TY - JOUR
T1 - Backscatter-Assisted Data Offloading in OFDMA-Based Wireless-Powered Mobile Edge Computing for IoT Networks
AU - Nguyen, Phu X.
AU - Tran, Dinh Hieu
AU - Onireti, Oluwakayode
AU - Tin, Phu Tran
AU - Nguyen, Sang Quang
AU - Chatzinotas, Symeon
AU - Vincent Poor, H.
N1 - Funding Information:
Manuscript received September 24, 2020; revised January 10, 2021; accepted January 27, 2021. Date of publication February 5, 2021; date of current version May 21, 2021. The work of Dinh-Hieu Tran and Symeon Chatzinotas was supported in part by the Luxembourg National Research Fund (FNR) in the framework of the FNR-FNRS Bilateral Project “InWIP-NETs: Integrated Wireless Information and Power Networks.” The work of H. Vincent Poor was supported in part by the U.S. National Science Foundation under Grant CCF-1908308. (Corresponding author: Phu Tran Tin.) Phu X. Nguyen is with the Department of Computer Fundamentals, FPT University, Ho Chi Minh City 700000, Vietnam (e-mail: phunx4@fe.edu.vn).
Publisher Copyright:
© 2014 IEEE.
PY - 2021/6/1
Y1 - 2021/6/1
N2 - 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 hinder 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 article 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 nonconvexity. 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 subproblems, namely, TS ratio optimization with given offloading decision matrices and offloading decision optimization with given TS ratio. Especially, a closed-form expression for the TS ratio is obtained which greatly enhances the CPU execution time. Based on the solutions of the two subproblems, 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), the 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 s in a 50-user network, which is tailored for ultralow latency applications of IoT networks.
AB - 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 hinder 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 article 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 nonconvexity. 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 subproblems, namely, TS ratio optimization with given offloading decision matrices and offloading decision optimization with given TS ratio. Especially, a closed-form expression for the TS ratio is obtained which greatly enhances the CPU execution time. Based on the solutions of the two subproblems, 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), the 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 s in a 50-user network, which is tailored for ultralow latency applications of IoT networks.
KW - Backscatter communication
KW - Internet of Things (IoT)
KW - OFDMA
KW - mobile-edge computing (MEC)
KW - wireless power transfer (WPT)
UR - http://www.scopus.com/inward/record.url?scp=85100861524&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85100861524&partnerID=8YFLogxK
U2 - 10.1109/JIOT.2021.3057360
DO - 10.1109/JIOT.2021.3057360
M3 - Article
AN - SCOPUS:85100861524
SN - 2327-4662
VL - 8
SP - 9233
EP - 9243
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 11
M1 - 9348943
ER -