Data Driven Resource Allocation for NFV-Based Internet of Things

Tian, Xiaohua; Huang, Wenguang; Yu, Ziao; Wang, Xinbing

doi:10.1109/jiot.2019.2916865

Cited by 8 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If the data has not been summarized, the demand can be submitted through the data sharing platform, and then, the submitted water resources data can be reviewed. In the process of data review, the data processing department confirms the integrity and standardization of data items and relevant elements and associates them with the corresponding directory in the water resources data sharing platform to form a database and to realize data collection, classification, and sharing [8]. Data sharing outside the industry should follow the national and provincial government public data management rules and rely on the public data sharing and exchange platform built at the provincial level.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Data-Driven Modelling of Water Allocation for the Internet of Things

Dong

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

The allocation of water resources is an important aspect of maintaining public security, but there are still many problems in water resources management. The application of IoT technology in water resources management mainly focuses on water quality detection and water flow monitoring. For the allocation of water resources, the application of the Internet of things technology is not deep and sufficient, and the advantages of the Internet of things technology in water resources management are not fully utilized. In view of the above problems in the current situation, this paper proposes solutions for smart water resources. Smart water resources combine geographic information technology and Internet of things technology to visualize a map of water resources and realize the whole process management from the source to the end user and automatic data collection and analysis. Therefore, an intelligent system with remote control function is constructed, which can be applied to the practice of water resources allocation and realize the full utilization of water resources.

show abstract

Section: Introductionmentioning

confidence: 99%

Dynamic Data-Driven Modelling of Water Allocation for the Internet of Things

Dong

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…However, with the rise of new technologies such as cloud computing, the Internet of Things (IoT) and 5G, the traditional Internet architecture has been unable to meet its explosive development [2], [3]. In particular, the contradiction between the expanding functional requirements of network users and the inefficient and unreasonable allocation of underlying network resources in Internet infrastructure is more significant [4]. It is mainly reflected in the following aspects: First, the development of emerging network paradigms such as the IoT and 5G requires strong and robust basic network architecture as support.…”

Section: Introductionmentioning

confidence: 99%

Network Resource Allocation Strategy Based on Deep Reinforcement Learning

Zhang¹,

Wang

Zhang

et al. 2020

IEEE Open J. Comput. Soc.

View full text Add to dashboard Cite

The traditional Internet has encountered a bottleneck in allocating network resources for emerging technology needs. Network virtualization (NV) technology as a future network architecture, the virtual network embedding (VNE) algorithm it supports shows great potential in solving resource allocation problems. Combined with the efficient machine learning (ML) algorithm, a neural network model close to the substrate network environment is constructed to train the reinforcement learning agent. This paper proposes a two-stage VNE algorithm based on deep reinforcement learning (DRL) (TS-DRL-VNE) for the problem that the mapping result of existing heuristic algorithm is easy to converge to the local optimal solution. For the problem that the existing VNE algorithm based on ML often ignores the importance of substrate network representation and training mode, a DRL VNE algorithm based on full attribute matrix (FAM-DRL-VNE) is proposed. In view of the problem that the existing VNE algorithm often ignores the underlying resource changes between virtual network requests, a DRL VNE algorithm based on matrix perturbation theory (MPT-DRL-VNE) is proposed. Experimental results show that the above algorithm is superior to other algorithms.

show abstract

A Computing Resource Allocation Optimization Strategy for Massive Internet of Health Things Devices Considering Privacy Protection in Cloud Edge Computing Environment

Wang

2021

J Grid Computing

View full text Add to dashboard Cite

Data Driven Resource Allocation for NFV-Based Internet of Things

Cited by 8 publications

References 25 publications

Dynamic Data-Driven Modelling of Water Allocation for the Internet of Things

Dynamic Data-Driven Modelling of Water Allocation for the Internet of Things

Network Resource Allocation Strategy Based on Deep Reinforcement Learning

A Computing Resource Allocation Optimization Strategy for Massive Internet of Health Things Devices Considering Privacy Protection in Cloud Edge Computing Environment

Contact Info

Product

Resources

About