Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/154196
Title: Distributed dynamic resource management and pricing in the IoT systems with blockchain-as-a-service and UAV-enabled mobile edge computing
Authors: Asheralieva, A.
Niyato, Dusit
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Asheralieva, A. & Niyato, D. (2020). Distributed dynamic resource management and pricing in the IoT systems with blockchain-as-a-service and UAV-enabled mobile edge computing. IEEE Internet of Things Journal, 7(3), 1974-1993. https://dx.doi.org/10.1109/JIOT.2019.2961958
Project: DeST-SCI2019-0007
WASP/NTU M4082187 (4080)
2017-T1-002-007 RG122/17
MOE2014-T2-2-015 ARC4/15
2015-NRF-ISF001-2277
NRF2017 EWT-EP003-041
Journal: IEEE Internet of Things Journal
Abstract: In this article, we study the pricing and resource management in the Internet of Things (IoT) system with blockchain-as-a-service (BaaS) and mobile-edge computing (MEC). The BaaS model includes the cloud-based server to perform blockchain tasks and the set of peers to collect data from local IoT devices. The MEC model consists of the set of terrestrial and aerial base stations (BSs), i.e., unmanned aerial vehicles (UAVs), to forward the tasks of peers to the BaaS server. Each BS is also equipped with an MEC server to run some blockchain tasks. As the BSs can be privately owned or controlled by different operators, there is no information exchange among them. We show that the resource management and pricing in the BaaS-MEC system are modeled as a stochastic Stackelberg game with multiple leaders and incomplete information about actions of leaders/BSs and followers/peers. We formulate a novel hierarchical reinforcement learning (RL) algorithm for the decision makings of BSs and peers. We also develop an unsupervised hierarchical deep learning (HDL) algorithm that combines deep Q -learning (DQL) for BSs with the Bayesian deep learning (BDL) for peers. We prove that the proposed algorithms converge to stable states in which the peers' actions are the best responses to optimal actions of BSs.
URI: https://hdl.handle.net/10356/154196
ISSN: 2327-4662
DOI: 10.1109/JIOT.2019.2961958
Rights: © 2019 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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