Soft-defined heterogeneous vehicular network: architecture and challenges

Zheng, Kan; Hou, Lu; Meng, Hanlin; Zheng, Qiang; Liu, Ning; Lei, Lei

doi:10.1109/mnet.2016.7513867

Cited by 148 publications

(56 citation statements)

References 12 publications

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“…2) Vehicular Networks: The concept of vehicular networking brings a new level of connectivity to vehicles, and has become a key driver of ITS. The control functionalities in vehicular network can be divided into three parts according to their usages, including communication control, computing control and storage control [78]. In vehicular networks, problems such as resource allocation, caching, and networking, can be formulated and solved via DRL.…”

Section: B Aiot Perception Layer -Smart Vehiclesmentioning

confidence: 99%

Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges

Lei

Tan

Zheng

et al. 2020

IEEE Commun. Surv. Tutorials

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226

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The Internet of Things (IoT) extends the Internet connectivity into billions of IoT devices around the world, which collect and share information to reflect the status of physical world. The Autonomous Control System (ACS), on the other hand, performs control functions on the physical systems without external intervention over an extended period of time. The integration of IoT and ACS results in a new concept -autonomous IoT (AIoT). The sensors collect information on the system status, based on which intelligent agents in IoT devices as well as Edge/Fog/Cloud servers make control decisions for the actuators to react. In order to achieve autonomy, a promising method is for the intelligent agents to leverage the techniques in the field of artificial intelligence, especially reinforcement learning (RL) and deep reinforcement learning (DRL) for decision making. In this paper, we first provide comprehensive survey of the state-of-art research, and then propose a general model for the applications of RL/DRL in AIoT. Finally, the challenges and open issues for future research are identified.

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Section: B Aiot Perception Layer -Smart Vehiclesmentioning

confidence: 99%

Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges

Lei

Tan

Zheng

et al. 2020

IEEE Commun. Surv. Tutorials

Self Cite

226

View full text Add to dashboard Cite

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“…To make them available and programmable for control layer, we develop a VNF pool that integrates all individual VNFs distributed in each network domain. 8 Hence, control layer can choose a set of VNFs in different network domains to compose a network slice as well as allocates network resources to support the operation of network slice. 2) Intelligent control layer: Control layer is responsible for deploying and managing of network slices.…”

Section: B Design Of Intelligent Network Slicing Architecturementioning

confidence: 99%

Intelligent Network Slicing for V2X Services Toward 5G

2019

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Benefiting from the widely deployed LTE infrastructures, the fifth generation (5G) wireless networks has been becoming a critical enabler for the emerging vehicle-to-everything (V2X) communications.However, existing LTE networks cannot efficiently support stringent but dynamic requirements of V2X services. One effective solution to overcome this challenge is network slicing, whereby different services could be supported by logically separated networks. To mitigate the increasing complexity of network slicing in 5G, we propose to leverage the recent advancement of Machine Learning (ML) technologies for automated network operation. Specifically, we propose intelligent network slicing architecture for V2X services, where network functions and multi-dimensional network resources are virtualized and assigned to different network slices. In achieving optimized slicing intelligently, several critical techniques, including mobile data collection and ML algorithm design, are discussed to tackle the related challenges.Then, we develop a simulation platform to illustrate the effectiveness of our proposed intelligent network slicing. With the integration of 5G network slicing and ML-enabled technologies, the QoS of V2X services is expected to be dramatically enhanced. Index TermsV2X services, network slicing, artificial intelligence, deep reinforcement learning Jie Mei is with the Intelligent Computing and Communication (IC 2 ) Lab, Wireless Signal Processing and Network

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“…and reliable [72]. Data preprocessing can be divided into three steps which are data cleaning, generation of implicit ratings, and data integration [46].…”

Section: Raw Datamentioning

confidence: 99%

A Survey on Machine Learning‐Based Mobile Big Data Analysis: Challenges and Applications

Xie

Song

et al. 2018

Wireless Communications and Mobile Computing

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This paper attempts to identify the requirement and the development of machine learning-based mobile big data (MBD) analysis through discussing the insights of challenges in the mobile big data. Furthermore, it reviews the state-of-the-art applications of data analysis in the area of MBD. Firstly, we introduce the development of MBD. Secondly, the frequently applied data analysis methods are reviewed. Three typical applications of MBD analysis, namely, wireless channel modeling, human online and offline behavior analysis, and speech recognition in the Internet of Vehicles, are introduced, respectively. Finally, we summarize the main challenges and future development directions of mobile big data analysis.

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Soft-defined heterogeneous vehicular network: architecture and challenges

Cited by 148 publications

References 12 publications

Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges

Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges

Intelligent Network Slicing for V2X Services Toward 5G

A Survey on Machine Learning‐Based Mobile Big Data Analysis: Challenges and Applications

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