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Service Scheduling Based on Edge Computing for Power Distribution IoT

Zhu Liu1, 2, *, Xuesong Qiu1, Shuai Zhang2, Siyang Deng2, Guangyi Liu3, *

1 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
2 State Grid Information & Telecommunication Group Co., Ltd., Beijing, 102211, China.
3 Global Energy Interconnection Research Institute North America, San Jose, CA, 95134, USA.

*Corresponding Authors: Zhu Liu. Email: email;
   Guangyi Liu. Email: email.

Computers, Materials & Continua 2020, 62(3), 1351-1364.


With the growing amounts of multi-micro grids, electric vehicles, smart home, smart cities connected to the Power Distribution Internet of Things (PD-IoT) system, greater computing resource and communication bandwidth are required for power distribution. It probably leads to extreme service delay and data congestion when a large number of data and business occur in emergence. This paper presents a service scheduling method based on edge computing to balance the business load of PD-IoT. The architecture, components and functional requirements of the PD-IoT with edge computing platform are proposed. Then, the structure of the service scheduling system is presented. Further, a novel load balancing strategy and ant colony algorithm are investigated in the service scheduling method. The validity of the method is evaluated by simulation tests. Results indicate that the mean load balancing ratio is reduced by 99.16% and the optimized offloading links can be acquired within 1.8 iterations. Computing load of the nodes in edge computing platform can be effectively balanced through the service scheduling.


Cite This Article

Z. Liu, X. Qiu, S. Zhang, S. Deng and G. Liu, "Service scheduling based on edge computing for power distribution iot," Computers, Materials & Continua, vol. 62, no.3, pp. 1351–1364, 2020.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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