Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/2815347.2815351acmconferencesArticle/Chapter ViewAbstractPublication PagesmswimConference Proceedingsconference-collections
research-article

Connectivity Stability in Autonomous Multi-level UAV Swarms for Wide Area Monitoring

Published: 02 November 2015 Publication History

Abstract

Many different types of unmanned aerial vehicles (UAVs) have been developed to address a variety of applications ranging from searching and mapping to surveillance. However, for complex wide-area surveillance scenarios, where fleets of autonomous UAVs must be deployed to work collectively on a common goal, multiple types of UAVs should be incorporated forming a heterogeneous UAV system. Indeed, the interconnection of two levels of UAVs---one with high altitude fixed-wing UAVs and one with low altitude rotary-wing UAVs---can provide applicability for scenarios which cannot be addressed by either UAV type. This work considers a bi-level flying ad hoc networks (FANETs), in which each UAV is equipped with ad hoc communication capabilities, in which the higher level fixed-wing swarm serves mainly as a communication bridge for the lower level UAV fleets, which conduct precise information sensing. The interconnection of multiple UAV types poses a significant challenge, since each UAV level moves according to its own mobility pattern, which is constrained by the UAV physical properties. Another important challenge is to form network clusters at the lower level, whereby the intra-level links must provide a certain degree of stability to allow a reliable communication within the UAV system. This article proposes a novel mobility model for the low-level UAVs that combines a pheromone-based model with a multi-hop clustering algorithm. The pheromones permit to focus on the least explored areas with the goal to optimize the coverage while the multi-hop clustering algorithm aims at keeping a stable and connected network. The proposed model works online and is fully distributed. The connection stability is evaluated against different measurements such as stability coefficient and volatility. The performance of the proposed model is compared to other state-of-the-art contributions using simulations. Experimental results demonstrate the ability of the proposed mobility model to significantly improve the network stability while having a limited impact on the wide-area coverage.

References

[1]
K. Abboud and W. Zhuang. Impact of node mobility on single-hop cluster overlap in vehicular ad hoc networks. In Proceedings of the 17th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, MSWiM '14, pages 65--72, New York, NY, USA, 2014. ACM.
[2]
I. Bekmezci, O. K. Sahingoz, and S. Temel. Flying ad-hoc networks (FANETs): A survey. Ad Hoc Networks, 11(3):1254 -- 1270, 2013.
[3]
E. Besada-Portas, L. de la Torre, J. de la Cruz, and B. de Andres-Toro. Evolutionary trajectory planner for multiple uavs in realistic scenarios. Robotics, IEEE Transactions on, 26(4):619--634, Aug 2010.
[4]
B. Brik, N. Lagraa, M. B. Yagoubi, and A. Lakas. An efficient and robust clustered data gathering protocol (cdgp) for vehicular networks. In Proceedings of the Second ACM International Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications, DIVANet '12, pages 69--74, New York, NY, USA, 2012. ACM.
[5]
M. R. Brust, A. Andronache, and S. Rothkugel. Waca: A hierarchical weighted clustering algorithm optimized for mobile hybrid networks. In 3rd International Conference on Wireless and Mobile Communications (ICWMC'07), pages 23--23. IEEE, 2007.
[6]
M. R. Brust, H. Frey, and S. Rothkugel. Adaptive multi-hop clustering in mobile networks. In ACM International Conference on Mobile Technology, Applications, and Systems, pages 132--138. ACM, 2007.
[7]
M. R. Brust, H. Frey, and S. Rothkugel. Dynamic multi-hop clustering for mobile hybrid wireless networks. In Int. Conf. on Ubiquitous information management and communication (ICUIMC), pages 130--135. ACM, 2008.
[8]
M. R. Brust and B. M. Strimbu. A networked swarm model for uav deployment in the assessment of forest environments. In IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing (IEEE ISSNIP), pages 1--6. IEEE, 2015.
[9]
Y. Cai, F. R. Yu, J. Li, Y. Zhou, and L. Lamont. Distributed scheduling for unmanned aerial vehicle networks with full-duplex radios and multi-packet reception. In Proceedings of the Second ACM International Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications, DIVANet '12, pages 89--96, New York, NY, USA, 2012. ACM.
[10]
L. Kesheng, Z. Jun, and Z. Tao. The clustering algorithm of UAV networking in near-space. In Antennas, Propagation and EM Theory, 2008. ISAPE 2008. 8th International Symposium on, pages 1550--1553, Nov 2008.
[11]
S. Khakpour, R. W. Pazzi, and K. El-Khatib. A prediction based clustering algorithm for target tracking in vehicular ad-hoc networks. In Proceedings of the Fourth ACM International Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications, DIVANet '14, pages 39--46, New York, NY, USA, 2014. ACM.
[12]
E. Kuiper and S. Nadjm-tehrani. Mobility models for UAV group reconnaissance applications. In in Proceedings of International Conference on Wireless and Mobile Communications, IEEE Computer Society. IEEE, 2006.
[13]
J. Li, C. Zhang, Y. Zhou, S. Perras, and Y. Q. Zhao. Exact analysis on network capacity of airborne manets with digital beamforming antennas. In Proceedings of the 17th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, MSWiM '14, pages 59--64, New York, NY, USA, 2014. ACM.
[14]
R. R. McCune and G. R. Madey. Swarm control of UAVs for cooperative hunting with DDDAS. Procedia Computer Science, 18(0):2537 -- 2544, 2013. 2013 International Conference on Computational Science.
[15]
L. Merino, F. Caballero, J. Ferruz, J. Wiklund, P.-E. Forssén, and A. Ollero. Multi-UAV cooperative perception techniques. In A. Ollero and I. Maza, editors, Multiple Heterogeneous Unmanned Aerial Vehicles, volume 37 of Springer Tracts in Advanced Robotics, pages 67--110. Springer Berlin Heidelberg, 2007.
[16]
Y. Pigné. Modeling and Processing Dynamic Graphs Applications to Mobile Ad Hoc Networks. Theses, Université du Havre, Dec. 2008.
[17]
Y. Pigné, A. Dutot, F. Guinand, and D. Olivier. Graphstream: A tool for bridging the gap between complex systems and dynamic graphs. CoRR, abs/0803.2093, 2008.
[18]
J. A. Sauter, R. Matthews, H. Van, D. Parunak, and S. A. Brueckner. Performance of digital pheromones for swarming vehicle control. In In Proceedings of Fourth International Joint Conference on Autonomous Agents and Multi-Agent Systems, pages 903--910. ACM Press, 2005.
[19]
J. Schleich, G.-J. Herbiet, P. Ruiz, P. Bouvry, J. Wagener, P. Bicheler, F. Guinand, and S. Chaumette. Enhancing the broadcast process in mobile ad hoc networks using community knowledge. In Proceedings of the First ACM International Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications, DIVANet '11, pages 23--30, New York, NY, USA, 2011. ACM.
[20]
J. Schleich, A. Panchapakesan, G. Danoy, and P. Bouvry. UAV fleet area coverage with network connectivity constraint. In Proceedings of the 11th ACM International Symposium on Mobility Management and Wireless Access, MobiWac '13, pages 131--138, New York, NY, USA, 2013. ACM.
[21]
C. Zang and S. Zang. Mobility prediction clustering algorithm for uav networking. In GLOBECOM Workshops (GC Wkshps), 2011 IEEE, pages 1158--1161, Dec 2011.

Cited By

View all
  • (2024)Refined Fractional-Order Fault-Tolerant Coordinated Tracking Control of Networked Fixed-Wing UAVs Against Faults and Communication Delays via Double Recurrent Perturbation FNNsIEEE Transactions on Cybernetics10.1109/TCYB.2022.320038254:2(1189-1201)Online publication date: Feb-2024
  • (2024)Distributed information fusion based trajectory tracking for USV and UAV clusters via multi-agent deep learning approachAerospace Systems10.1007/s42401-024-00275-4Online publication date: 23-Feb-2024
  • (2024)Scalable Task Allocation with Communications Connectivity for Flying Ad-Hoc NetworksJournal of Intelligent and Robotic Systems10.1007/s10846-024-02059-6110:1Online publication date: 1-Feb-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
DIVANet '15: Proceedings of the 5th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications
November 2015
124 pages
ISBN:9781450337601
DOI:10.1145/2815347
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 November 2015

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. ACO
  2. UAV
  3. clustering
  4. mobility model

Qualifiers

  • Research-article

Conference

MSWiM'15
Sponsor:

Acceptance Rates

Overall Acceptance Rate 70 of 308 submissions, 23%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)18
  • Downloads (Last 6 weeks)1
Reflects downloads up to 13 Sep 2024

Other Metrics

Citations

Cited By

View all
  • (2024)Refined Fractional-Order Fault-Tolerant Coordinated Tracking Control of Networked Fixed-Wing UAVs Against Faults and Communication Delays via Double Recurrent Perturbation FNNsIEEE Transactions on Cybernetics10.1109/TCYB.2022.320038254:2(1189-1201)Online publication date: Feb-2024
  • (2024)Distributed information fusion based trajectory tracking for USV and UAV clusters via multi-agent deep learning approachAerospace Systems10.1007/s42401-024-00275-4Online publication date: 23-Feb-2024
  • (2024)Scalable Task Allocation with Communications Connectivity for Flying Ad-Hoc NetworksJournal of Intelligent and Robotic Systems10.1007/s10846-024-02059-6110:1Online publication date: 1-Feb-2024
  • (2024)Drones as a service (DaaS) for 5G networks and blockchain-assisted IoT-based smart city infrastructureCluster Computing10.1007/s10586-024-04354-127:7(8725-8788)Online publication date: 17-Apr-2024
  • (2023)Stop & Route: Periodic Data Offloading in UAV Networks2023 18th Wireless On-Demand Network Systems and Services Conference (WONS)10.23919/WONS57325.2023.10062043(92-99)Online publication date: 30-Jan-2023
  • (2023)Stabilization through self-coupling in networks of small-world and scale-free topologyScientific Reports10.1038/s41598-023-27809-813:1Online publication date: 19-Jan-2023
  • (2023)Stop & Offload: Periodic data offloading in UAV networksComputer Communications10.1016/j.comcom.2023.10.003212(239-250)Online publication date: Dec-2023
  • (2023)The Unmanned Ground Vehicles (UGVs) for Digital AgricultureSmart Big Data in Digital Agriculture Applications10.1007/978-3-031-52645-9_5(99-109)Online publication date: 29-Dec-2023
  • (2022)Learning to Optimise a Swarm of UAVsApplied Sciences10.3390/app1219958712:19(9587)Online publication date: 24-Sep-2022
  • (2022)Oracle-Guided Deep Reinforcement Learning for Large-Scale Multi-UAVs Flocking and NavigationIEEE Transactions on Vehicular Technology10.1109/TVT.2022.318404371:10(10280-10292)Online publication date: Oct-2022
  • Show More Cited By

View Options

Get Access

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media