SERVitES: An efficient search and allocation resource protocol based on V2V communication for vehicular cloud

Meneguette, Rodolfo I.; Boukerche, Azzedine

doi:10.1016/j.comnet.2017.05.014

Cited by 32 publications

(30 citation statements)

References 17 publications

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“…The communication protocol is based on the SERVitES protocol [ 16 ], which creates a communication infrastructure only between vehicles, not considering the RSUs connection. Thus, we consider the FOG as the cluster head, reducing the number of messages transmitted on the network since all requests initially go to the FOG.…”

Section: Foresam—fog Paradigm-based Resource Allocation Mechanism For Vehicular Cloudsmentioning

confidence: 99%

“…One possible solution is sharing computational capacities, such as storage and processing, with nearby vehicles [ 16 ]. One possibility is the use of vehicular cloud, which consists of a set of cooperating vehicles and collaboration among themselves sharing their computational resources, which can be dynamically scheduled between vehicles or by roadside units (RSU) [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

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FORESAM—FOG Paradigm-Based Resource Allocation Mechanism for Vehicular Clouds

Pereira

Boukerche

Silva

et al. 2021

Sensors

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The Intelligent Transport Systems (ITS) has the objective quality of transportation improvement through transportation system monitoring and management and makes the trip more comfortable and safer for drivers and passengers. The mobile clouds can assist the ITS in handling the resource management problem. However, resource allocation management in an ITS is challenging due to vehicular network characteristics, such as high mobility and dynamic topology. With that in mind, we propose the FORESAM, a mechanism for resources management and allocation based on a set of FOGs which control vehicular cloud resources in the urban environment. The mechanism is based on a more accurate mathematical model (Multiple Attribute Decision), which aims to assist the allocation decision of resources set that meets the period requested service. The simulation results have shown that the proposed solution allows a higher number of services, reducing the number of locks of services with its accuracy. Furthermore, its resource allocation is more balanced the provided a smaller amount of discarded services.

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Section: Foresam—fog Paradigm-based Resource Allocation Mechanism For Vehicular Cloudsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

FORESAM—FOG Paradigm-Based Resource Allocation Mechanism for Vehicular Clouds

Pereira

Boukerche

Silva

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

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“…The proposed scheme also maintains a cache to help the vehicles during searching of required resources. Furthermore, to facilitate the vehicles in searching and managing the required resources, the same authors extended their work in [21] and introduced a more efficient resource allocation and research protocol called SERVitES. In the proposed scheme, vehicles share information by organizing themselves into clusters without the help of the RSU.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Priority-Based Efficient Resource Allocation and Computing Framework for Vehicular Multimedia Cloud Computing

et al. 2020

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In intelligent transportation system, smart vehicles are equipped with a variety of sensing devices those offer various multimedia applications and services related to smart driving assistance, weather forecasting, traffic congestion information, road safety alarms, and many entertainment and comfort-related applications. These smart vehicles produce a massive amount of multimedia related data that required fast and real-time processing which cannot be fully handled by the standalone onboard computing devices due to their limited computational power and storage capacities. Therefore, handling such multimedia applications and services demanded changes in the underlaying networking and computing models. Recently, the integration of vehicles with cloud computing is emerged as a challenging computing paradigm. However, there are certain challenges related to multimedia contents processing, (i.e., resource cost, fast service response time, and quality of experience) that severely affect the performance of vehicular communication. Thus, in this paper, we propose an efficient resource allocation and computation framework for vehicular multimedia cloud computing to overcome the aforementioned challenges. The performance of the proposed scheme is evaluated in terms of quality of experience, service response time, and resource cost by using the Cloudsim simulator. INDEX TERMS Intelligent transportation system, multimedia cloud computing, vehicular networks, quality of experience, multimedia contents processing, response time, smart vehicle.

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“…Recent works have promoted such Clouds by tackling diverse aspects, such as mobility, heterogeneity, and scheduling. Cluster-based Vehicular Cloud creation and coordination in highly dynamic urban scenarios aim at following platooning-inspired strategies [22,21]. Based on this approach, the MDP-based modeling of vehicle mobility attempted to define enhanced resource allocation in Vehicular Clouds [23].…”

Section: Connection and Communication Stabilitymentioning

confidence: 99%

MDP-based Vehicular Network Connectivity Model for VCC Management

Saad

Grande

2020

2020 IEEE/ACM 24th International Symposium on Distributed Simulation and Real Time Applications (DS-RT)

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Vehicular Cloud computing is a new paradigm in which vehicles collaboratively exchange data and resources to support services and problem-solving in urban environments. Characteristically, such Clouds undergo severe challenging conditions from the high mobility of vehicles, and by essence, they are rather dynamic and complex.Many works have explored the assembling and management of Vehicular Clouds with designs that heavily focus on mobility. However, a mobility-based strategy relies on vehicles' geographical position, and its feasibility has been questioned in some recent works. Therefore, we present a more relaxed Vehicular Cloud management scheme that relies on connectivity. This work models uncertainty and considers every possible chance a vehicle may be available through accessible communication means, such as vehicle-to-everything (V2X) communications and the vehicle being in the range of road-side units (RSUs) for data transmissions. We propose an markov-decisision process (MDP) model to track vehicles' connection status and estimate their reliability for data transmissions. Also, from analyses, we observed that higher vehicle connectivity presents a trace of repeated connection patterns. We reinforce the connectivity status by validating it through an availability model to distinguish the vehicles which support high availability regardless of their positioning. The availability model thus determines the suitability of the MDP model in a given environment. I like to extend my gigantic thanks to Professor Robson for guidance, teaching, and being available most of the time to answer my stupid questions. Thiago, for helping understand the software we use for this research. My parents for their supports. My friends and colleagues for making this journey bearable. Thank you

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SERVitES: An efficient search and allocation resource protocol based on V2V communication for vehicular cloud

Cited by 32 publications

References 17 publications

FORESAM—FOG Paradigm-Based Resource Allocation Mechanism for Vehicular Clouds

FORESAM—FOG Paradigm-Based Resource Allocation Mechanism for Vehicular Clouds

Dynamic Priority-Based Efficient Resource Allocation and Computing Framework for Vehicular Multimedia Cloud Computing

MDP-based Vehicular Network Connectivity Model for VCC Management

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