Revenue Model with Multi-Access Edge Computing for Cellular Network Architecture

Author(s):  
Jin Nakazato ◽  
Yu Tao ◽  
Gia Khanh Tran ◽  
Kei Sakaguchi
2020 ◽  
Vol 10 (7) ◽  
pp. 2478 ◽  
Author(s):  
Woosik Lee ◽  
Eun Suk Suh ◽  
Woo Young Kwak ◽  
Hoon Han

Mobile communication technology is evolving from 4G to 5G. Compared to previous generations, 5G has the capability to implement latency-critical services, such as autonomous driving, real-time AI on handheld devices and remote drone control. Multi-access Edge Computing is one of the key technologies of 5G in guaranteeing ultra-low latency aimed to support latency critical services by distributing centralized computing resources to networks edges closer to users. However, due to its high granularity of computing resources, Multi-access Edge Computing has an architectural vulnerability in that it can lead to the overloading of regional computing resources, a phenomenon called regional traffic explosion. This paper proposes an improved communication architecture called Hybrid Cloud Computing, which combines the advantages of both Centralized Cloud Computing and Multi-access Edge Computing. The performance of the proposed network architecture is evaluated by utilizing a discrete-event simulation model. Finally, the results, advantages, and disadvantages of various network architectures are discussed.


10.29007/7g1p ◽  
2018 ◽  
Author(s):  
Giovanni Nardini ◽  
Antonio Virdis ◽  
Giovanni Stea ◽  
Angelo Buono

Multi-access Edge Computing (MEC) is a novel paradigm to enrich current 4G and future 5G cellular networks by placing cloud-computing-based capabilities at the edge of the network. This will allow operators and service providers to endow the cellular network with enriched services. In this paper we describe the modeling and development of a MEC extension for the SimuLTE framework.


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

ICT Express ◽  
2021 ◽  
Author(s):  
Madhusanka Liyanage ◽  
Pawani Porambage ◽  
Aaron Yi Ding ◽  
Anshuman Kalla

Author(s):  
Chia-Shin Yeh ◽  
Shang-Liang Chen ◽  
I-Ching Li

The core concept of smart manufacturing is based on digitization to construct intelligent production and management in the manufacturing process. By digitizing the production process and connecting all levels from product design to service, the purpose of improving manufacturing efficiency, reducing production cost, enhancing product quality, and optimizing user experience can be achieved. To digitize the manufacturing process, IoT technology will have to be introduced into the manufacturing process to collect and analyze process information. However, one of the most important problems in building the industrial IoT (IIoT) environment is that different industrial network protocols are used for different equipment in factories. Therefore, the information in the manufacturing process may not be easily exchanged and obtained. To solve the above problem, a smart factory network architecture based on MQTT (MQ Telemetry Transport), IoT communication protocol, is proposed in this study, to construct a heterogeneous interface communication bridge between the machine tool, embedded device Raspberry Pi, and website. Finally, the system architecture is implemented and imported into the factory, and a smart manufacturing information management system is developed. The edge computing module is set up beside a three-axis machine tool, and a human-machine interface is built for the user controlling and monitoring. Users can also monitor the system through the dynamically updating website at any time and any place. The function of real-time gesture recognition based on image technology is developed and built on the edge computing module. The gesture recognition results can be transmitted to the machine controller through MQTT, and the machine will execute the corresponding action according to different gestures to achieve human-robot collaboration. The MQTT transmission architecture developed here is validated by the given edge computing application. It can serve as the basis for the construction of the IIoT environment, assist the traditional manufacturing industry to prepare for digitization, and accelerate the practice of smart manufacturing.


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