Regional Intelligent Resource Allocation in Mobile Edge Computing Based Vehicular Network

Wang, Ge; Xu, Fangmin

doi:10.1109/access.2020.2964018

Cited by 34 publications

(17 citation statements)

References 22 publications

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“…Power/energy [28][29][30] QoS [33][34][35] Delay [25,26,28,[35][36][37][38][39] QoE and user satisfaction [34,37] Network/system utility [26,33,37,39,40] Reliability [20] However, at the same time, they are required to perform many intensive computations as part of the network [27]. Hence, authors in [28][29][30][31] try to optimize energy and power consumption in their resource allocation schemes.…”

Section: Table 2 Classification Of Framework According To Their Optimization Goals Optimization Goal Referencesmentioning

confidence: 99%

“…As in any network, revenue generation is directly dependent on the network users, and it is crucial to have a good QoS. In works [33][34][35] focus on maximizing their QoS in their resource allocation schemes for VEC networks.…”

Section: Qos Optimizationmentioning

confidence: 99%

“…In [35], the authors adopt a deep Q-learning based approach in their MEC-based IoV system for optimized RA while simultaneously maintaining QoS. In [34] the authors systematically look into the QoS guaranteed optimal RA problem for connected vehicular fleets in MEC environ-ments.…”

Section: Qos Optimizationmentioning

confidence: 99%

See 2 more Smart Citations

A survey on computation resource allocation in IoT enabled vehicular edge computing

Naren

Gaurav

Sahu

et al. 2021

Complex Intell. Syst.

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The number of vehicles is increasing at a very high rate throughout the globe. It reached 1 billion in 2010, in 2020 it was around 1.5 billion and experts say this could reach up to 2–2.5 billion by 2050. A large part of these vehicles will be electrically driven and connected to a vehicular network. Rapid advancements in vehicular technology and communications have led to the evolution of vehicular edge computing (VEC). Computation resource allocation is a vehicular network’s primary operations as vehicles have limited onboard computation. Different resource allocation schemes in VEC operate in different environments such as cloud computing, artificial intelligence, blockchain, software defined networks and require specific network performance characteristics for their operations to achieve maximum efficiency. At present, researchers have proposed numerous computation resource allocation schemes which optimize parameters such as power consumption, network stability, quality of service (QoS), etc. These schemes are based on widely used optimization and mathematical models such as the Markov process, Shannon’s law, etc. So, there is a need to present an organized overview of these schemes to help in the future research of the same. In this paper, we classify state-of-the-art computation resource allocation schemes based on three criteria: (1) Their optimization goal, (2) Mathematical models/algorithms used, and (3) Major technologies involved. We also identify and discuss current issues in computation resource allocation in VEC and mention the future research directions.

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Section: Table 2 Classification Of Framework According To Their Optimization Goals Optimization Goal Referencesmentioning

confidence: 99%

Section: Qos Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

A survey on computation resource allocation in IoT enabled vehicular edge computing

Naren

Gaurav

Sahu

et al. 2021

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…as being drunk [29]). As an example for these technologies, consider blockchain [30,31,32,33], Edge/Cloud computing [34,35,36] and Artificial Intelligence [37,38].…”

Section: Iov Overviewmentioning

confidence: 99%

Towards a secure ITS: Overview, challenges and solutions

Sleem

Noura

Couturier

2020

Journal of Information Security and Applications

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Intelligent Transportation Systems (ITS), the heart of the new revolution of smart transport, has evolved from the well-known Vehicular Ad-hoc Networks (VANETs) to become the Internet of Vehicles (IoV). In fact, the increase in the number of vehicles and the newly born technologies have stimulated the new Internet of Vehicles (IoV) or the Internet of Cars. In general, ITS aims at ensuring better traffic efficiency and reducing road accidents. However, due to different limitations and issues, these systems suffer from different security and privacy vulnerabilities. In fact, they are both vulnerable to various types of security and privacy attacks that may result in life-endangering situations. As a result, several solutions were presented to achieve the required levels of security and confidentiality. In this paper, an overview of ITS is presented stating the reasons behind the evolution from Vanet to IoV. Then, the main threats/attacks that threaten ITS are classified according to their (1) security impact and according to the (2) network layer(s) they affect. Solutions for each attack are also well presented. In addition, a security and performance evaluation and summary tables are presented to provide an overview of these surveyed solutions.

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“…[8] considered MEC in vehicular networks and maximized system utility for joint load balancing and offloading. In [9], a regional intelligent system for vehicular networks was presented using MEC. The authors considered different types of tasks and aimed at optimizing the resource allocation to minimize the delay.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Buffer-Aided Relay-Assisted MEC

Hajipour

2020

IEEE Commun. Lett.

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Mobile Edge Computing (MEC) is a promising paradigm to respond to the rising computation requests of the users in the emerging wireless networks, and especially in the Internet of Things (IoT). In this paper, we study bufferaided relay-assisted MEC in systems with discrete transmission time-line and block fading channels. We consider a hierarchical network composed of a source, a buffer-aided relay, and another node in a higher level in the hierarchy. The source sends its tasks to the relay which in turn randomly assigns the received tasks to its own computing server or to the server of the next node in the hierarchy. We provide a framework to take into account the delays in both the transmission and computation buffers which facilitates the derivation of the expression for the Average Response Time (ART) in the system. Based on that and the system average power consumption in each slot, we introduce the concept of Average Response Energy (ARE) as a novel metric to capture the energy efficiency in MEC. Accordingly, we propose two offloading schemes with relevant problem formulations, namely the Minimum ART (MART) and the Minimum ARE (MARE) schemes, to optimize the ART or the ARE while keeping the system queues stable. We analyze the properties of the formulated problems, in terms of the feasible sets and the objective functions and noting them, we propose effective solution methods. Using extensive simulations, we validate the presented analysis and show the effectiveness of the proposed schemes in comparison with various baseline methods.

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Regional Intelligent Resource Allocation in Mobile Edge Computing Based Vehicular Network

Cited by 34 publications

References 22 publications

A survey on computation resource allocation in IoT enabled vehicular edge computing

A survey on computation resource allocation in IoT enabled vehicular edge computing

Towards a secure ITS: Overview, challenges and solutions

Stochastic Buffer-Aided Relay-Assisted MEC

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