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Trusted UAV network coverage using blockchain, machine learning and auction mechanisms

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journal contribution
posted on 18.06.2020, 10:33 authored by Amjad Khan, Gaojie Chen, Yogachandran RahulamathavanYogachandran Rahulamathavan, Gan Zheng, Basil AsSadhan, Sangarapillai LambotharanSangarapillai Lambotharan
The UAV is emerging as one of the greatest technology developments for rapid network coverage provisioning at affordable cost. The aim of this paper is to outsource network coverage of a specific area according to a desired quality of service requirement and to enable various entities in the network to have intelligence to make autonomous decisions using blockchain and auction mechanisms. In this regard, by considering a multiple-UAV network where each UAV is associated to its own controlling operator, this paper addresses two major challenges: the selection of the UAV for the desired quality of network coverage and the development of a distributed and autonomous real-time monitoring framework for the enforcement of service level agreement (SLA). For a suitable UAV selection, we employ a reputation-based auction mechanism to model the interaction between the business agent who is interested in outsourcing the network coverage and the UAV operators serving in closeby areas. In addition, theoretical analysis is performed to show that the proposed auction mechanism attains a dominant strategy equilibrium. For the SLA enforcement and trust model, we propose a permissioned blockchain architecture considering Support Vector Machine (SVM) for real-time autonomous and distributed monitoring of UAV service. In particular, smart contract features of the blockchain are invoked for enforcing the SLA terms of payment and penalty, and for quantifying the UAV service reputation. Simulation results confirm the accuracy of theoretical analysis and efficacy of the proposed model.

Funding

Communications Signal Processing Based Solutions for Massive Machine-to-Machine Networks (M3NETs) : EP/R006385/1

History

School

  • Loughborough University London
  • Mechanical, Electrical and Manufacturing Engineering

Published in

IEEE Access

Volume

8

Pages

118219 - 118234

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Version

VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.

Acceptance date

15/06/2020

Publication date

2020-06-22

ISSN

2169-3536

Language

en

Depositor

Prof Sangarapillai Lambotharan. Deposit date: 17 June 2020