A collective filtering based content transmission scheme in edge of vehicles

Wang, Xiaojie; Feng, Yufan; Ning, Zhaolong; Hu, Xiping; Kong, Xiangjie; Hu, Bin; Guo, Yi

doi:10.1016/j.ins.2019.07.083

Cited by 11 publications

(10 citation statements)

References 23 publications

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“…Traditional data sources are typically transferred remotely to the cloud center, and services that are based on the mobile cloud cannot guarantee the satisfaction of low-latency requirements for content transfer [52]. Therefore, mobile edge computing has the potential to overcome this challenge.…”

Section: A Architecturementioning

confidence: 99%

“…Since edge servers are distributed in the surrounding area, fog computing is expected to improve the data transmission efficiency. Reference [52] proposed a vehicle edge collaborative filtering content transmission scheme that is based on fog calculation. A collective filtering algorithm and a two-dimensional Markov chain are used to combine positional awareness, content caching, and decentralized computing for content precaching at the edge of the vehicle network.…”

Section: Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence-Empowered Edge of Vehicles: Architecture, Enabling Technologies, and Applications

Alfarraj

Tolba

2020

IEEE Access

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With the proliferation of mobile devices and a wealth of rich application services, the Internet of vehicles (IoV) has struggled to handle computationally intensive and delay-sensitive computing tasks. To substantially reduce the latency and the energy consumption, application work is offloaded from a mobile device to a remote cloud or a nearby mobile edge cloud for processing. Compared with remote clouds, mobile edge clouds are located at the edge of the network. Therefore, mobile edge computing (MEC) has the advantages of effectively utilizing idle computing and storage resources at the edge of the network and reducing the network transmission delay. In addition, mobile devices are increasingly moving toward intelligence. To satisfy the service experience and service quality requirements of mobile users, the vehicle Internet is transforming into the intelligent vehicle Internet. Artificial intelligence (AI) technology can adapt to rapidly changing dynamic environments to provide multiple task requirements for resource allocation, computational task scheduling, and vehicle trajectory prediction. On this basis, combined with MEC technology and AI technology, computing and storage resources are placed on the edge of the network to provide real-time data processing while providing more efficient and intelligent services. This article introduces IoV from three aspects, namely, MEC, AI and the advantages of combining the two, and analyzes the corresponding architecture and implementation technology. The application of MEC and AI in IoV is analyzed and compared with current approaches. Finally, several promising future directions in the field of IoV are discussed. INDEX TERMS Internet of Vehicles (IoV), mobile edge computing (MEC), artificial intelligence (AI).

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Section: A Architecturementioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

Artificial Intelligence-Empowered Edge of Vehicles: Architecture, Enabling Technologies, and Applications

Alfarraj

Tolba

2020

IEEE Access

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“…After making several improvements to these shortcomings, it was found that the security and various performances of the protocol were greatly improved [15]. Wang et al (2020) introduced the agent to the offloading of computing tasks, proposed a new drone-assisted mobile edge computing (MEC) framework, and used the intelligence and perception of the agent to build a system model. The simulation showed that introducing the agent could significantly reduce the delay and energy consumption.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The simulation showed that introducing the agent could significantly reduce the delay and energy consumption. Also, the effectiveness of the agent was explained [16].…”

Section: Literature Reviewmentioning

confidence: 99%

A freight integer linear programming model under fog computing and its application in the optimization of vehicle networking deployment

Wang¹,

Qiu²

2020

PLoS ONE

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The increase in data amount makes the traditional Internet of Vehicles (IoV) fail to meet users' needs. Hence, the IoV is explored in series. To study the construction of freight integer linear programming (ILP) model based on fog computing (FG), and to analyze the application of the model in the optimization of the networking deployment (ND) of the IoV. FG and ILP are combined to build a freight computing ILP model. The model is used to analyze the application of ND optimization in the IoV system through simulations. The results show that while analyzing the ND results in different scenarios, the model is more suitable for small-scale scenarios and can optimize the objective function; however, its utilization rate is low in large-scale scenarios. While comparing and analyzing the network cost and running time, compared with traditional cloud computing solutions, the ND solution based on FG requires less cost, shorter running time, and has apparent effectiveness and efficiency. Therefore, it is found that the FG-based model has low cost, short running time, and apparent efficiency, which provides an experimental basis for the application of the later deployment of freight vehicles (FVs) in the Internet of Things (IoT) system for ND optimization. The results will provide important theoretical support for the overall deployment of IoV.

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“…Therefore, it would be a much more feasible solution to deploy certain parts of a deep learning model to the IoT edge device in an effort to assist the cloud in the entire continuous learning process instead of relying completely on the cloud [19]. When a given deep learning model is trained in a distributed manner between two or more devices, this will present certain challenges such as deciding how many and which specific layers of a model must be run on the edge and the cloud (this is known as offloading [20][21][22][23]), dealing with the transmission load between the IoT edge device and the cloud [24] and evaluating whether it is important for the IoT edge device to transmit all of the data to the cloud for model training or whether some of the data be discarded [25]. These challenges have not yet been addressed in a distributed incremental learning scenario which is what this paper attempts to do.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach of IoT Stream Sampling and Model Update on the IoT Edge Device for Class Incremental Learning in an Edge-Cloud System

2021

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With the exponential rise of the number of IoT devices, the amount of data being produced is massive. Thus, it is unfeasible to send all the raw data directly to the cloud for processing, especially for data that is high dimensional. Training deep learning models incrementally evolves the model over time and eliminates the need to statically training the models with all the data. However, the integration of class incremental learning and the Internet of Things (IoT) is a new concept and is not yet mature. In the context of IoT and deep learning, the transmission cost of data in the edge-cloud architecture is a challenge. We demonstrate a novel sample selection method that discards certain training images on the IoT edge device that reduces transmission cost and still maintains class incremental learning performance. It can be unfeasible to transmit all parameters of a trained model back to the IoT edge device. Therefore, we propose an algorithm to find only the useful parameters of a trained model in an efficient way to reduce the transmission cost from the cloud to the edge devices. Results show that our proposed methods can effectively perform class-incremental learning in an edge-cloud setting.

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A collective filtering based content transmission scheme in edge of vehicles

Cited by 11 publications

References 23 publications

Artificial Intelligence-Empowered Edge of Vehicles: Architecture, Enabling Technologies, and Applications

Artificial Intelligence-Empowered Edge of Vehicles: Architecture, Enabling Technologies, and Applications

A freight integer linear programming model under fog computing and its application in the optimization of vehicle networking deployment

A Novel Approach of IoT Stream Sampling and Model Update on the IoT Edge Device for Class Incremental Learning in an Edge-Cloud System

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