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Towards Intelligent Management of Internet of Modern Drones

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Computer and Communication Engineering (CCCE 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1630))

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Abstract

Internet of drones (IoD) provides coordinated access, between drones and users, over the Internet to controlled airspace. With advanced drone, mobile and Artificial Intelligence (AI) technologies, today’s drones are equipped with sophisticated onboard AI software that enhances drone services and our way of life (e.g., package delivery, traffic surveillance). As IoD grows, there is a need to effectively manage large-scaled drones with multiple regulation and resource constraints, particularly energy usage. This paper presents preliminary work on generic architecture and operations to lay foundations for intelligent drone management systems. By also introducing a method to pre-determine estimated energy consumption of deep neural net image analysis deployed in drones, the paper illustrates this work on managing the search rescue drone autonomy to decide on its actions based on energy consumption. The proposed approach can be extended to manage a network of drones and additional resource constraints including response time, safety or environmental compliance and financial budget.

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References

  1. Abualigah, L., Diabat, A., Sumari, P., Gandomi, A.H.: Applications, deployments, and integration of Internet of Drones (IoD): a review. IEEE Sens. J. 21, 25532–25546 (2021). https://doi.org/10.1109/JSEN.2021.3114266

    Article  Google Scholar 

  2. Boccadoro, P., Striccoli, D., Grieco, L.A.: An extensive survey on the Internet of Drones. Ad Hoc Netw. 122, 102600 (2021). https://doi.org/10.1016/j.adhoc.2021.102600

  3. Alsamhi, S.H., Ma, O., Ansari, M.S., Almalki, F.A.: Survey on collaborative smart drones and internet of things for improving smartness of smart cities. IEEE Access 7, 128125–128152 (2019). https://doi.org/10.1109/ACCESS.2019.2934998

    Article  Google Scholar 

  4. Yao, J., Ansari, N.: QoS-aware power control in internet of drones for data collection service. IEEE Trans. Veh. Technol. 68, 6649–6656 (2019). https://doi.org/10.1109/TVT.2019.2915270

    Article  Google Scholar 

  5. Sarkar, S., Khare, S., Totaro, M.W., Kumar, A.: A novel energy aware secure internet of drones design: ESIoD. In: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 1–6 (2021). https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484461

  6. Wang, L., Hu, B., Chen, S.: Energy efficient placement of a drone base station for minimum required transmit power. IEEE Wirel. Commun. Lett. 9, 2010–2014 (2020). https://doi.org/10.1109/LWC.2018.2808957

    Article  Google Scholar 

  7. Yang, T.J., Chen, Y.H., Emer, J., Sze, V.: A method to estimate the energy consumption of deep neural networks. In: Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017, 2017 October, pp. 1916–1920 (2018). https://doi.org/10.1109/ACSSC.2017.8335698

  8. Russell, S., Norvig, P.: AI a modern approach. Learning 2, 4 (2005)

    Google Scholar 

  9. Mozaffari, M., Saad, W., Bennis, M., Debbah, M.: Drone small cells in the clouds: design, deployment and performance analysis. In: 2015 IEEE Global Communications Conference (GLOBECOM), pp. 1–6 (2015). https://doi.org/10.1109/GLOCOM.2015.7417609

  10. Auer, G., et al.: How much energy is needed to run a wireless network? IEEE Wirel. Commun. 18, 40–49 (2011). https://doi.org/10.1109/MWC.2011.6056691

    Article  Google Scholar 

  11. Gundlach, J., Gundlach, J.: Designing unmanned aircraft systems: a comprehensive approach. American Institute of Aeronautics and Astronautics Reston, VA (2012)

    Google Scholar 

  12. Duangsuwan, S., Maw, M.M.: Comparison of path loss prediction models for UAV and IoT air-to-ground communication system in rural precision farming environment. J. Commun. 16, 60–66 (2021)

    Article  Google Scholar 

  13. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)

    Google Scholar 

  14. Puangpontip, S., Hewett, R.: Energy usage of deep learning in smart cities. In: Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020, pp. 1143–1148 (2020). https://doi.org/10.1109/CSCI51800.2020.00214

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Author information

Authors and Affiliations

  1. Texas Tech University, Lubbock, TX, 79409, USA

    Supadchaya Puangpontip & Rattikorn Hewett

Authors
  1. Supadchaya Puangpontip
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  2. Rattikorn Hewett
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Corresponding author

Correspondence to Supadchaya Puangpontip .

Editor information

Editors and Affiliations

  1. University of Naples Federico II, Naples, Italy

    Filippo Neri

  2. Concordia University, Montreal, QC, Canada

    Ke-Lin Du

  3. The University of New South Wales, Sydney, NSW, Australia

    Vijaya Kumar Varadarajan

  4. Miguel Hernandez University, Elche, Spain

    San-Blas Angel-Antonio

  5. Northwestern Polytechnical University, Xi'an, China

    Zhiyu Jiang

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Puangpontip, S., Hewett, R. (2022). Towards Intelligent Management of Internet of Modern Drones. In: Neri, F., Du, KL., Varadarajan, V.K., Angel-Antonio, SB., Jiang, Z. (eds) Computer and Communication Engineering. CCCE 2022. Communications in Computer and Information Science, vol 1630. Springer, Cham. https://doi.org/10.1007/978-3-031-17422-3_1

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  • DOI: https://doi.org/10.1007/978-3-031-17422-3_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-17421-6

  • Online ISBN: 978-3-031-17422-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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