TY - JOUR
T1 - Optimizing the Operation Cost for UAV-Aided Mobile Edge Computing
AU - Zhang, Liang
AU - Ansari, Nirwan
N1 - Funding Information:
Manuscript received November 21, 2020; revised March 3, 2021 and April 27, 2021; accepted April 28, 2021. Date of publication April 30, 2021; date of current version July 8, 2021. This work was supported in part by National Science Foundation under Grant CNS-1814748. The review of this article was coordinated by Prof. Yi Qian. (Corresponding author: Liang Zhang.) The authors are with the Advanced Networking Laboratory, Department of Electrical and Computing Engineering, New Jersey Institute of Technology, Newark, NJ 07102 USA (e-mail: lz284@njit.edu; nirwan.ansari@njit.edu). Digital Object Identifier 10.1109/TVT.2021.3076980
Publisher Copyright:
© 1967-2012 IEEE.
PY - 2021/6
Y1 - 2021/6
N2 - Mobile edge computing (MEC) is leveraged to reduce the latency for the computation-intensive and latency-critical tasks offloaded from wireless devices and Internet of Things Devices (IoTDs). Unmanned aerial vehicles (UAVs) have attracted much attention from both academia and industry attributed to high mobility, high flexibility, and high maneuverability of UAVs. In this article, a novel UAV-assisted MEC architecture is proposed to provision services to IoTDs, where a UAV provides both communication and computing services or works as a relay node. We then formulate the joint computation offloading, spectrum resource allocation, computation resource allocation, and UAV placement (Joint-CAP) problem in the UAV-MEC network to minimize the operation cost of provisioning IoTDs. Since the Joint-CAP problem is a mixed integer non-linear programming problem and NP-hard, we decompose it into two sub-problems and solve the sub-problems sequentially. Then, we propose a $(1+\epsilon)$-approximation algorithm, named AA-CAP, to solve the Joint-CAP problem, and the performance of the AA-CAP algorithm is demonstrated to be superior to the baseline algorithms via simulations.
AB - Mobile edge computing (MEC) is leveraged to reduce the latency for the computation-intensive and latency-critical tasks offloaded from wireless devices and Internet of Things Devices (IoTDs). Unmanned aerial vehicles (UAVs) have attracted much attention from both academia and industry attributed to high mobility, high flexibility, and high maneuverability of UAVs. In this article, a novel UAV-assisted MEC architecture is proposed to provision services to IoTDs, where a UAV provides both communication and computing services or works as a relay node. We then formulate the joint computation offloading, spectrum resource allocation, computation resource allocation, and UAV placement (Joint-CAP) problem in the UAV-MEC network to minimize the operation cost of provisioning IoTDs. Since the Joint-CAP problem is a mixed integer non-linear programming problem and NP-hard, we decompose it into two sub-problems and solve the sub-problems sequentially. Then, we propose a $(1+\epsilon)$-approximation algorithm, named AA-CAP, to solve the Joint-CAP problem, and the performance of the AA-CAP algorithm is demonstrated to be superior to the baseline algorithms via simulations.
KW - Internet of Things
KW - Unmanned aerial vehicles
KW - computation offloading
KW - cost minimization
KW - joint resource allocation
KW - wireless backhauling
UR - http://www.scopus.com/inward/record.url?scp=85105109319&partnerID=8YFLogxK
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U2 - 10.1109/TVT.2021.3076980
DO - 10.1109/TVT.2021.3076980
M3 - Article
AN - SCOPUS:85105109319
SN - 0018-9545
VL - 70
SP - 6085
EP - 6093
JO - IEEE Transactions on Vehicular Communications
JF - IEEE Transactions on Vehicular Communications
IS - 6
M1 - 9420280
ER -