Araştırma Makalesi
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Yazılım-tanımlı İHA Ağları için Deney Ortamı Tasarımı

Yıl 2024, Cilt: 17 Sayı: 2, 132 - 141
https://doi.org/10.54525/bbmd.1595419

Öz

İnsansız Hava Araçları (İHA) çeviklikleri ve esnek kişiselleştirilme seçenekleri ile geniş bir yelpazedeki problemlerin çözümlerinde kolay ve yaygın şekilde kullanılmaktadırlar. Fakat, bu çözümleri geliştirirken ve uygularken uygulama ortamının gerektirdiği birden fazla ve farklı telsiz haberleşme yöntemleri, benzer şekilde çeşitlilik gösteren hesaplama birimlerinin birbirleri ile uyum içerisinde işlemesi zorlu bir engel teşkil etmektedir. Bununla birlikte uçuş denetçisinin de İHA’ların görev sahasına uygun bir şekilde biçimlendirilmesi ve geliştirilmesi gerekmektedir. Bu çalışmada İHA’lar için esnek deney ortamı tanıtılmakta ve kurulum ve yürütülme basamakları anlatılmaktadır. Önerilen deney ortamı haberleşme için Wi-Fi, LoRa ve Yazılım-tanımlı Radyo erişim yöntemlerini desteklemekte ve ek olarak bu haberleşme teknolojilerini ağ katmanı seviyesinde, Yazılım-tanımlı Ağ yaklaşımı kullanılarak yatay düzlemde birleştirmekte ve karmaşık yönlendirme algoritmalarının kolayca yürütülmesine olanak sağlamaktadır. Dolayısı ile önerilen deney ortamı Fiziksel, Veri Bağlantı ve Ağ katmanlarının esnek şekilde değiştirilmesine ve bu katmanları kapsayan çözüm veya çözümlerin kolaylıkla geliştirilmesine olanak sağlamaktadır. Çalışmada son olarak bu önerilen deney ortamı kullanarak elde edilen deney detayları ve ilgili sonuçları paylaşılmaktadır.

Kaynakça

  • Fan, B., Li, Y., Zhang, R., & Fu, Q., Review on the technological development and application of UAV systems. Chinese Journal of Electronics, 29(2), 2020, 199-207.
  • Bertizzolo, L., D’oro, S., Ferranti, L., Bonati, L., Demirors, E., Guan, Z., Melodia, T., & Pudlewski, S., Swarmcontrol: An automated distributed control framework for self-optimizing drone networks, 2020, https://arxiv.org/abs/2005.09781
  • Çoğay, S., Sarı, T. T. & Seçinti, G. SoNaR: Software-defined Network and Radio Framework for FANETs, IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), 2021, pp. 268-273.
  • Paredes, W.D., Kaushal, H., Vakilinia, I., Prodanoff, Z. LoRa Technology in Flying Ad Hoc Networks: A Survey of Challenges and Open Issues, Sensors 2023, 23, 2403. https://doi.org/10.3390/s23052403
  • Sobot, S., et al., Two-Tier UAV-based Low Power Wide Area Networks: A Testbed and Experimentation Study, 2023 6th Conference on Cloud and Internet of Things (CIoT), Lisbon, Portugal, 2023, pp. 85-90, doi: 10.1109/CIoT57267.2023.10084912.
  • De Rango, F., & Stumpo, D., Supporting Path Planning in LoRa-based UAVs for dynamic Coverage for IoT devices, 2023 IEEE 20th Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, 2023, pp. 337-340, doi: 10.1109/CCNC51644.2023.10060525.
  • Cheng, H., Bertizzolo, L., D’oro, S., Buczek, J., Melodia, T., & Bentley, E.S., "Learning to Fly: A Distributed Deep Reinforcement Learning Framework for Software-Defined UAV Network Control," in IEEE Open Journal of the Communications Society, vol. 2, pp. 1486-1504, 2021, doi: 10.1109/OJCOMS.2021.3092690.
  • Mohanti, S., et al, "AirBeam: Experimental Demonstration of Distributed Beamforming by a Swarm of UAVs," 2019 IEEE 16th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), Monterey, CA, USA, 2019, pp. 162-170, doi: 10.1109/MASS.2019.00028.
  • Aydın, E. E., Kara, O., Cakir, F., Cansiz, B. S., Secinti, G., & Canberk, B., Enabling Self-Organizing TDMA Scheduling for Aerial Swarms. In Proceedings of the Eighth Workshop on Micro Aerial Vehicle Networks, Systems, and Applications, 2022, July, pp. 13-18.
  • Shukla, R.M., Sengupta, S., & Patra, A.N., “Software-defined network based resource allocation in distributed servers for unmanned aerial vehicles,” in Proc. IEEE CCWC, 2018.
  • Zhao, Z., et al., “Software-defined unmanned aerial vehicles networking for video dissemination services,” Ad Hoc Netw., vol. 83, 2019, pp. 68–77, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1570870518306231
  • Xiong, F., Li, A., Wang, H., & Tang, L., "An SDN-MQTT Based Communication System for Battlefield UAV Swarms," in IEEE Communications Magazine, vol. 57, no. 8, August 2019, pp. 41-47, doi: 10.1109/MCOM.2019.1900291.
  • Shurrab, M., Mizouni, R., Singh, S., & Otrok, H., Reinforcement learning framework for UAV-based target localization applications. Internet of Things, 23, 100867, 2023.
  • Choi, H.-H., Oh, J., Kang, K.-M., & Lee, H., "Idle-Less Slotted ALOHA Protocol for Drone Swarm Identification," in IEEE Transactions on Vehicular Technology, vol. 72, no. 8, Aug. 2023, pp. 11080-11085, doi: 10.1109/TVT.2023.3261104.
  • Chang, H., Chen, Y., Zhang, B., & Doermann, D., "Multi-uav mobile edge computing and path planning platform based on reinforcement learning", IEEE Transactions on Emerging Topics in Computational Intelligence, 2021.
  • Liu, X., Lam, K., Alkouz, B., Shahzaad, B., & Bouguettaya, A., "Constraint-based Formation of Drone Swarms," 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), Pisa, Italy, 2022, pp. 73-75, doi: 10.1109/PerComWorkshops53856.2022.9767410.
  • Song, Q., Zeng, Y., Xu, J. et al., A survey of prototype and experiment for UAV communications. Sci. China Inf. Sci. 64, 140301, 2021. https://doi.org/10.1007/s11432-020-3030-2
  • Douklias, A., Karagiannidis, L., Misichroni, F., & Amditis, A., Design and implementation of a UAV-based airborne computing platform for computer vision and machine learning applications, Sensors, 22(5), 2049, 2022.
  • Shi, Y., Wensowitch, J., Ward, A., Badi, M., & Camp, J., "Building UAV-Based Testbeds for Autonomous Mobility and Beamforming Experimentation," 2018 IEEE International Conference on Sensing, Communication and Networking (SECON Workshops), Hong Kong, China, 2018, pp. 1-5, doi: 10.1109/SECONW.2018.8396345.
  • Sommer, D., Irigireddy, A.S.C.R., Parkhurst, J., & Nastrucci, E.-R, "SDR- and UAV-Based Wireless Avionics Intra-Communication Testbed," 2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC), San Antonio, TX, USA, 2020, pp. 1-5, doi: 10.1109/DASC50938.2020.9256639.
  • Enhos, K., Unal, D., Turco, J., Demirors, E., & Melodia, T., Marena: Sdr-based testbed for underwater wireless communication and networking research. In Proceedings of The 17th ACM Workshop on Wireless Network Testbeds, Experimental evaluation & Characterization, 2023, October, pp. 80-87.
  • Baumgärtner, L., Bauer, M., & Bloessl, B., SUN: A Simulated UAV Network Testbed with Hardware-in-the-Loop SDR Support. In 2023 IEEE Wireless Communications and Networking Conference (WCNC), 2023, March, pp. 1-6.
  • Lahoud, C., Ehsanfar, S., Gabriel, M., Küffner, P., & Mößner, K., Experimental Testbed Results on LTE/5G-V2I Communication using Software Defined Radio. In ICC 2022-IEEE International Conference on Communications, 2022, May, pp. 2894-2899.
  • McKeown N., Anderson T., Balakrishnan H., Parulkar G., Peterson L., Rexford J., Shenker S. & Turner J.. 2008. OpenFlow: enabling innovation in campus networks. SIGCOMM Comput. Commun. Rev. 38, 2, April 2008, 69–74. https://doi.org/10.1145/1355734.1355746
  • Secinti G., Trotta A., Mohanti S., Di Felice M. & Chowdhury K. R., "FOCUS: Fog Computing in UAS Software-Defined Mesh Networks," in IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 6, pp. 2664-2674, June 2020, doi: 10.1109/TITS.2019.2960305.
  • Sharma, V., Song, F., You, I., & Chao, H. C. (2017). Efficient management and fast handovers in software defined wireless networks using UAVs. IEEE Network, 31(6), 78-85.
  • Oubbati, O. S., Atiquzzaman, M., Ahanger, T. A., & Ibrahim, A. (2020). Softwarization of UAV networks: A survey of applications and future trends. IEEE Access, 8, 98073-98125.
  • Brooke, J. (2013). SUS: a retrospective. Journal of usability studies, 8(2), 29-40.

Test-bed Design for Software-defined UAV Networks

Yıl 2024, Cilt: 17 Sayı: 2, 132 - 141
https://doi.org/10.54525/bbmd.1595419

Öz

Unmanned Aerial Vehicles (UAVs) can be used to solve a wide range of problems due to their high agility and flexible configuration capabilities. However, developing and implementing these solutions become a challenging obstacle as multiple communication technologies and computing units must be coordinated to work together seamlessly. Additionally, flight control must be provided appropriately for the task at hand. In this study, we introduce our flexible UAV-based test environment and explain the setup steps. Our test environment has Wi-Fi, LoRa, and Software-defined Radio capabilities for communication. Furthermore, these communication technologies can be adjusted as needed at the network layer level using the Software-Defined Networking paradigm. Therefore, our test environment facilitates the flexible coordination of the Physical, Data Link, and Network layers. Finally, we share the test results obtained using this proposed structure in our study.

Kaynakça

  • Fan, B., Li, Y., Zhang, R., & Fu, Q., Review on the technological development and application of UAV systems. Chinese Journal of Electronics, 29(2), 2020, 199-207.
  • Bertizzolo, L., D’oro, S., Ferranti, L., Bonati, L., Demirors, E., Guan, Z., Melodia, T., & Pudlewski, S., Swarmcontrol: An automated distributed control framework for self-optimizing drone networks, 2020, https://arxiv.org/abs/2005.09781
  • Çoğay, S., Sarı, T. T. & Seçinti, G. SoNaR: Software-defined Network and Radio Framework for FANETs, IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), 2021, pp. 268-273.
  • Paredes, W.D., Kaushal, H., Vakilinia, I., Prodanoff, Z. LoRa Technology in Flying Ad Hoc Networks: A Survey of Challenges and Open Issues, Sensors 2023, 23, 2403. https://doi.org/10.3390/s23052403
  • Sobot, S., et al., Two-Tier UAV-based Low Power Wide Area Networks: A Testbed and Experimentation Study, 2023 6th Conference on Cloud and Internet of Things (CIoT), Lisbon, Portugal, 2023, pp. 85-90, doi: 10.1109/CIoT57267.2023.10084912.
  • De Rango, F., & Stumpo, D., Supporting Path Planning in LoRa-based UAVs for dynamic Coverage for IoT devices, 2023 IEEE 20th Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, 2023, pp. 337-340, doi: 10.1109/CCNC51644.2023.10060525.
  • Cheng, H., Bertizzolo, L., D’oro, S., Buczek, J., Melodia, T., & Bentley, E.S., "Learning to Fly: A Distributed Deep Reinforcement Learning Framework for Software-Defined UAV Network Control," in IEEE Open Journal of the Communications Society, vol. 2, pp. 1486-1504, 2021, doi: 10.1109/OJCOMS.2021.3092690.
  • Mohanti, S., et al, "AirBeam: Experimental Demonstration of Distributed Beamforming by a Swarm of UAVs," 2019 IEEE 16th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), Monterey, CA, USA, 2019, pp. 162-170, doi: 10.1109/MASS.2019.00028.
  • Aydın, E. E., Kara, O., Cakir, F., Cansiz, B. S., Secinti, G., & Canberk, B., Enabling Self-Organizing TDMA Scheduling for Aerial Swarms. In Proceedings of the Eighth Workshop on Micro Aerial Vehicle Networks, Systems, and Applications, 2022, July, pp. 13-18.
  • Shukla, R.M., Sengupta, S., & Patra, A.N., “Software-defined network based resource allocation in distributed servers for unmanned aerial vehicles,” in Proc. IEEE CCWC, 2018.
  • Zhao, Z., et al., “Software-defined unmanned aerial vehicles networking for video dissemination services,” Ad Hoc Netw., vol. 83, 2019, pp. 68–77, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1570870518306231
  • Xiong, F., Li, A., Wang, H., & Tang, L., "An SDN-MQTT Based Communication System for Battlefield UAV Swarms," in IEEE Communications Magazine, vol. 57, no. 8, August 2019, pp. 41-47, doi: 10.1109/MCOM.2019.1900291.
  • Shurrab, M., Mizouni, R., Singh, S., & Otrok, H., Reinforcement learning framework for UAV-based target localization applications. Internet of Things, 23, 100867, 2023.
  • Choi, H.-H., Oh, J., Kang, K.-M., & Lee, H., "Idle-Less Slotted ALOHA Protocol for Drone Swarm Identification," in IEEE Transactions on Vehicular Technology, vol. 72, no. 8, Aug. 2023, pp. 11080-11085, doi: 10.1109/TVT.2023.3261104.
  • Chang, H., Chen, Y., Zhang, B., & Doermann, D., "Multi-uav mobile edge computing and path planning platform based on reinforcement learning", IEEE Transactions on Emerging Topics in Computational Intelligence, 2021.
  • Liu, X., Lam, K., Alkouz, B., Shahzaad, B., & Bouguettaya, A., "Constraint-based Formation of Drone Swarms," 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), Pisa, Italy, 2022, pp. 73-75, doi: 10.1109/PerComWorkshops53856.2022.9767410.
  • Song, Q., Zeng, Y., Xu, J. et al., A survey of prototype and experiment for UAV communications. Sci. China Inf. Sci. 64, 140301, 2021. https://doi.org/10.1007/s11432-020-3030-2
  • Douklias, A., Karagiannidis, L., Misichroni, F., & Amditis, A., Design and implementation of a UAV-based airborne computing platform for computer vision and machine learning applications, Sensors, 22(5), 2049, 2022.
  • Shi, Y., Wensowitch, J., Ward, A., Badi, M., & Camp, J., "Building UAV-Based Testbeds for Autonomous Mobility and Beamforming Experimentation," 2018 IEEE International Conference on Sensing, Communication and Networking (SECON Workshops), Hong Kong, China, 2018, pp. 1-5, doi: 10.1109/SECONW.2018.8396345.
  • Sommer, D., Irigireddy, A.S.C.R., Parkhurst, J., & Nastrucci, E.-R, "SDR- and UAV-Based Wireless Avionics Intra-Communication Testbed," 2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC), San Antonio, TX, USA, 2020, pp. 1-5, doi: 10.1109/DASC50938.2020.9256639.
  • Enhos, K., Unal, D., Turco, J., Demirors, E., & Melodia, T., Marena: Sdr-based testbed for underwater wireless communication and networking research. In Proceedings of The 17th ACM Workshop on Wireless Network Testbeds, Experimental evaluation & Characterization, 2023, October, pp. 80-87.
  • Baumgärtner, L., Bauer, M., & Bloessl, B., SUN: A Simulated UAV Network Testbed with Hardware-in-the-Loop SDR Support. In 2023 IEEE Wireless Communications and Networking Conference (WCNC), 2023, March, pp. 1-6.
  • Lahoud, C., Ehsanfar, S., Gabriel, M., Küffner, P., & Mößner, K., Experimental Testbed Results on LTE/5G-V2I Communication using Software Defined Radio. In ICC 2022-IEEE International Conference on Communications, 2022, May, pp. 2894-2899.
  • McKeown N., Anderson T., Balakrishnan H., Parulkar G., Peterson L., Rexford J., Shenker S. & Turner J.. 2008. OpenFlow: enabling innovation in campus networks. SIGCOMM Comput. Commun. Rev. 38, 2, April 2008, 69–74. https://doi.org/10.1145/1355734.1355746
  • Secinti G., Trotta A., Mohanti S., Di Felice M. & Chowdhury K. R., "FOCUS: Fog Computing in UAS Software-Defined Mesh Networks," in IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 6, pp. 2664-2674, June 2020, doi: 10.1109/TITS.2019.2960305.
  • Sharma, V., Song, F., You, I., & Chao, H. C. (2017). Efficient management and fast handovers in software defined wireless networks using UAVs. IEEE Network, 31(6), 78-85.
  • Oubbati, O. S., Atiquzzaman, M., Ahanger, T. A., & Ibrahim, A. (2020). Softwarization of UAV networks: A survey of applications and future trends. IEEE Access, 8, 98073-98125.
  • Brooke, J. (2013). SUS: a retrospective. Journal of usability studies, 8(2), 29-40.
Toplam 28 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Bilgi Modelleme, Yönetim ve Ontolojiler
Bölüm Araştırma Makaleleri
Yazarlar

Gökhan Seçinti 0000-0003-0640-8368

Erken Görünüm Tarihi 3 Aralık 2024
Yayımlanma Tarihi
Gönderilme Tarihi 27 Kasım 2023
Kabul Tarihi 28 Nisan 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 17 Sayı: 2

Kaynak Göster

IEEE G. Seçinti, “Yazılım-tanımlı İHA Ağları için Deney Ortamı Tasarımı”, bbmd, c. 17, sy. 2, ss. 132–141, 2024, doi: 10.54525/bbmd.1595419.