Artificial intelligence for edge service optimization in Internet of Vehicles: A survey

Xu, Xiaolong; Li, Haoyuan; Xu, Weijie; Liu, Zhong‐Jian; Yao, Liang; Dai, Fei

doi:10.26599/tst.2020.9010025

Cited by 139 publications

(58 citation statements)

References 86 publications

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“…Internet of vehicles is an important application of intelligent transportation with Internet of things, and it is also an important part of smart city [ 1 – 6 ]. In Internet of vehicles system, there are various types of data sources that produce large amounts of temporal and spatial uninterrupted data flow.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Performance Analysis of STEP System in Internet of Vehicles Based on Queuing Theory

Wang

2022

Computational Intelligence and Neuroscience

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The Internet of vehicles (IoV) is an important research area of the intelligent transportation systems using Internet of things theory. The complex event processing technology is a basic issue for processing the data stream in IoV. In recent years, many researchers process the temporal and spatial data flow by complex event processing technology. Spatial Temporal Event Processing (STEP) is a complex event query language focusing on the temporal and spatial data flow in Internet of vehicles. There are four processing models of the event stream processing system based on the complex event query language: finite automata model, matching tree model, directed acyclic graph model, and Petri net model. In addition, the worst-case response time of the event stream processing system is an important indicator of evaluating the performance of the system. Firstly, this paper proposed a core algorithm of the temporal and spatial event stream processing program based on STEP by Petri net model. Secondly, we proposed a novel method to estimate the worst-case response time of the event stream processing system, which is based on stochastic Petri net and queuing theory. Finally, through the simulation experiment based on queuing theory, this paper proves that the data stream processing system based on STEP has good dynamic performance in processing the spatiotemporal data stream in Internet of vehicles.

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Section: Introductionmentioning

confidence: 99%

Dynamic Performance Analysis of STEP System in Internet of Vehicles Based on Queuing Theory

Wang

2022

Computational Intelligence and Neuroscience

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“…Xiaolong et al [24] presented a comprehensive and exhaustive survey of AI for edge service optimization in the IoV. To begin, the fundamental motivations for this study were discussed.…”

Section: Related Workmentioning

confidence: 99%

A Fog Computing Model for VANET to Reduce Latency and Delay Using 5G Network in Smart City Transportation

et al. 2022

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Connected vehicles are a vital part of smart cities, which connect over a wireless connection and bring mobile computation and communication abilities. As a mediator, fog computing resides between vehicles and the cloud and provides vehicles with processing, storage, and networking power through Vehicular Ad-hoc networks (VANET). VANET is a time-sensitive technology that requires less time to process a request received from a vehicle. Delay and latency are the notorious issues of VANET and fog computing. To deal with such problems, in this work, we developed a priority-based fog computing model for smart urban vehicle transportation that reduces the delay and latency of fog computing. To upgrade the fog computing infrastructure to meet the latency and Quality of Service (QoS) requirements, 5G localized Multi-Access Edge Computing (MEC) servers have also been used, which resulted tremendously in reducing the delay and the latency. We decreased the data latency by 20% compared to the experiment carried using only cloud computing architecture. We also reduced the processing delay by 35% compared with the utilization of cloud computing architecture.

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“…They focus mainly on DL on Edge aspect, but also give an overview of how DL has been used for optimizing edge. The covered edge management issues are edge caching, task offloading, Finally, Xu et al [20] survey AI solutions for edge server placement and offloading in Internet of Vehicles (IoV). Their survey is very limited due to the focus on IoV.…”

Section: Related Work a Edge Aimentioning

confidence: 99%

Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration

Kokkonen¹,

Lovén²,

Motlagh³

et al. 2022

Preprint

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Future AI applications require performance, reliability, and privacy that the existing, cloud-dependant system architectures cannot provide. In this article, we study orchestration in the device-edge-cloud continuum, and focus on AI for edge, that is, the AI methods used in resource orchestration. We claim that to support the constantly growing requirements of intelligent applications in the device-edge-cloud computing continuum, resource orchestration needs to embrace edge AI and emphasize local autonomy and intelligence. To justify the claim, we provide a general definition for continuum orchestration, and look at how current and emerging orchestration paradigms are suitable for the computing continuum. We describe certain major emerging research themes that may affect future orchestration, and provide an early vision of an orchestration paradigm that embraces those research themes. Finally, we survey current key edge AI methods and look at how they may contribute into fulfilling the vision of future continuum orchestration.

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Artificial intelligence for edge service optimization in Internet of Vehicles: A survey

Cited by 139 publications

References 86 publications

Dynamic Performance Analysis of STEP System in Internet of Vehicles Based on Queuing Theory

Dynamic Performance Analysis of STEP System in Internet of Vehicles Based on Queuing Theory

A Fog Computing Model for VANET to Reduce Latency and Delay Using 5G Network in Smart City Transportation

Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration

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