Deep Learning Based Caching for Self-Driving Cars in Multi-Access Edge Computing

Ndikumana, Anselme; Tran, Nguyen H.; Kim, Do Hyeon; Kim, Ki Tae; Hong, Choong Seon

doi:10.1109/tits.2020.2976572

Cited by 110 publications

(61 citation statements)

References 37 publications

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“…Then, we discuss the proposed non-parametric algorithm to solve the presented problem description. In doing so, we consider a small network with one MBS and two RSUs, RSU X and RSU Y, which is usually a practical scenario for simulation [9].…”

Section: Proposed Method: Dcol For Proactive Content Cachingmentioning

confidence: 99%

“…That means, for a missed request of content x at RSU r, the content retrieval cost δ r k is the function of the content size z k and the available link capacity Ω r k between the RUS r and the MBS 2 , i.e., δ r k = z k /Ω r k . Mathematically, Ω r k can be defined as the standard Shannon rate function [3], [9] which is dependent on the available wireless resources, such as the transmit power and allocated bandwidth, and the channel gain, i.e., Ω r…”

Section: System Modelmentioning

confidence: 99%

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DCoL: Distributed Collaborative Learning for Proactive Content Caching at Edge Networks

2021

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Caching popular content at the network edge, such as roadside units (RSUs), is a promising solution that enhances the user's quality-of-experience (QoE) and reduces network traffic. In this regard, the most challenging issue is to correctly predict the future popularity of contents and effectively store them in the cache of edge nodes. Thus, in this paper, we propose a distributed proactive caching scheme at the edge to optimize the content retrieval cost and improve the QoE of the mobile users. This proactive content caching scheme, namely Distributed Collaborative Learning (DCoL), is a non-parametric content popularity prediction mechanism in a distributed setting. Next, we show the advantage of DCoL as two folds: (i) it leverages distributed content popularity information to develop local content caching strategy, and (ii) it exploits the regional database using the long short-term memory (LSTM)-based prediction model to capture the dependency between requested contents. Simulation results using real datasets demonstrate that our scheme yields 8.9% and 18% gains, respectively, in terms of the cache hit efficiency and content retrieval cost, compared with a competitive centralized baseline, and outperforms other traditional caching strategies.INDEX TERMS proactive content caching, mobile edge computing, distributed learning, collaborative filtering, neural network.

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Section: Proposed Method: Dcol For Proactive Content Cachingmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

DCoL: Distributed Collaborative Learning for Proactive Content Caching at Edge Networks

2021

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“…Hence cloud edge servers reduces the cost of transmission [2]. Such extended technology gets implemented to reduce the delay time in bus services by using deep learning technique [3].…”

Section: Scope and Objectivementioning

confidence: 99%

A Four-Layer Robust Storage in Cloud using Privacy Preserving Technique with Reliable Computational Intelligence in Fog-Edge

2020

KSII TIIS

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The proposed framework of Four Layer Robust Storage in Cloud (FLRSC) architecture involves host server, local host and edge devices in addition to Virtual Machine Monitoring (VMM). The goal is to protect the privacy of stored data at edge devices. The computational intelligence (CI) part of our algorithm distributes blocks of data to three different layers by partially encoded and forwarded for decoding to the next layer using hash and greed Solomon algorithms. VMM monitoring uses snapshot algorithm to detect intrusion. The proposed system is compared with Tiang Wang method to validate efficiency of data transfer with security. Hence, security is proven against the indexed efficiency. It is an important study to integrate communication between local host software and nearer edge devices through different channels by verifying snapshot using lamport mechanism to ensure integrity and security at software level thereby reducing the latency. It also provides thorough knowledge and understanding about data communication at software level with VMM. The performance evaluation and feasibility study of security in FLRSC against three-layered approach is proven over 2 32 blocks of data with 98% accuracy. Practical implications and contributions to the growing knowledge base are highlighted along with directions for further research.

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“…In the last few years, a lot of research work about content caching in VANETs has been carried out [ 8 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. Wang, X. takes advantage of vehicular cloud to reduce the content delivery cost and latency, where vehicular cloud members store and provide the content locally so that vehicles can rapidly retrieve the content from the nearest member [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, the delay of fetching content from base stations (BSs) is ignored and the channel is assumed ideal. In order to minimize downloading delay, Ndikumana, A. et al propose a deep learning-based caching, where caching decisions depend on passengers’ features by deploying the multi-access edge computing servers at RSUs [ 18 ]. However, high computational complexity of the proposed scheme will increase the running cost of RSUs and the channel fading is also ignored.…”

Section: Introductionmentioning

confidence: 99%

A Stackelberg Game-Based Caching Incentive Scheme for Roadside Units in VANETs

Wang¹,

Lin²,

Chen³

et al. 2020

Sensors

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As a key technology of intelligent transportation systems (ITS), vehicular ad hoc networks (VANETs) have been promising to provide safety and infotainment for drivers and passengers. To support different applications about traffic safety, traffic efficiency, autonomous driving and entertainment, it is important to investigate how to effectively deliver content in VANETs. Since it takes resources such as bandwidth and power for base stations (BSs) or roadside units (RSUs) to deliver content, the optimal pricing strategy for BSs and the optimal caching incentive scheme for RSUs need to be studied. In this paper, a framework of content delivery is proposed first, where each moving vehicle can obtain small-volume content files from either the nearest BS or the nearest RSU according to the competition among them. Then, the profit models for both BSs and RSUs are established based on stochastic geometry and point processes theory. Next, a caching incentive scheme for RSUs based on Stackelberg game is proposed, where both competition sides (i.e., BSs and RSUs) can maximize their own profits. Besides, a backward introduction method is introduced to solve the Stackelberg equilibrium. Finally, the simulation results demonstrate that BSs can obtain their own optimal pricing strategy for maximizing the profit as well as RSUs can obtain the optimal caching scheme with the maximum profit during the content delivery.

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Deep Learning Based Caching for Self-Driving Cars in Multi-Access Edge Computing

Cited by 110 publications

References 37 publications

DCoL: Distributed Collaborative Learning for Proactive Content Caching at Edge Networks

DCoL: Distributed Collaborative Learning for Proactive Content Caching at Edge Networks

A Four-Layer Robust Storage in Cloud using Privacy Preserving Technique with Reliable Computational Intelligence in Fog-Edge

A Stackelberg Game-Based Caching Incentive Scheme for Roadside Units in VANETs

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