Multiple User Cooperative Mobility in Mobile Ad Hoc Networks: An Interaction Position Game

Mobile ad hoc networks (MANETs) have been widely used by individuals for residential or commercial usage in small-scale areas depending on fifth-generation (5G) and device-to-device (D2D) advancements. MANET demands extremely high system throughput, low latency, and best quality of service (QoS) mai...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.126297-126314
Main Authors: Xie, Jiquan, Murase, Tutomu
Format: Article
Language:English
Subjects:
Algorithms
Computing costs
cooperative behaviors
Game theory
Games
interaction position game
Interference
Mobile ad hoc networks
Mobile communication systems
Mobile computing
multiple user cooperative mobility
Performance evaluation
Quality of service
Simulated annealing
system throughput
Throughput
Wireless networks
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Summary:Mobile ad hoc networks (MANETs) have been widely used by individuals for residential or commercial usage in small-scale areas depending on fifth-generation (5G) and device-to-device (D2D) advancements. MANET demands extremely high system throughput, low latency, and best quality of service (QoS) maintenance. The current user mobility approaches only analyze the single movable user and use intuitive methods but cannot fundamentally solve multiple user cooperative mobility in practical applications. In this paper, we first jointly consider multiple user mobility and different geographical places and distances among all users. Then, we propose an interaction position game (IPG) to achieve high throughput and decrease computational cost. In this game, cooperative behaviors among movable users are proposed instead of assumed selfishness, as in traditional game models. In addition, we also present the simulated annealing (SA) algorithm to solve this NP-hard problem as a performance comparison. Finally, we evaluate the performance of this proposed game in various position cases. The results show that this game method improves the maximum throughput ratio evidently by 57.35% and 27.27% compared with the conventional intuitive method and SA algorithm, respectively. Compared with the exhaustive algorithm, this game reduces the computation cost by 82.79%.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3007931