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A decentralized radio network for small groups of unmanned aerial vehicles

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The investigation of a decentralized radio network dedicated to unmanned aerial systems (UASs) was presented in the paper. Two frequencies (315 MHz; 434 MHz) and five different configurations of Gaussian frequency-shift keying (GFSK) were taken into account. Three different algorithms for decentralized networks were investigated and their influence on the network capacity was measured. The research was done both for static and dynamically changed unmanned aerial vehicle (UAV) positions. In order to quantify the research three different parameters were determined: RSSI, nP (number of data packets in one second), and f (frequency of data update).
Rocznik
Strony
art. no. e147922
Opis fizyczny
Bibliogr. 27 poz., rys., tab.
Twórcy
  • Faculty of Electrical Engineering, Automatic Control and Informatics, Department of Electrical Engineering and Mechatronics,Opole University of Technology, Opole, Poland
  • Faculty of Electrical Engineering, Automatic Control and Informatics, Department of Electrical Engineering and Mechatronics,Opole University of Technology, Opole, Poland
Bibliografia
  • [1] F.L. Bonali et al., “UAV-based surveying in volcano-tectonics: An example from the Iceland rift”, J. Struct. Geol. of Structural Geology, vol. 121, pp. 46–64, 2019, doi: 10.1016/j.jsg.2019.02.004.
  • [2] G. Choudhary, V. Sharma, and I. You, “Sustainable and secure trajectories for the military Internet of Drones (IoD) through an efficient Medium Access Control (MAC) protocol”, Comput. Electr. Eng., vol. 74, pp. 59–73, 2019, doi: 10.1016/j.compeleceng.2019.01.007.
  • [3] M.K. Nallapaneni, K. Sudhakar, M. Samykano, and V. Jayaseelan, “On the technologies empowering drones for intelligent monitoring of solar photovoltaic power plants”, presented at the International Conference on Robotics and Smart Manufacturing (RoSMa2018), Procedia Comput. Sci., vol. 133, pp. 585–593, 2018, doi: 10.1016/j.procs.2018.07.087.
  • [4] A. Pulvera and R. Weib, “Optimizing the spatial location of medical drones”, Appl. Geogr., vol. 90, pp. 9–16, 2018, doi: 10.1016/j.apgeog.2017.11.009.
  • [5] J. Abdulridha, Y. Ampatzidis, P. Roberts, and S.Ch. Kakarla, “Detecting powdery mildew disease in squash at different stages using UAV-based hyperspectral imaging and artificial intelligence”, Biosyst. Eng., vol. 197, pp. 135–148, 2020, doi: 10.1016/j.biosystemseng.2020.07.001.
  • [6] Y. Ampatzidis, V. Partel, B. Meyering, and U. Albrecht, “Citrus rootstock evaluation utilizing UAV-based remote sensing and artificial intelligence”, Comput. Electron. Agric., vol. 164, 104900, 2019, doi: 10.1016/j.compag.2019.104900.
  • [7] Y. Ampatzidis, V. Partel, and L. Costa, “Agroview: Cloud-based application to process, analyze and visualize UAV-collected data for precision agriculture applications utilizing artificial intelligence”, Comput. Electron. Agric., vol. 174, p. 105457, 2020, doi: 10.1016/j.compag.2020.105457.
  • [8] W. Zhang, M.W. Mueller, and R. D’Andrea, “Design, modeling and control of a flying vehicle with a single moving part that can be positioned anywhere in space”, Mechatronics, vol. 61, pp. 117–130, 2019, doi: 10.1016/j.mechatronics.2019.06.004.
  • [9] Z. Zhen, G. Tao, Y. Xu, and G. Song, “Multivariable adaptive control based consensus flight control system for UAVs formation”, Aerosp. Sci. Technol., vol. 93, p. 105336, 2019, doi: 10.1016/j.ast.2019.105336.
  • [10] M. Podpora, A. Kawala-Sterniuk, V. Kovalchuk, G. Bialic, and P. Piekielny, “A distributed cognitive approach in cybernetic modelling of human vision in a robotic swarm”, Bio-Algorithms Med-Syst., vol. 16, no. 3, p. 20200025, 2020, doi: 10.1515/bams-2020-0025.
  • [11] S. Mousavi, F. Afghah, J.D. Ashdown, and K. Turck, “Use of a quantum genetic algorithm for coalition formation in large-scale UAV networks”, Ad Hoc Netw., vol. 87, pp. 26–36, 2019, doi: 10.1016/j.adhoc.2018.11.008.
  • [12] Y. Wu and M. Cardei, “Multi-channel and cognitive radio approaches for wireless sensor networks”, Comput. Commun., vol. 94, pp. 30–45, 2016, doi: 10.1016/j.comcom.2016.08.010.
  • [13] R. Shahzadi, M. Ali, H.Z. Khan, and M. Naeem, “UAV assisted 5G and beyond wireless networks: A survey”, J. Netw. Comput. Appl., vol. 189, p. 103114, 2021, doi: 10.1016/j.jnca.2021.103114.
  • [14] J. Wang, Y. Liu, S. Niu, and H. Song, “Lightweight blockchain assisted secure routing of swarm UAS networking”, Comput. Commun., vol. 165, pp. 131–140, 2021, doi: 10.1016/j.comcom.2020.11.008.
  • [15] D. Mishra, A. Trotta, E. Traversi, M. Di Felice, and E. Natalizio, “Cooperative Cellular UAV-to-Everything (C-U2X) communication based on 5G sidelink for UAV swarms”, Comput. Commun., vol. 192, pp. 173–184, 2022, doi: 10.1016/j.comcom.2022.06.001.
  • [16] C. Kownacki and L. Ambroziak, “Local and asymmetrical potential field approach to leader tracking problem in rigid formations of fixed-wing UAVs”, Aerosp. Sci. Technol., vol. 68, pp. 465–474, 2017, doi: 10.1016/j.ast.2017.05.040.
  • [17] R. Wang, M. Lungu, Z. Zhou, X. Zhu, Y. Ding, and Q. Zhao, “Least global position information based control of fixed-wing UAVs formation flight: Flight tests and experimental validation”, Aerosp. Sci. Technol., vol. 140, p. 108473, 2023, doi: 10.1016/j.ast.2023.108473.
  • [18] L. Ambroziak and Z. Gosiewski, “Two stage switching control for autonomous formation flight of Unmanned Aerial Vehicles”, Aerosp. Sci. Technol., vol. 46, pp. 221–226, 2015, doi: 10.1016/j.ast.2015.07.015.
  • [19] Krajowa Tablica Przeznaczeń Częstotliwości, Dz.U.2022.1988 t.j. https://isap.sejm.gov.pl/isap.nsf/DocDetails.xsp?id=WDU20220001988
  • [20] K. Sohrabi, J. Gao, V. Ailawadhi, and G.J. Pottie, “Protocols for self-organization of a wireless sensor network”, IEEE Pers. Commun., vol. 7, no. 5, pp. 16–27, 2000, doi: 10.1109/98.878532.
  • [21] S. Shu and F. Lin, “Decentralized control of networked discrete event systems with communication delays”, Automatica, vol. 50, no. 8, pp. 2108–2112, 2014, doi: 10.1016/j.automatica.2014.05.035.
  • [22] S.J. Park, and K.H. Cho, “Decentralized supervisory control of discrete event systems with communication delays based on conjunctive and permissive decision structures”, Automatica, vol. 43, no. 4, pp. 738–743, 2007, doi: 10.1016/j.automatica.2006.10.016.
  • [23] H.D. Balbi, D. Passos, J. Vieira, C. Ricardo, L.C.S. Magalhães, and C. Albuquerque, “Towards a fast and stable filter for RSSI-based handoff algorithms in dense indoor WLANs”, Comput. Commun., vol. 183, pp. 19–32, 2022, doi: 10.1016/j.comcom.2021.10.024.
  • [24] J. Luomala and I. Hakala, “Analysis and evaluation of adaptive RSSI-based ranging in outdoor wireless sensor networks”, Ad Hoc Netw., vol. 87, pp. 100–112, 2019, doi: 10.1016/j.adhoc.2018.10.004.
  • [25] “RHMesh Class Reference,” RadioHead. [Online]. Available: https://www.airspayce.com/mikem/arduino/RadioHead/classRHMesh.html#details [Accessed: 20. June 2023].
  • [26] X. Zhang, T. Li, P. Gong, R. Liu, and X. Zha, “Modulation Recognition of Communication Signals Based on Multimodal Feature Fusion”, Sensors, vol. 22, p. 6539, 2022, doi: 10.3390/s22176539.
  • [27] “RH_RF69 Class Reference,” RadioHead. [Online]. Available: https://www.airspayce.com/mikem/arduino/RadioHead/classRH__RF69.html [Accessed: 20. June 2023].
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-183fd9a3-f3c2-44c8-9ce5-8d9d32df91a4
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