Paper Info
Title
Collaborative Dynamic Task Allocation With Demand Response in Cloud-Assisted Multiedge System for Smart Grids
Abstract
Collaborative cloud–edge Power Internet of Things technology is required to support the development of smart grids, which have become intelligent, green, and regionally autonomous systems. The diversity of electricity customer behaviors and different computational intensities of energy management applications present challenges for task allocation among computing resources that belong to different agents. In this article, we propose a novel trilevel collaborative optimization model to comprehensively consider the relation among various agents, including users, edge nodes (ENs), a cloud center (CC), and a multiedge league (MEL). We first formulate a Stackelberg game between users and ENs modeled as the lower level and middle level. In addition, with the assistance of the CC, we propose a MEL cooperation scheme to analyze the collaborative task allocation problem among multiple edges, which is modeled as the upper level to maximize the social welfare of the multiedge system (MES) without damaging the interests of the various ENs. The proposed trilevel model is equivalent to a bilevel program, solved by the proposed collaborative dynamic task allocation (CDTA) algorithm. Numerical simulations are presented to verify the proposed scheme and the results show that this scheme is effective for task allocation among users, ENs, the cloud, and the MEL in a cloud-assisted MES.
Year
DOI
Venue
2022
10.1109/JIOT.2021.3096979
IEEE Internet of Things Journal
Keywords
DocType
Volume
Computing resources,edge and cloud,Power Internet of Things (P-IoT),revenue maximization,task allocation
Journal
9
Issue
ISSN
Citations 
4
2327-4662
0
PageRank 
References 
Authors
0.34
0
9
Name
Order
Citations
PageRank
Yuyan Sun101.69
Ze-xiang Cai201.35
Caishan Guo302.37
Guolong Ma401.01
Ziyi Zhang522.40
Haizhu Wang601.01
Jianing Liu710.69
Yiqun Kang800.34
Jianwen Yang900.34