Intelligent resource allocation management for vehicles network: An A3C learning approach

Chen, Miaojiang; Wang, Tian; Ota, Kaoru; Dong, Mianxiong; Zhao, Ming; Liu, Anfeng

doi:10.1016/j.comcom.2019.12.054

Cited by 95 publications

(49 citation statements)

References 26 publications

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“…In conclusion, the improved alr is much better than any of the ilr, the clr, and Geboy et al's [16] approach. An interesting line of future work is to consider consistent interpretations of coal geochemistry data on whole-coal versus ash bases through deep learning [43][44][45].…”

Section: Discussionmentioning

confidence: 99%

Towards Consistent Interpretations of Coal Geochemistry Data on Whole-Coal versus Ash Bases through Machine Learning

Peng

et al. 2020

Minerals

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Coal geochemistry compositional data on whole-coal basis can be converted back to ash basis based on samples' loss on ignition. However, the correlation between the concentrations of elements reported on whole-coal versus ash bases in many cases is inconsistent. Traditional statistical methods (e.g., correlation analysis) for compositional data on both bases may sometimes result in misleading results. To address this issue, we hereby propose an improved additive log-ratio data transformation method for analyzing the correlation between element concentrations reported on whole-coal versus ash bases. To verify the validity of the method proposed in this study, a data set which contains comprehensive analyses of 106 Late Paleozoic coal samples from the Datanhao mine and Adaohai Mine, Inner Mongolia, China, is used for the validity testing. A prediction model was built for performance evaluation of two methods based on the hierarchical clustering algorithm. The results show that the improved additive log-ratio is more effective in prediction for occurrence modes of elements in coal than the previously reported stability method, and therefore can be adopted for consistent interpretations of coal geochemistry compositional data on whole-coal vs. ash bases.

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Section: Discussionmentioning

confidence: 99%

Towards Consistent Interpretations of Coal Geochemistry Data on Whole-Coal versus Ash Bases through Machine Learning

Peng

et al. 2020

Minerals

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“…To improve the quality of services and enhance the resource allocation management in the IoV, Chen et al [21] propose a virtual framework using a learning-based resource allocation scheme for mobile vehicle service. Huang et al [22] also propose a service-oriented network architecture to reduce the traffic load and simplify network management with a service aggregation and caching (SAaC) scheme.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Resource Scheduling Optimization With Network Coding for Multi-User Services in the Internet of Vehicles

et al. 2020

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For Internet of Vehicles (IoV) systems with multiple users, network coding can be introduced to provide efficient error control and throughput improvement services. However, if the heterogeneity characteristics and requirements of the end users (vehicles) are neglected, it will be difficult for an IoV system to provide each end user with fair system services, without which the advantages of network coding cannot be fully achieved and the performance of the multiuser diversity system will be degraded. In this paper, we propose a Dynamic Resource Scheduling Optimization (DRSO) algorithm, a dynamic fair scheduling algorithm combined with network coding for system resource allocation in a multiuser IoV system. We construct a general solution framework for service scheduling: first, we estimate the fairness index for each end user (vehicle) with the key information on Quality of Service (QoS). Second, we construct a service scheduling control model based on the service capability of control entities (multiaccess edge computing servers), and propose a new utility evaluation function. Third, based on the fairness index, we select end users into multiple network coding sets. Network coding sets are the basic units of service scheduling. The optimization objective of the scheduling service is to maximize the total utility of all the network coding sets (the utility of the control entity). Finally, we establish a coding cache queue in the control entity based on the scheduling decision. To obtain the global optimal solution for active queue control, we combine a Quantum Particle Swarm Optimization (QPSO) algorithm with a Proportional Integral (PI) model. Then, the optimal scheduling decision can be made. Extensive simulation results show that DRSO outperforms related scheduling algorithms in varying traffic loads, demonstrating that DRSO can effectively guide service resource allocation. INDEX TERMS multiuser , fairness control, network coding set, cache queue, Internet of Vehicles

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“…They are often selforganized into networks [25][26][27]. Sensing devices on the roadside act as gateway to collect the data of the entire network [4], and then send the collected data to the passing mobile vehicles [28][29][30]. Due to the advanced hardware of mobile vehicles, they can communicate directly with the Internet.…”

Section: Introductionmentioning

confidence: 99%

An active and verifiable trust evaluation approach for edge computing

Wang

Zhang

et al. 2020

J Cloud Comp

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Billions of Internet of Thing (IoT) devices are deployed in edge network. They are used to monitor specific event, process and to collect huge data to control center with smart decision based on the collected data. However, some malicious IoT devices may interrupt and interfere with normal nodes in data collection, causing damage to edge network. Due to the open character of the edge network, how to identify the credibility of these nodes, thereby identifying malicious IoT devices, and ensure reliable data collection in the edge network is a great challenge. In this paper, an Active and Verifiable Trust Evaluation (AVTE) approach is proposed to identify the credibility of IoT devices, so to ensure reliable data collection for Edge Computing with low cost. The main innovations of the AVTE approach compared with the existing work are as follows: (1) In AVTE approach, the trust of the device is obtained by an actively initiated trusted detection routing method. It is fast, accurate and targeted. (2) The acquisition of trust in the AVTE approach is based on a verifiable method and it ensures that the trust degree has higher reliability. (3) The trust acquisition method proposed in this paper is low-cost. An encoding returned verification method is applied to obtain verification messages at a very low cost. This paper proposes an encoding returned verification method, which can obtain verification messages at a very low cost. In addition, the strategy of this paper adopts initiation and verification of adaptive active trust detection according to the different energy consumption of IoT devices, so as to reliably obtain the trust of device under the premise of ensuring network lifetime. Theoretical analysis shows that AVTE approach can improve the data collection rate by 0.5 ~ 23.16% while ensuring long network lifetime compared with the existing scheme.

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Intelligent resource allocation management for vehicles network: An A3C learning approach

Cited by 95 publications

References 26 publications

Towards Consistent Interpretations of Coal Geochemistry Data on Whole-Coal versus Ash Bases through Machine Learning

Towards Consistent Interpretations of Coal Geochemistry Data on Whole-Coal versus Ash Bases through Machine Learning

Dynamic Resource Scheduling Optimization With Network Coding for Multi-User Services in the Internet of Vehicles

An active and verifiable trust evaluation approach for edge computing

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