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Hierarchical game-theoretic and reinforcement learning framework for computational offloading in UAV-enabled mobile edge computing networks with multiple service providers

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posted on 2024-10-09, 15:07 authored by Alia AsheralievaAlia Asheralieva, Dusit Niyato
We present a novel game-theoretic (GT) and reinforcement learning (RL) framework for computational offloading in the mobile edge computing (MEC) network operated by multiple service providers (SPs). The network is formed by MEC servers installed at stationary base stations (BSs) and unmanned aerial vehicles (UAVs) deployed as quasi-stationary BSs. Since computing powers of MEC servers are limited, the BSs in proximity can form coalitions with shared data processing resources to serve their users more efficiently. However, as BSs can be privately owned or controlled by different SPs, in any coalition, the BSs: 1) take only the actions that maximize their long-term payoffs and 2) do not coordinate their actions with other BSs in the coalition. That is, inside each coalition, BSs act in an independent and self-interested manner. Therefore, the interactions among BSs cannot be described by conventional coalitional games. Instead, the network operation is modeled by a two-level hierarchical model. The upper level is a cooperative game that defines the process of coalition formation. The lower level comprises the set of noncooperative subgames to represent a self-interested and independent behavior of BSs in coalitions. To enable each BS to select a coalition and decide on its action maximizing its long-term payoff, we propose two algorithms that combine coalition formation with RL and prove that these algorithms converge to the states where the coalitional structure is strongly stable and the strategies of BSs are in the mixed-strategy Nash equilibrium (NE).

Funding

A*STAR-NTU-SUTD Joint Research Grant Call on Artificial Intelligence for the Future of Manufacturing RGANS1906

WASP/NTU: grant M4082187 (4080)

Singapore MOE Tier 1: grant 2017-T1-002-007 RG122/17

Singapore MOE Tier 2: grant MOE2014-T2-2-015 ARC4/15

Singapore NRF: grant 2015-NRF-ISF001-2277

Singapore EMA Energy Resilience: grant NRF2017EWT-EP003-041

History

School

  • Science

Department

  • Computer Science

Published in

IEEE Internet of Things Journal

Volume

6

Issue

5

Pages

8753 - 8769

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Version

  • AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Acceptance date

2019-06-12

Publication date

2019-06-19

Copyright date

2019

eISSN

2327-4662

Language

  • en

Depositor

Dr Alia Asheralieva. Deposit date: 29 May 2024