IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i24p4668-d998352.html
   My bibliography  Save this article

Memetic Algorithm with Isomorphic Transcoding for UAV Deployment Optimization in Energy-Efficient AIoT Data Collection

Author

Listed:
  • Xin Zhang

    (Jiangsu Key Laboratory of Media Design and Software Technology, School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China)

  • Yiyan Cao

    (Jiangsu Key Laboratory of Media Design and Software Technology, School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China)

Abstract

Unmanned aerial vehicles (UAVs) are one of the devices used to collect big data as part of the artificial intelligence of things (AIoT). To reduce total energy consumption, most researchers focus on optimizing the number and the location of UAVs, but ignore the distribution of UAVs in relation to the AIoT devices. Therefore, this paper proposes a memetic algorithm based on isomorphic transcoding space (MA-IT) to optimize the deployment of UAVs, solving, in particular, the distribution of UAVs in energy-efficient AIoT data collection. First, a simplified encoding method is designed to reduce the search space. This method only uses the distribution to represent a solution, and the number and the location of UAVs can be greedily deduced through the distribution. Afterwards, a pseudo-random initialization is proposed to initialize a population randomly and greedily. Then, an isomorphic transcoding (isoTcode) method is proposed to identify solutions with the isomorphic relations and to represent these solutions in a practical way in the UAV deployment problem. Finally, a crossover and a local search based on the isoTcode method are proposed to increase the solution diversity and improve the solution quality. Comparative experiments are conducted in the randomly generated instances with three problem scales. The results show that MA-IT performs better than other algorithms for solving the deployment optimization of UAVs.

Suggested Citation

  • Xin Zhang & Yiyan Cao, 2022. "Memetic Algorithm with Isomorphic Transcoding for UAV Deployment Optimization in Energy-Efficient AIoT Data Collection," Mathematics, MDPI, vol. 10(24), pages 1-18, December.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:24:p:4668-:d:998352
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/24/4668/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/24/4668/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tran Van Tung & To Truong An & Byung Moo Lee, 2022. "Joint Resource and Trajectory Optimization for Energy Efficiency Maximization in UAV-Based Networks," Mathematics, MDPI, vol. 10(20), pages 1-17, October.
    2. Jaeyoung Yang & Yong-Hyuk Kim & Yourim Yoon, 2022. "A Memetic Algorithm with a Novel Repair Heuristic for the Multiple-Choice Multidimensional Knapsack Problem," Mathematics, MDPI, vol. 10(4), pages 1-15, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Abdu Salam & Qaisar Javaid & Masood Ahmad & Ishtiaq Wahid & Muhammad Yeasir Arafat, 2023. "Cluster-Based Data Aggregation in Flying Sensor Networks Enabled Internet of Things," Future Internet, MDPI, vol. 15(8), pages 1-24, August.

    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. Mohammad Arif & Wooseong Kim, 2023. "Analysis of Fluctuating Antenna Beamwidth in UAV-Assisted Cellular Networks," Mathematics, MDPI, vol. 11(22), pages 1-24, November.
    2. Yong-Hyuk Kim & Fabio Caraffini, 2023. "Preface to “Swarm and Evolutionary Computation—Bridging Theory and Practice”," Mathematics, MDPI, vol. 11(5), pages 1-3, March.
    3. Jin Li & Wenyang Guan & Zuoyin Tang, 2023. "A Resource Allocation Scheme for Packet Delay Minimization in Multi-Tier Cellular-Based IoT Networks," Mathematics, MDPI, vol. 11(21), pages 1-18, November.

    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:jmathe:v:10:y:2022:i:24:p:4668-:d:998352. 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.