Tutorial: Edge Computing for Mobile Internet of Things

2021 ◽  
Author(s):  
Rodolfo W. L. Coutinho
Keyword(s):  
Internet Of Things ◽  
Mobile Internet ◽  
Edge Computing

scholarly journals A New Task Offloading Algorithm in Edge Computing

2021 ◽  
Author(s):  
Zhenjiang Zhang ◽  
Chen Li ◽  
ShengLung Peng ◽  
Xintong Pei
Keyword(s):  
Internet Of Things ◽  
User Experience ◽  
Mobile Internet ◽  
Edge Computing ◽  
The Internet ◽  
Disruptive Technology ◽  
Computing Power ◽  
New Applications ◽  
The Internet Of Things ◽  
Task Offloading

Abstract In the last few years, the Internet of Things (IOT), as a new disruptive technology, has gradually changed the world. With the prosperous development of the mobile Internet and the rapid growth of the Internet of Things, various new applications continue to emerge, such as mobile payment, face recognition, wearable devices, driverless, VR/AR, etc. Although the computing power of mobile terminals is getting higher and the traditional cloud computing model has higher computing power, it is often accompanied by higher latency and cannot meet the needs of users. In order to reduce user delay to improve user experience, and at the same time reduce network load to a certain extent, edge computing, as an application of IOT, came into being. In view of the new architecture after dating edge computing, this paper focuses on the task offloading in edge computing, from task migration in multi-user scenarios and edge server resource management expansion, and proposes a multi-agent load balancing distribution based on deep reinforcement learning DTOMALB, a distributed task allocation algorithm, can perform a reasonable offload method for this scenario to improve user experience and balance resource utilization. Simulations show that the algorithm has a certain adaptability compared to the traditional algorithm in the scenario of multi-user single cell, and reduces the complexity of the algorithm compared to the centralized algorithm, and reduces the average response delay of the overall user. And balance the load of each edge computing server, improve the robustness and scalability of the system.

scholarly journals A New Task Offloading Algorithm in Edge Computing

2020 ◽  
Author(s):  
Zhenjiang Zhang ◽  
Chen Li ◽  
ShengLung Peng ◽  
Xintong Pei
Keyword(s):  
Internet Of Things ◽  
User Experience ◽  
Mobile Internet ◽  
Edge Computing ◽  
The Internet ◽  
Disruptive Technology ◽  
Computing Power ◽  
New Applications ◽  
The Internet Of Things ◽  
Task Offloading

Abstract In the last few years, the Internet of Things (IOT), as a new disruptive technology, has gradually changed the world. With the prosperous development of the mobile Internet and the rapid growth of the Internet of Things, various new applications continue to emerge, such as mobile payment, face recognition, wearable devices, driverless, VR/AR, etc. Although the computing power of mobile terminals is getting higher and the traditional cloud computing model has higher computing power, it is often accompanied by higher latency and cannot meet the needs of users. In order to reduce user delay to improve user experience, and at the same time reduce network load to a certain extent, edge computing, as an application of IOT, came into being. In view of the new architecture after dating edge computing, this paper focuses on the task offloading in edge computing, from task migration in multi-user scenarios and edge server resource management expansion, and proposes a multi-agent load balancing distribution based on deep reinforcement learning DTOMALB, a distributed task allocation algorithm, can perform a reasonable offload method for this scenario to improve user experience and balance resource utilization. Simulations show that the algorithm has a certain adaptability compared to the traditional algorithm in the scenario of multi-user single cell, and reduces the complexity of the algorithm compared to the centralized algorithm, and reduces the average response delay of the overall user. And balance the load of each edge computing server, improve the robustness and scalability of the system.

scholarly journals A new task offloading algorithm in edge computing

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Zhenjiang Zhang ◽  
Chen Li ◽  
ShengLung Peng ◽  
Xintong Pei
Keyword(s):  
Internet Of Things ◽  
User Experience ◽  
Mobile Internet ◽  
Edge Computing ◽  
The Internet ◽  
Disruptive Technology ◽  
Computing Power ◽  
New Applications ◽  
The Internet Of Things ◽  
Task Offloading

AbstractIn the last few years, the Internet of Things (IOT), as a new disruptive technology, has gradually changed the world. With the prosperous development of the mobile Internet and the rapid growth of the Internet of Things, various new applications continue to emerge, such as mobile payment, face recognition, wearable devices, driverless, VR/AR, etc. Although the computing power of mobile terminals is getting higher and the traditional cloud computing model has higher computing power, it is often accompanied by higher latency and cannot meet the needs of users. In order to reduce user delay to improve user experience, and at the same time reduce network load to a certain extent, edge computing, as an application of IOT, came into being. In view of the new architecture after dating edge computing, this paper focuses on the task offloading in edge computing, from task migration in multi-user scenarios and edge server resource management expansion, and proposes a multi-agent load balancing distribution based on deep reinforcement learning DTOMALB, a distributed task allocation algorithm, can perform a reasonable offload method for this scenario to improve user experience and balance resource utilization. Simulations show that the algorithm has a certain adaptability compared to the traditional algorithm in the scenario of multi-user single cell, and reduces the complexity of the algorithm compared to the centralized algorithm, and reduces the average response delay of the overall user. And balance the load of each edge computing server, improve the robustness and scalability of the system.

scholarly journals Editorial: Special Section on Pervasive Edge Computing for Industrial Internet of Things

2021 ◽  
Vol 17 (7) ◽  
pp. 5010-5011
Author(s):  
Zhaolong Ning ◽  
Edith Ngai ◽  
Ricky Y. K. Kwok ◽  
Mohammad S. Obaidat
Keyword(s):  
Internet Of Things ◽  
Special Section ◽  
Edge Computing ◽  
Industrial Internet Of Things ◽  
Industrial Internet
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