Optimal Resources Allocation from VANET Infrastructures in Dynamic Mobile Environments

Cumbal, Renato; Gutiérrez, Sebastián; Guerrero, Carlos; Hincapié, Roberto; Arévalo, Germán V.

doi:10.1109/latincom48065.2019.8937962

Cited by 6 publications

(6 citation statements)

References 11 publications

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“…The quality of the obtained solutions is evaluated using the fitness function. Since this work intends to minimize average waiting time (AWT), turnaround time (TAT), and RT, the fitness function is defined as in Equation (3). Estimate the fitness "F" of each EF in the BM-BFO as the subsequent assumption, F I G U R E 1 Proposed architecture.…”

Section: Feature Selection Using Bm-bfomentioning

confidence: 99%

A novel clustering and optimal resource scheduling in vehicular cloud networks using MKMA and the CM‐CSO algorithm

Kouser

Manikandan²

2023

Int J Communication

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Summary Various opportunities aimed at the employment of delay‐sensitive applications in the vehicular environment are presented by the vehicular cloud (VC). Contrary to diverse wireless networks, VC networks possess exceptional features among others, namely, shorter transmission time along with a higher dynamic topology. Although integration with the cloud offers higher storage along with computation capabilities, it as well entails restricted resource availability. The restrictions on the number of resources serve as a challenge in servicing the applications with their necessary quality of service (QoS) guarantees as the number of service requests for applications keeps on augmenting with diverse circumstances. Thus, the need for an effective scheduling methodology arises to decide the sequence of servicing application requests and successful utilization of a broadcast medium, along with data transmission. To do efficient resource scheduling on VC networks, an optimization algorithm, namely, the crossover and mutation (CM)‐centered chicken swarm optimization (CSO) is proposed and implemented with the help of a publicly available dataset. Initially, the VC infrastructure is initialized and some vehicle information is extracted as features. Next, the Brownian motion‐centered bacteria foraging optimization (BM‐BFO) algorithm chooses the essential features. Centered on the chosen features, the vehicles are clustered using the modified K‐means algorithm. Next, as for the cloud server's virtual machines (VMs), the resource information is extracted. Lastly, the CM‐CSO algorithm carries out the optimal scheduling in the VC by means of the clustered features of vehicles and features of the VM. The proposed techniques' findings are scrutinized and analogized to the other prevailing methodologies to confirm that the proposed work performs effectively and gives optimal resource allocation (RA) to the VC.

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Section: Feature Selection Using Bm-bfomentioning

confidence: 99%

A novel clustering and optimal resource scheduling in vehicular cloud networks using MKMA and the CM‐CSO algorithm

Kouser

Manikandan²

2023

Int J Communication

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“…There is also the context of the saturation of base stations as a cloudlet [56]. It is also important to ensure that the network is sustainable and maintainable for all connected nodes and able to service the requests of the area [57]. Theses units do suffer from decisions that have much larger ramifications on the network as a whole.…”

Section: Schedulingmentioning

confidence: 99%

Resource Allocation Methods in Vanets:a Systematic Literature Review

Trombly

Alsmadi

2023

Preprint

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Autonomous vehicles take on a more prominent part of our every day life activities. As the number of vehicles grows so does the need for resources to ensure safe and consistent operations of these vehicles. Today's networks and power grid are already used heavily just supplying resources for our day to day lives and leisure. The continuous increase in demand for such resources drives the need for more advanced resource management tools and techniques and more precise resource allocation schemes. This paper is a systematic literature review on the current methods and research trends for resource allocation in vehicular networks. The purpose of this review is to understand current research trends and motivations. Our hope is to provide a comparative analysis of resource allocation solutions, research trends and also challenges in vehicular networks.

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“…These impel the researchers to establish the vehicular ad-hoc network (VANET) (Sharma and Mohan, 2020), enabling the wireless interaction between the vehicles to restrain the number of accidents and traffic congestion. The exchange of the real data concerning the status of the roadway, traffic and accidents ensures safe driving and assures the safety of the roadway (Mohan, 2016; Cumbal et al , 2019). The VANET consolidates all the vehicles in the thruway with the metropolitan territories to authorize the wireless communication between the vehicles to the pavement way infrastructure.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the VANET empowers broadcast communication between vehicles within the networks (Cao et al , 2016). In such a way, the smart vehicle framework and VANET redesign and advance the traffic administration system, which assures the protected streets for both the pedestrians and the drivers (Cumbal et al , 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, medical care administration also benefitted through VANET as it guarantees the safest roadway in the occurrence of exigency (Cumbal et al , 2019; Tang et al , 2015). The administration of the VANET is reached out to a wide scope of activity such as mishap disturbing, versatility improvement, clog cautioning, rerouting and data downloading (Cao et al , 2016; Sampigethaya et al , 2007; Shen et al , 2013).…”

Section: Introductionmentioning

confidence: 99%

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Seek-and-destroy algorithm for optimal resource allocation and security analysis in software-defined vehicular networks

Raut¹,

Vishwamitra²

2021

IJPCC

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Purpose Software-define vehicular networks (SDVN) assure the direct programmability for controlling the vehicles with improved accuracy and flexibility. In this research, the resource allocation strategy is focused on which the seek-and-destroy algorithm is implemented in the controller in such a way that an effective allocation of the resources is done based on the multi-objective function. Design/methodology/approach The purpose of this study is focuses on the resource allocation algorithm for the SDVN with the security analysis to analyse the effect of the attacks in the network. The genuine nodes in the network are granted access to the communication in the network, for which the factors such as trust, throughput, delay and packet delivery ratio are used and the algorithm used is Seek-and-Destroy optimization. Moreover, the optimal resource allocation is done using the same optimization in such a way that the network lifetime is extended. Findings The security analysis is undergoing in the research using the simulation of the attackers such as selective forwarding attacks, replay attacks, Sybil attacks and wormhole attacks that reveal that the replay attacks and the Sybil attacks are dangerous attacks and in future, there is a requirement for the security model, which ensures the protection against these attacks such that the network lifetime is extended for a prolonged communication. The achievement of the proposed method in the absence of the attacks is 84.8513% for the remaining nodal energy, 95.0535% for packet delivery ratio (PDR), 279.258 ms for transmission delay and 28.9572 kbps for throughput. Originality/value The seek-and-destroy algorithm is one of the swarm intelligence-based optimization designed based on the characteristics of the scroungers and defenders, which is completely novel in the area of optimizations. The diversification and intensification of the algorithm are perfectly balanced, leading to good convergence rates.

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Optimal Resources Allocation from VANET Infrastructures in Dynamic Mobile Environments

Cited by 6 publications

References 11 publications

A novel clustering and optimal resource scheduling in vehicular cloud networks using MKMA and the CM‐CSO algorithm

A novel clustering and optimal resource scheduling in vehicular cloud networks using MKMA and the CM‐CSO algorithm

Resource Allocation Methods in Vanets:a Systematic Literature Review

Seek-and-destroy algorithm for optimal resource allocation and security analysis in software-defined vehicular networks

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