Joint Optimization of Task Offloading and Resource Allocation Based on Differential Privacy in Vehicular Edge Computing

Wang, Shupeng; Li, Jun; Wu, Guangjun; Chen, Handi; Sun, Shihui

doi:10.1109/tcss.2021.3074949

Cited by 46 publications

(7 citation statements)

References 31 publications

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“…According to (6), if the number of VUs requesting services from the 𝑛th EN are less than L 𝑛 , each can have access to the single CPU core with capacity (𝑐 𝑅 𝑛 • 𝑓 𝑅 𝑛 ). In case the number of users becomes higher than L 𝑛 , multiple VUs share CPU core resources.…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

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A Markov Decision Process Solution for Energy-Saving Network Selection and Computation Offloading in Vehicular Networks

Shinde

Tarchi

2023

IEEE Trans. Veh. Technol.

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Vehicular Edge Computing (VEC) enables the integration of edge computing facilities in vehicular networks (VNs), allowing data-intensive and latency-critical applications and services to end-users. Though VEC brings several benefits in terms of reduced task computation time, energy consumption, backhaul link congestion, and data security risks, VEC servers are often resource-constrained. Therefore, the selection of proper edge nodes and the amount of data to be offloaded becomes important for having VEC process benefits. However, with the involvement of high mobility vehicles and dynamically changing vehicular environments, proper VEC node selection and data offloading can be challenging. In this work, we consider a joint network selection and computation offloading problem over a VEC environment for minimizing the overall latency and energy consumption during vehicular task processing, considering both user and infrastructure side energy-saving mechanisms. We have modeled the problem as a sequential decision-making problem and incorporated it in a Markov Decision Process (MDP). Numerous vehicular scenarios are considered based upon the users' positions, the states of the surrounding environment, and the available resources for creating a better environment model for the MDP analysis. We use a value iteration algorithm for finding an optimal policy of the MDPs over an uncertain vehicular environment. Simulation results show that the proposed approaches improve the network performance in terms of latency and consumed energy.

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Section: System Model and Problem Formulationmentioning

confidence: 99%

“…This approach, known as vehicular edge computing (VEC), has the potential to serve VUs with reduced transmission delays and energy requirements. The importance of VEC in the VN scenarios is highlighted by several works in the recent past, mainly for enabling latency-critical applications [5], [6].…”

mentioning

confidence: 99%

A Markov Decision Process Solution for Energy-Saving Network Selection and Computation Offloading in Vehicular Networks

Shinde

Tarchi

2023

IEEE Trans. Veh. Technol.

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“…While gathering and evaluating relevant sources, we noticed that not many papers came up to the topic of securing issues in VANETs or utilizing security mechanisms in VANET. [91] investigated secure actors by introducing a collaborative (Intrusion Detection Systems, (IDS) approach to monitoring traffic in VANETs, while [92] looked at securing base station data that is transmitted from vehicle nodes using a privacy ensuring minimal increase in latency. Since VANET applications tend to have a low latency threshold it makes security the communications difficult due to the added overhead to either encryption or validating the identities of the nodes.…”

Section: Securitymentioning

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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“…Several authentication schemes that have been developed are found to consume considerable processing time owing to the frequent round trip needed between the terminal of service provider and the consumer's domain. [15][16][17][18][19][20] Therefore, there is a need for secure and fast authentication policy.…”

Section: Authentication Delaymentioning

confidence: 99%

DS2AN: Deep stacked sparse autoencoder for secure and fast authentication in HetNets

Hemavathi

Akhila

Zubeda

et al. 2022

Security and Privacy

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With the development of wireless communication technologies, providing seamless communication and secured roaming across devices has gained momentum. With 5G building upon and working together with 4G, it promises up to 100 times faster connectivity than 4G, greater bandwidth and extremely low latency. Though this dramatically enhances user experience, it calls for densification of the network by deploying smaller cells. This solution significantly improves network coverage but results in frequent handoffs. With the adoption of newer network technologies, potential new threats are introduced which becomes quite challenging for the industry. For ensuring seamless mobility, an effective handoff mechanism should ensure a secure and fast authentication process. In this article, Deep Sparse Stacked Autoencoder Network (DS2AN), an unsupervised deep learning model has been used to achieve the said objective. In the proposed method, training is done based on number of hidden layers using back propagation algorithm. Once trained, the cost function is minimized in order to achieve fine tuning of DS2AN.Compared with the existing handoff schemes, DS2AN promises a better level of security and a reduction in time required for handoff. Also, DS2AN is used to identify the presence of malicious entities thereby increasing security in the network. The performance of DS2AN algorithm for authentication delay has been evaluated based on communication and computational cost, and the results shows that DS2AN performs better compared to existing techniques.

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Joint Optimization of Task Offloading and Resource Allocation Based on Differential Privacy in Vehicular Edge Computing

Cited by 46 publications

References 31 publications

A Markov Decision Process Solution for Energy-Saving Network Selection and Computation Offloading in Vehicular Networks

A Markov Decision Process Solution for Energy-Saving Network Selection and Computation Offloading in Vehicular Networks

Resource Allocation Methods in Vanets:a Systematic Literature Review

DS2AN: Deep stacked sparse autoencoder for secure and fast authentication in HetNets

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