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Power-constrained edge computing with maximum processing capacity for IoT networks

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Qin, Min, Chen, Li, Zhao, Nan, Chen, Yunfei, Yu, F. Richard and Wei, Guo (2019) Power-constrained edge computing with maximum processing capacity for IoT networks. IEEE Internet of Things Journal, 6 (3). pp. 4330-4343. doi:10.1109/JIOT.2018.2875218 ISSN 2327-4662.

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Official URL: https://doi.org/10.1109/JIOT.2018.2875218

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Abstract

Mobile edge computing (MEC) plays an important role in next-generation networks. It aims to enhance processing capacity and offer low-latency computing services for Internet of Things (IoT). In this paper, we investigate a resource allocation policy to maximize the available processing capacity (APC) for MEC IoT networks with constrained power and unpredictable tasks. First, the APC which describes the computing ability and speed of a served IoT device is defined. Then its expression is derived by analyzing the relationship between task partitioning and resource allocation. Based on this expression, the power allocation solution for the single-user MEC system with a single subcarrier is studied and the factors that affect the APC improvement are considered. For the multiuser MEC system, an optimization problem of APC with a general utility function is formulated and several fundamental criteria for resource allocation are derived. By leveraging these criteria, a binarysearch water-filling algorithm is proposed to solve the power allocation between local CPU and multiple subcarriers, and a suboptimal algorithm is proposed to assign the subcarriers among users. Finally, the validity of the proposed algorithms is verified by Monte Carlo simulation.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics > QA75 (Please use QA76 Electronic Computers. Computer Science)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Science, Engineering and Medicine > Engineering > Engineering
Library of Congress Subject Headings (LCSH): Internet of things, Mobile computing, Computer capacity
Journal or Publication Title: IEEE Internet of Things Journal
Publisher: IEEE
ISSN: 2327-4662
Official Date: June 2019
Dates:
DateEvent
June 2019Published
10 October 2018Available
6 October 2018Accepted
Volume: 6
Number: 3
Page Range: pp. 4330-4343
DOI: 10.1109/JIOT.2018.2875218
Status: Peer Reviewed
Publication Status: Published
Reuse Statement (publisher, data, author rights): © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 9 October 2018
Date of first compliant Open Access: 10 October 2018
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
2017ZX03001003-003[MSTPRC] Ministry of Science and Technology of the People's Republic of Chinahttp://dx.doi.org/10.13039/501100002855
61601432[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
UNSPECIFIED[MEPRC] Ministry of Education of the People's Republic of Chinahttp://dx.doi.org/10.13039/501100002338
2018D03Southeast Universityhttp://dx.doi.org/10.13039/501100008081
61871065[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
DUT17JC43[MEPRC] Ministry of Education of the People's Republic of Chinahttp://dx.doi.org/10.13039/501100002338
Related URLs:
  • http://ieee-iotj.org/

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