML) into UAV based IoV ecosystem to better manage resource allocation, routing and mobility management issues to optimize the overall network performance. In this survey paper we review the existing research done in AI/ML based UAV-IoV networks and communications with focus on resource management and routing from year 2019 to 2023. We have studied different AI techniques, their training attributes and architectures in various research works. The limitations of AI-based approaches in terms of required computational resources, availability of real world data and AI models’ complexity for the UAV-IoV environment are also discussed. Finally, the future directions for UAV-IoV research opportunities that can leverage the full potential of AI are presented.

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Deep Learning for IoT “Artificial Intelligence of Things (AIoT)”

Mohamed

2023

Synthesis Lectures on Engineering, Science, and Technology

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

Cited by 11 publications

References 33 publications

An Improved ARO Model for Task Offloading in Vehicular Cloud Computing in VANET

An Improved ARO Model for Task Offloading in Vehicular Cloud Computing in VANET

A Survey on Artificial Intelligence based Internet of Vehicles utilizing Unmanned Aerial Vehicles

Deep Learning for IoT “Artificial Intelligence of Things (AIoT)”

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