A UAV-Swarm-Communication Model Using a Machine-Learning Approach for Search-and-Rescue Applications

• Published in: Drones (Basel) Vol. 6; no. 12; p. 372
• Main Authors: Khalil, Hisham, Rahman, Saeed Ur, Ullah, Inam, Khan, Inayat, Alghadhban, Abdulaziz Jarallah, Al-Adhaileh, Mosleh Hmoud, Ali, Gauhar, ElAffendi, Mohammed
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-12-2022
• Subjects: Algorithms; Antennas; artificial intelligence/machine learning for UAV communications; Cluster analysis; Clustering; Communication channels; Drone aircraft; emergency services; integration of UAV communications with space and terrestrial networks; Machine learning; Mathematical analysis; Mathematical models; Mobile communication systems; Optimization techniques; path-loss; Radio communications; Search and rescue operations; Signal strength; Spectrum allocation; Swarm intelligence; Technology application; UAV swarm control; Unmanned aerial vehicles; Vector quantization; Velocity; Wireless communication systems; Pakistan
• ISSN: 2504-446X; 2504-446X
• DOI: 10.3390/drones6120372

This paper presents a UAV-swarm-communication model using a machine-learning approach for search-and-rescue applications. Firstly, regarding the communication of UAVs, the receive signal strength (RSS) and power loss have been modeled using random forest regression, and the mathematical representation of the channel matrix has also been discussed. The second part consisted of swarm control modeling of UAVs; however, a dataset for five types of triangular swarm formations was generated, and K-means clustering was applied to predict the cluster. In order to obtain the correct swarm formation, the dendrogram of all types was investigated. Finally, the heat map and contour were plotted for all kinds of swarm clusters. Furthermore, it was observed that the RSS of proposed swarms had good agreement with swarm distances.