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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
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    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.
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    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.

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