Quality of Experience-Aware Mobile Edge Caching through a Vehicular Cloud

Vigneri, Luigi; Spyropoulos, Thrasyvoulos; Barakat, Chadi

doi:10.1145/3127540.3127555

Cited by 21 publications

(9 citation statements)

References 17 publications

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“…CLAID is the content-addressable cloud environment's transmission model. This model is used to maintain the data transaction within the linked node on the cloud environment and unify all data transactions that link to the decentralized peer-to-peer cloud system [26]. Whenever the decentralized peer-to-peer cloud system receives a request from the clustering-based search optimization, the hidden Markov model first handles this request [27].…”

Section: Proposed System Architecturementioning

confidence: 99%

Cross-Layer Hidden Markov Analysis for Intrusion Detection

Venkatachalam¹,

Prabu²,

Kang³

et al. 2022

Computers, Materials &Amp; Continua

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Ad hoc mobile cloud computing networks are affected by various issues, like delay, energy consumption, flexibility, infrastructure, network lifetime, security, stability, data transition, and link accomplishment. Given the issues above, route failure is prevalent in ad hoc mobile cloud computing networks, which increases energy consumption and delay and reduces stability. These issues may affect several interconnected nodes in an ad hoc mobile cloud computing network. To address these weaknesses, which raise many concerns about privacy and security, this study formulated clustering-based storage and search optimization approaches using cross-layer analysis. The proposed approaches were formed by cross-layer analysis based on intrusion detection methods. First, the clustering process based on storage and search optimization was formulated for clustering and route maintenance in ad hoc mobile cloud computing networks. Moreover, delay, energy consumption, network lifetime, and link accomplishment are highly addressed by the proposed algorithm. The hidden Markov model is used to maintain the data transition and distributions in the network. Every data communication network, like ad hoc mobile cloud computing, faces security and confidentiality issues. However, the main security issues in this article are addressed using the storage and search optimization approach. Hence, the new algorithm developed helps detect intruders through intelligent cross layer analysis with the Markov model. The proposed model was simulated in Network Simulator 3, and the outcomes were compared with those of prevailing methods for evaluating parameters, like accuracy, end-to-end delay, energy consumption, network lifetime, packet delivery ratio, and throughput.

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Section: Proposed System Architecturementioning

confidence: 99%

Cross-Layer Hidden Markov Analysis for Intrusion Detection

Venkatachalam¹,

Prabu²,

Kang³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Su et al [ 84 ] analyzed the characteristics of the content request in the car according to the content access mode of the vehicle, the speed of the vehicle and the road traffic density, and then a cross-entropy-based edge caching scheme is proposed based on the request decisions, which can dynamically adapt to the requests of different vehicles. Considering the time-varying nature of content popularity prediction, Vigneri et al [ 85 ] distributed different retrieval deadlines to different types of content, which can maximize the amount of offloading and reduce the impact of intensive request processing on the quality of users’ experience.…”

Section: Delivery Processmentioning

confidence: 99%

A Comprehensive Review on Edge Caching from the Perspective of Total Process: Placement, Policy and Delivery

Fan

Wang

et al. 2021

Sensors

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With the explosive growth of smart devices and mobile applications, mobile core networks face the challenge of exponential growth in traffic and computing demand. Edge caching is one of the most promising solutions to the problem. The main purpose of edge caching is to place popular content that users need at the edge of the network, borrow free space to reduce user waiting time, and lighten the network load by reducing the amount of duplicate data. Due to the promising advantages of edge caching, there have been many efforts motivated by this topic. In this paper, we have done an extensive survey on the existing work from our own perspectives. Distinguished from the existing review articles, our work not only investigates the latest articles in this area, but more importantly, covers all the researches of the total process of edge caching from caching placement optimization, policy design, to the content delivery process. In particular, we discuss the benefits of caching placement optimization from the perspective of different stakeholders, detail the delivery process, and conduct in-depth discussions from the five phases, i.e., requested content analysis, user model analysis, content retrieval, delivery, and update. Finally, we put forward several challenges and potential future directions, and hope to bring some ideas for the follow-up researches in this area.

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“…The proposed model also addressed other services issues like resource management, security, privacy, and data aggregation. Vigneri et al proposed that the vehicles could be used as mobile caches to meet the increasing demand of Internet contents and reducing the overload on the cellular infrastructure in [32]. In the proposed scheme, users can connect to the nearby vehicle for the related contents temporarily and then the connection is shifted towards the cellular infrastructure after the expiry of the predefined timeline.…”

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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Quality of Experience-Aware Mobile Edge Caching through a Vehicular Cloud

Cited by 21 publications

References 17 publications

Cross-Layer Hidden Markov Analysis for Intrusion Detection

Cross-Layer Hidden Markov Analysis for Intrusion Detection

A Comprehensive Review on Edge Caching from the Perspective of Total Process: Placement, Policy and Delivery

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

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