Paper Info
Title
Task number maximization offloading strategy seamlessly adapted to UAV scenario.
Abstract
Mobile edge computing (MEC) has been proposed in recent years to process resource-intensive and delay-sensitive applications at the edge of mobile networks, which can break the hardware limitations and resource constraints at user equipment (UE). In order to fully use the MEC server resource, how to maximize the number of offloaded tasks is meaningful especially for crowded place or disaster area. In this paper, an optimal partial offloading scheme POSMU (Partial Offloading Strategy Maximizing the User task number) is proposed to obtain the optimal offloading ratio, local computing frequency, transmission power and MEC server computing frequency for each UE. The problem is formulated as a mixed integer nonlinear programming problem (MINLP), which is NP-hard and challenging to solve. As such, we convert the problem into multiple nonlinear programming problems (NLPs) and propose an efficient algorithm to solve them by applying the block coordinate descent (BCD) as well as convex optimization techniques. Besides, we can seamlessly apply POSMU to UAV (Unmanned Aerial Vehicle) enabled MEC system by analyzing the 3D communication model. The optimality of POSMU is illustrated in numerical results, and POSMU can approximately maximize the number of offloaded tasks compared to other schemes.
Year
DOI
Venue
2020
10.1016/j.comcom.2019.12.018
Computer Communications
Keywords
Field
DocType
Offloaded task number maximization,Partial offloading,Unmanned aerial vehicle,Mobile edge computing
Computer science,Nonlinear programming,Real-time computing,Models of communication,Mobile edge computing,User equipment,Coordinate descent,Disaster area,Convex optimization,Maximization,Distributed computing
Journal
Volume
ISSN
Citations 
151
0140-3664
1
PageRank 
References 
Authors
0.37
0
6
Name
Order
Citations
PageRank
Qiang Tang123132.00
Lu Chang210.37
Kun Yang32045177.36
Kezhi Wang463741.20
jin wang524336.79
Pradip Kumar Sharma621923.83