Article (Scientific journals)
Backscatter-Assisted Data Offloading inOFDMA-based Wireless Powered Mobile EdgeComputing for IoT Networks
Nguyen, Xuan Phu; Tran Dinh, Hieu; Onireti, ‪Oluwakayode et al.
2021In IEEE Internet of Things Journal
Peer reviewed


Full Text
Author postprint (1.23 MB)

All documents in ORBilu are protected by a user license.

Send to


Keywords :
Backscatter communication; Internet ofThings (IoT); Mobile edge computing (MEC); OFDMA; wireless power transfer (WPT)
Abstract :
[en] Mobile edge computing (MEC) has emerged as a prominent technology to overcome sudden demands on computation-intensive applications of the Internet of Things (IoT) with finite processing capabilities. Nevertheless, the limited energy resources also seriously hinders IoT devices from offloading tasks that consume high power in active RF communications. Despite the development of energy harvesting (EH) techniques, the harvested energy from surrounding environments could be inadequate for power-hungry tasks. Fortunately, Backscatter communications (Backcom) is an intriguing technology to narrow the gap between the power needed for communication and harvested power. Motivated by these considerations, this paper investigates a backscatter-assisted data offloading in OFDMA-based wireless-powered (WP) MEC for IoT systems. Specifically, we aim at maximizing the sum computation rate by jointly optimizing the transmit power at the gateway (GW), backscatter coefficient, time-splitting (TS) ratio, and binary decision-making matrices. This problem is challenging to solve due to its non-convexity. To find solutions, we first simplify the problem by determining the optimal values of transmit power of the GW and backscatter coefficient. Then, the original problem is decomposed into two sub-problems, namely, TS ratio optimization with given offloading decision matrices and offloading decision optimization with given TS ratio. Especially, a closedform expression for the TS ratio is obtained which greatly enhances the CPU execution time. Based on the solutions of the two sub-problems, an efficient algorithm, termed the fast-efficient algorithm (FEA), is proposed by leveraging the block coordinate descent method. Then, it is compared with exhaustive search (ES), bisection-based algorithm (BA), edge computing (EC), and local computing (LC) used as reference methods. As a result, the FEA is the best solution which results in a near-globally-optimal solution at a much lower complexity as compared to benchmark schemes. For instance, the CPU execution time of FEA is about 0.029 second in a 50-user network, which is tailored for ultralow latency applications of IoT networks.
Research center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SIGCOM
Disciplines :
Electrical & electronics engineering
Author, co-author :
Nguyen, Xuan Phu;  FPT University > Department of Computer Fundamentals, FPT University, Ho Chi Minh City
Tran Dinh, Hieu ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Onireti, ‪Oluwakayode;  University of Glasgow
Tran Phu, Tin;  Ton Duc Thang University > Wireless Communications Research Group,Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam,
Nguyen Quang, Sang;  Duy Tan University > Institute of Fundamental and AppliedSciences, Duy Tan University, Ho Chi Minh City
Chatzinotas, Symeon  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
H. Vincent, Poor;  Princeton University > lectrical Engineering Department at Princeton University, NJ 08544, USA
External co-authors :
Language :
Title :
Backscatter-Assisted Data Offloading inOFDMA-based Wireless Powered Mobile EdgeComputing for IoT Networks
Publication date :
05 February 2021
Journal title :
IEEE Internet of Things Journal
Publisher :
Institute of Electrical and Electronics Engineers
Peer reviewed :
Peer reviewed
Focus Area :
Security, Reliability and Trust
Available on ORBilu :
since 06 June 2021


Number of views
119 (8 by Unilu)
Number of downloads
305 (4 by Unilu)

Scopus citations®
Scopus citations®
without self-citations
WoS citations


Similar publications

Contact ORBilu