scholarly journals Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness

2021 ◽  
Author(s):  
Yuwei Sun ◽  
Hideya Ochiai ◽  
Hiroshi Esaki
Keyword(s):  
Deep Learning ◽  
Edge Computing ◽  
Survey Paper ◽  
Communication Efficiency ◽  
Multi Access

A survey paper.

scholarly journals Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning

Telecom ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 446-471
Author(s):  
Percy Kapadia ◽  
Boon-Chong Seet
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Access Network ◽  
Edge Computing ◽  
Fifth Generation ◽  
Radio Access ◽  
Ping Pong ◽  
Lstm Network ◽  
Multi Access

This paper proposes a potential enhancement of handover for the next-generation multi-tier cellular network, utilizing two fifth-generation (5G) enabling technologies: multi-access edge computing (MEC) and machine learning (ML). MEC and ML techniques are the primary enablers for enhanced mobile broadband (eMBB) and ultra-reliable and low latency communication (URLLC). The subset of ML chosen for this research is deep learning (DL), as it is adept at learning long-term dependencies. A variant of artificial neural networks called a long short-term memory (LSTM) network is used in conjunction with a look-up table (LUT) as part of the proposed solution. Subsequently, edge computing virtualization methods are utilized to reduce handover latency and increase the overall throughput of the network. A realistic simulation of the proposed solution in a multi-tier 5G radio access network (RAN) showed a 40–60% improvement in overall throughput. Although the proposed scheme may increase the number of handovers, it is effective in reducing the handover failure (HOF) and ping-pong rates by 30% and 86%, respectively, compared to the current 3GPP scheme.

scholarly journals Wearable IoT Smart-Log Patch: An Edge Computing-Based Bayesian Deep Learning Network System for Multi Access Physical Monitoring System

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 3030 ◽  
Author(s):  
Gunasekaran Manogaran ◽  
P. Shakeel ◽  
H. Fouad ◽  
Yunyoung Nam ◽  
S. Baskar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Monitoring System ◽  
Health Condition ◽  
Physical Activities ◽  
Edge Computing ◽  
Monitoring Systems ◽  
Multimedia Technology ◽  
Learning Network ◽  
Deep Learning Network ◽  
Multi Access

According to the survey on various health centres, smart log-based multi access physical monitoring system determines the health conditions of humans and their associated problems present in their lifestyle. At present, deficiency in significant nutrients leads to deterioration of organs, which creates various health problems, particularly for infants, children, and adults. Due to the importance of a multi access physical monitoring system, children and adolescents’ physical activities should be continuously monitored for eliminating difficulties in their life using a smart environment system. Nowadays, in real-time necessity on multi access physical monitoring systems, information requirements and the effective diagnosis of health condition is the challenging task in practice. In this research, wearable smart-log patch with Internet of Things (IoT) sensors has been designed and developed with multimedia technology. Further, the data computation in that smart-log patch has been analysed using edge computing on Bayesian deep learning network (EC-BDLN), which helps to infer and identify various physical data collected from the humans in an accurate manner to monitor their physical activities. Then, the efficiency of this wearable IoT system with multimedia technology is evaluated using experimental results and discussed in terms of accuracy, efficiency, mean residual error, delay, and less energy consumption. This state-of-the-art smart-log patch is considered as one of evolutionary research in health checking of multi access physical monitoring systems with multimedia technology.

Deep Learning Based Caching for Self-Driving Cars in Multi-Access Edge Computing

2020 ◽  
pp. 1-16 ◽  
Author(s):  
Anselme Ndikumana ◽  
Nguyen H. Tran ◽  
Do Hyeon Kim ◽  
Ki Tae Kim ◽  
Choong Seon Hong
Keyword(s):  
Deep Learning ◽  
Edge Computing ◽  
Self Driving Cars ◽  
Multi Access

Evolving Multi-Access Edge Computing to Support Enhanced IoT Deployments

2019 ◽  
Vol 3 (2) ◽  
pp. 26-34 ◽  
Author(s):  
Lanfranco Zanzi ◽  
Flavio Cirillo ◽  
Vincenzo Sciancalepore ◽  
Fabio Giust ◽  
Xavier Costa-Perez ◽  
...  
Keyword(s):  
Edge Computing ◽  
Multi Access
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