Computational Intelligence and Deep Learning for Next-Generation Edge-Enabled Industrial IoT

Tang, Shunpu; Chen, Lunyuan; He, Ke; Xia, Junjuan; Fan, Lisheng; Nallanathan, Arumugam

doi:10.1109/tnse.2022.3180632

Cited by 63 publications

(31 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A key design in the MEC networks is the offloading strategy, which determines how many parts of the tasks should be computed by the CAPs. In essence, offloading is to utilize the computational resources from the CAPs at the cost of wireless transmission [11], [12]. A lot of researches have been done to achieve a fine trade-off between the communication and computation.…”

Section: A Literature Reviewmentioning

confidence: 99%

Outdated Access Point Selection for Mobile Edge Computing With Cochannel Interference

Lai

Xia

Fan

et al. 2022

IEEE Trans. Veh. Technol.

Self Cite

View full text Add to dashboard Cite

Section: A Literature Reviewmentioning

confidence: 99%

Outdated Access Point Selection for Mobile Edge Computing With Cochannel Interference

Lai

Xia

Fan

et al. 2022

IEEE Trans. Veh. Technol.

Self Cite

View full text Add to dashboard Cite

“…Among the existing activation functions, there is a most widely used activation function, named as linear rectification function (ReLu), and it can be expressed as [21][22][23] f (x) = max(0, x).…”

Section: System Model Of Deep Learning For Standard Knowledge Service...mentioning

confidence: 99%

Analysis and Design of Standard Knowledge Service System based on Deep Learning

Zhou

Lin

et al. 2022

EAI Endorsed Scal Inf Syst

View full text Add to dashboard Cite

The development of information technology has changed the mode of communication of social information, and this change has put forward new requirements on the contents, methods and even objects of information science research. Knowledge service in the information service process can extract knowledge and information content from various explicit and implicit knowledge resources according to people’s needs, build knowledge networks, and provide knowledge content or solutions for users’ problems. Hence, it is very important to investigate how to analyze and design the advanced standard knowledge service system based on deep learning. To this end, we firstly introduce the typical deep learning networks of convolutional neural network (CNN) for the knowledge service system, and then employ the CNN to implement the knowledge classification based on deep learning. Finally, some simulation results on the knowledge service system are presented to validate the proposed studies in this paper.

show abstract

“…where λ t represents the threshold of the monitoring rate. Then, this equation can be further derived as [22][23][24] In further, the outage probability can be calculated as,…”

Section: System Modelmentioning

confidence: 99%

Intelligent Wireless Monitoring Technology for 10kV Overhead lines in Smart Grid Networks

Shi

Liu

2022

EAI Endorsed Scal Inf Syst

View full text Add to dashboard Cite

Promoted by the rapid development of information technology, 10kV overhead line has been widely used in the majority of cities, and it is of great significance to monitor the distribution network effectively, in order to ensure the normal operation of the system. Most of traditional distribution network monitoring methods are based on manual work, which causes inconvenience to the distribution network fault location, repair, maintenance and real-time monitoring, and reduces the efficiency of the distribution network emergency repair and the reliability of power supply. Aiming at the automatic monitoring problem of 10kV overhead network, this paper adopts an intelligent wireless monitoring technology, where a monitoring node is employed to monitor the network transmission status through wireless links. We evaluate the system monitoring performance by using the metric of outage probability, depending on the wireless data rate over wireless channels. For the considered system, we derive analytical outage probability, in order to measure the system performance in the whole range of signal-to-noise ratio (SNR). The simulation results are finally presented to verify the analytical expressions on the system monitoring outage probability in this paper.

show abstract

Computational Intelligence and Deep Learning for Next-Generation Edge-Enabled Industrial IoT

Cited by 63 publications

References 33 publications

Outdated Access Point Selection for Mobile Edge Computing With Cochannel Interference

Outdated Access Point Selection for Mobile Edge Computing With Cochannel Interference

Analysis and Design of Standard Knowledge Service System based on Deep Learning

Intelligent Wireless Monitoring Technology for 10kV Overhead lines in Smart Grid Networks

Contact Info

Product

Resources

About