SwarmControl: An Automated Distributed Control Framework for Self-Optimizing Drone Networks

  • Lorenzo Bertizzolo
  • , Salvatore D'Oro
  • , Ludovico Ferranti
  • , Leonardo Bonati
  • , Emrecan Demirors
  • , Zhangyu Guan
  • , Tommaso Melodia
  • , Scott Pudlewski

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Networks of Unmanned Aerial Vehicles (UAVs), composed of hundreds, possibly thousands of highly mobile and wirelessly connected flying drones will play a vital role in future Internet of Things (IoT) and 5G networks. However, how to control UAV networks in an automated and scalable fashion in distributed, interference-prone, and potentially adversarial environments is still an open research problem. This article introduces SwarmControl, a new software-defined control framework for UAV wireless networks based on distributed optimization principles. In essence, SwarmControl provides the Network Operator (NO) with a unified centralized abstraction of the networking and flight control functionalities. High-level control directives are then automatically decomposed and converted into distributed network control actions that are executed through programmable software-radio protocol stacks. SwarmControl (i) constructs a network control problem representation of the directives of the NO; (ii) decomposes it into a set of distributed sub-problems; and (iii) automatically generates numerical solution algorithms to be executed at individual UAVs.We present a prototype of an SDR-based, fully reconfigurable UAV network platform that implements the proposed control framework, based on which we assess the effectiveness and flexibility of SwarmControl with extensive flight experiments. Results indicate that the SwarmControl framework enables swift reconfiguration of the network control functionalities, and it can achieve an average throughput gain of 159% compared to the state-of-the-art solutions.

Original languageEnglish (US)
Title of host publicationINFOCOM 2020 - IEEE Conference on Computer Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1768-1777
Number of pages10
ISBN (Electronic)9781728164120
DOIs
StatePublished - Jul 2020
Externally publishedYes
Event38th IEEE Conference on Computer Communications, INFOCOM 2020 - Toronto, Canada
Duration: Jul 6 2020Jul 9 2020

Publication series

NameProceedings - IEEE INFOCOM
Volume2020-July
ISSN (Print)0743-166X

Conference

Conference38th IEEE Conference on Computer Communications, INFOCOM 2020
Country/TerritoryCanada
CityToronto
Period7/6/207/9/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

  • Distributed Network Control
  • Drone Networks
  • Software-Defined Networking

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