IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i10p5927-d815088.html
   My bibliography  Save this article

System-Level Performance Analysis of Cooperative Multiple Unmanned Aerial Vehicles for Wildfire Surveillance Using Agent-Based Modeling

Author

Listed:
  • Ayesha Maqbool

    (Department of Computer Science, NBC, National University of Sciences and Technology, Islamabad 44000, Pakistan)

  • Alina Mirza

    (Department of Electrical Engineering, MCS, National University of Sciences and Technology, Islamabad 44000, Pakistan)

  • Farkhanda Afzal

    (Department of H&BS, MCS, National University of Sciences and Technology, Islamabad 44000, Pakistan)

  • Tajammul Shah

    (Department of Software Engineering, MCS, National University of Sciences and Technology, Islamabad 44000, Pakistan)

  • Wazir Zada Khan

    (Department of Computer Science, University of Wah, Wah, Rawalpindi 47040, Pakistan)

  • Yousaf Bin Zikria

    (Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea)

  • Sung Won Kim

    (Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea)

Abstract

In this paper, we propose an agent-based approach for the evaluation of Multiple Unmanned Autonomous Vehicle (MUAV) wildfire monitoring systems for remote and hard-to-reach areas. Emerging environmental factors are causing a higher number of wildfires and keeping these fires in check is becoming a global challenge. MUAV deployment for the monitoring and surveillance of potential fires has already been established. However, most of the scholarly work is still focused on MUAV operations details. In wildfire surveillance and monitoring, evaluations of the system-level performance in terms of the analysis of the effects of individual behavior on system surveillance has yet to be established. Especially in an MUAV system, the individual and cooperative behaviors of the team affect the overall performance of the system. Such systems are dynamic and stochastic because of an ever-changing environment. Quantifying the emergent system behavior and general performance measures of such a system by analytical methods is challenging. In our work, we present an agent-based model for MUAV surveillance missions. This paper focuses on the overall system performance of cooperative UAVs performing forest fire surveillance. The principal theme is to present the effects of three behaviors on overall performance: (1) the area allocation and (2) dynamic coverage, and (3) the effects of forest density on team allocation. For area allocation, three behaviors are simulated: (1) randomized, (2) two-layer barrier sweep coverage, and (3) full sweep coverage. For dynamic coverage, the effects of communication and resource unavailability during the mission are studied by analyzing the agent’s downtime spent on refueling. Last, an extensive simulation is carried out on wildfire models with varying forest density. It is found that cooperative complete sweep coverage strategies perform better than the rest and the performance of the team is greatly affected by the forest density.

Suggested Citation

  • Ayesha Maqbool & Alina Mirza & Farkhanda Afzal & Tajammul Shah & Wazir Zada Khan & Yousaf Bin Zikria & Sung Won Kim, 2022. "System-Level Performance Analysis of Cooperative Multiple Unmanned Aerial Vehicles for Wildfire Surveillance Using Agent-Based Modeling," Sustainability, MDPI, vol. 14(10), pages 1-21, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:5927-:d:815088
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/10/5927/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/10/5927/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ghafar Salavati & Ebrahim Saniei & Ebrahim Ghaderpour & Quazi K. Hassan, 2022. "Wildfire Risk Forecasting Using Weights of Evidence and Statistical Index Models," Sustainability, MDPI, vol. 14(7), pages 1-15, March.
    2. Xiaowei Fu & Haiyang Bi & Xiaoguang Gao, 2017. "Multi-UAVs Cooperative Localization Algorithms with Communication Constraints," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-8, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. José Francisco de Oliveira-Júnior & Munawar Shah & Ayesha Abbas & Washington Luiz Félix Correia Filho & Carlos Antonio da Silva Junior & Dimas de Barros Santiago & Paulo Eduardo Teodoro & David Mendes, 2022. "Spatiotemporal Analysis of Fire Foci and Environmental Degradation in the Biomes of Northeastern Brazil," Sustainability, MDPI, vol. 14(11), pages 1-19, June.
    2. Ebrahim Ghaderpour & Paolo Mazzanti & Francesca Bozzano & Gabriele Scarascia Mugnozza, 2024. "Trend Analysis of MODIS Land Surface Temperature and Land Cover in Central Italy," Land, MDPI, vol. 13(6), pages 1-15, June.
    3. Osama Ashraf Mohammed & Sasan Vafaei & Mehdi Mirzaei Kurdalivand & Sabri Rasooli & Chaolong Yao & Tongxin Hu, 2022. "A Comparative Study of Forest Fire Mapping Using GIS-Based Data Mining Approaches in Western Iran," Sustainability, MDPI, vol. 14(20), pages 1-13, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:5927-:d:815088. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.