A Prediction-Based Multisensor Heuristic for the Internet of Things

Martins, Gabriel; Kopp, Luis Filipe; Genta, Jessica; Carmo, Luiz Fernando Rust da Costa; Farias, Claudio M. de

doi:10.1145/3345837.3355957

“…Finally, in Martins et al (2018), the authors present a knowledge fusion algorithm called Athena, which appears to follow the four levels of abstraction described by Luo et al (2002) (signal, pixel, feature, and symbol/decision), though it is not clearly stated. The proposed algorithm focuses on enhancing accuracy on a multi-application wireless sensor and actuator network (WSAN) using multisensory data fusion supporting knowledge extraction.…”

Section: Related Workmentioning

confidence: 99%

Multilevel data fusion for the internet of things in smart agriculture

Torres

¹

,

Rocha

²

,

Silva

³

et al. 2020

Computers and Electronics in Agriculture

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“…In this architecture, the implementation of network functions no longer depends on specific hardware facilities, but on the way of software programming to achieve flexible deployment of virtual functions [3][4][5]. For example, the realization of intelligent applications, such as the Internet of vehicles (IoV), intelligent medical, military unmanned aerial vehicle (UAV), cannot do without a strong virtual network infrastructure as support [6][7][8]. It cannot be ignored that there are a series of challenges in using network virtualization (NV) technology to provide services for these intelligent applications [9,10].…”

Section: Introductionmentioning

confidence: 99%

VNE solution for network differentiated QoS and security requirements: from the perspective of deep reinforcement learning

Wang

¹

,

Batth

²

,

Zhang

³

et al. 2021

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The rapid development and deployment of network services has brought a series of challenges to researchers. On the one hand, the needs of Internet end users/applications reflect the characteristics of travel alienation, and they pursue different perspectives of service quality. On the other hand, with the explosive growth of information in the era of big data, a lot of private information is stored in the network. End users/applications naturally start to pay attention to network security. In order to solve the requirements of differentiated quality of service (QoS) and security, this paper proposes a virtual network embedding (VNE) algorithm based on deep reinforcement learning (DRL), aiming at the CPU, bandwidth, delay and security attributes of substrate network. DRL agent is trained in the network environment constructed by the above attributes. The purpose is to deduce the mapping probability of each substrate node and map the virtual node according to this probability. Finally, the breadth first strategy (BFS) is used to map the virtual links. In the experimental stage, the algorithm based on DRL is compared with other representative algorithms in three aspects: long term average revenue, long term revenue consumption ratio and acceptance rate. The results show that the algorithm proposed in this paper has achieved good experimental results, which proves that the algorithm can be effectively applied to solve the end user/application differentiated QoS and security requirements.

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VNE Solution for Network Differentiated QoS and Security Requirements from the Perspective of Deep Reinforcement Learning

Jiang

¹

,

Zhang

²

2021

QoS-Aware Virtual Network Embedding

3

0

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The rapid development and deployment of network services has brought a series of challenges to researchers. On the one hand, the needs of Internet end users/applications reflect the characteristics of travel alienation, and they pursue different perspectives of service quality. On the other hand, with the explosive growth of information in the era of big data, a lot of private information is stored in the network. End users/applications naturally start to pay attention to network security. In order to solve the requirements of differentiated quality of service (QoS) and security, this paper proposes a virtual network embedding (VNE) algorithm based on deep reinforcement learning (DRL), aiming at the CPU, bandwidth, delay and security attributes of substrate network. DRL agent is trained in the network environment constructed by the above attributes. The purpose is to deduce the mapping probability of each substrate node and map the virtual node according to this probability. Finally, the breadth first strategy (BFS) is used to map the virtual links. In the experimental stage, the algorithm based on DRL is compared with other representative algorithms in three aspects: long term average revenue, long term revenue consumption ratio and acceptance rate. The results show that the algorithm proposed in this paper has achieved good experimental results, which proves that the algorithm can be effectively applied to solve the end user/application differentiated QoS and security requirements.

show abstract

A Prediction-Based Multisensor Heuristic for the Internet of Things

Cited by 5 publications

References 20 publications

Multilevel data fusion for the internet of things in smart agriculture

Multilevel data fusion for the internet of things in smart agriculture

VNE solution for network differentiated QoS and security requirements: from the perspective of deep reinforcement learning

VNE Solution for Network Differentiated QoS and Security Requirements from the Perspective of Deep Reinforcement Learning

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