Latency-Aware IoT Service Provisioning in UAV-Aided Mobile-Edge Computing Networks

Advances in wireless communications are empowering the emerging Internet-of-Things (IoT) applications and services with billions of connected devices. Mobile-edge computing (MEC) has been proposed to reduce the round-trip delay of these applications as IoT devices may have limited computing resource...

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Bibliographic Details
Published in:IEEE internet of things journal Vol. 7; no. 10; pp. 10573 - 10580
Main Authors: Zhang, Liang, Ansari, Nirwan
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.10.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Approximation
Approximation algorithms
Cloud computing
Computational modeling
Edge computing
Electronic devices
Internet of Things
Internet of Things (IoT)
joint resource allocation
latency minimization
Mobile computing
mobile-edge computing (MEC)
Network latency
Provisioning
Relays
Unmanned aerial vehicles
unmanned aerial vehicles (UAV)
wireless backhauling
Wireless communications
Wireless networks
ISSN:2327-4662, 2327-4662
Online Access:Get full text
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Summary:Advances in wireless communications are empowering the emerging Internet-of-Things (IoT) applications and services with billions of connected devices. Mobile-edge computing (MEC) has been proposed to reduce the round-trip delay of these applications as IoT devices may have limited computing resources and the resource-rich mobile cloud may be far away. On the other aspect, unmanned aerial vehicles (UAVs) may potentially be employed to improve the quality of service and the channel conditions of users. We thus propose to utilize the UAV as a computing node as well as a relay node to improve the average user latency in the UAV-aided MEC (UAV-MEC) network and formulate the UAV-MEC problem with the objective to minimize the average latency of all UEs. As the UAV-MEC problem is NP-hard, we decompose it into three subproblems. We propose an approximation algorithm with low complexity to solve the first subproblem and then we obtain the optimal solutions of the remaining two subproblems, upon which another proposed approximation algorithm employs these solutions to finally solve the UAV-MEC problem. The evaluation results demonstrate that the proposed algorithm is superior to three baseline algorithms.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2020.3005117