Enhancement of Drone-as-a-Service Using Blockchain and AI

##plugins.themes.academic_pro.article.sidebar##

Published Nov 18, 2022
https://doi.org/10.47164/ijngc.v13i4.567
Download
Requires Subscription PDF
Statistic

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...
Volume 13, Issue 4, November 2022

##plugins.themes.academic_pro.article.main##

Dharna Nar
K. J. Somaiya Institute of Engineering and Information Technology
Dr. Radhika Kotecha
K. J. Somaiya Institute of Engineering and Information Technology

Abstract

With the rapid technological development of robust and intelligent UAVs (Unmanned Aerial Vehicles), typically referred to as drones, much opportunities have emerged to provide DraaS (Drone-as-a-Service) to help industries such as agriculture, energy and utilities, GIS, package delivery, cinematography, industrial inspection and many more. The capability of drones to lift payload, acquire data with camera and sensors mounted on it make drones as a useful tool for various commercial applications. However, there exist great challenges for executing autonomous missions, operations, management, ensuring safety and secure communications. In this research paper, we review the latest research in the field of Artificial Intelligence and Blockchain applied for DraaS. Blockchain being a distributed ledger protects the shared data using cryptography techniques such as hash functions and public key encryption. It can also be used for assuring the truthfulness of the information stored and for improving the security and transparency of the UAVs. The integration of AI contributes more intelligence to the system enabling informed decision making and eventually converting drones into vehicles capable of executing autonomous missions in the real-world.

##plugins.themes.academic_pro.article.details##

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
Dharna Nar, & Radhika Kotecha. (2022). Enhancement of Drone-as-a-Service Using Blockchain and AI. International Journal of Next-Generation Computing, 13(4). https://doi.org/10.47164/ijngc.v13i4.567
Crossref
Scopus
Google Scholar
Europe PMC

References

  1. Kozera, Cyprian. (2018). Military Use of Unmanned Aerial Vehicles – A Historical Study. Safety & Defense. 4. 17-21. 10.37105/sd.4. DOI: https://doi.org/10.37105/sd.4
  2. Zheng, Zibin & Xie, Shaoan & Dai, Hong-Ning & Chen, Xiangping & Wang, Huaimin. (2017). An Overview of Blockchain Technology: Architecture, Consensus, and Future Trends. 10.1109/BigDataCongress.2017.85. DOI: https://doi.org/10.1109/BigDataCongress.2017.85
  3. Abdelkader, M., Güler, S., Jaleel, H., & Shamma, J.S. (2021). Aerial Swarms: Recent Applications and Challenges. Current Robotics Reports, 1 - 12. DOI: https://doi.org/10.1007/s43154-021-00063-4
  4. Lanteigne, A., Kibru, E., Azam, S., & Shammary, S. Al. Design of a Drone Light-Show Production System.
  5. S. Nakamoto et al., “Bitcoin: A peer-to-peer electronic cash system,” 2008.
  6. Alladi, T., Chamola, V., Sahu, N., & Guizani, M. (2020). Applications of blockchain in unmanned aerial vehicles: A review. Vehicular Communications, 23(February), 0–28. DOI: https://doi.org/10.1016/j.vehcom.2020.100249
  7. Fernández-Caramés, T. M., Blanco-Novoa, O., Froiz-Míguez, I., & Fraga-Lamas, P. (2019). Towards an Autonomous Industry 4.0 Warehouse: A UAV and Blockchain-Based System for Inventory and Traceability Applications in Big Data-Driven Supply Chain Management. Sensors (Basel, Switzerland), 19(10). DOI: https://doi.org/10.3390/s19102394
  8. Alsamhi, S. H., Lee, B., Guizani, M., Kumar, N., Qiao, Y., & Liu, X. (2021). Blockchain for Decentralized Multi-Drone to Combat COVID-19. Transactions on Emerging Telecommunication Technologies. DOI: https://doi.org/10.1002/ett.4255
  9. Dawaliby, S., Aberkane, A., & Bradai, A. (2020). Blockchain-based IoT platform for autonomous drone operations management. DroneCom 2020 - Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond, 31–36. DOI: https://doi.org/10.1145/3414045.3415939
  10. Ersson, L., & Olsson, E. (2020). Drones to the Rescue A literary study of Unmanned Aerial Systems within healthcare.
  11. Eichleay, M., Evens, E., Stankevitz, K., & Parker, C. (2019). Using the Unmanned Aerial Vehicle Delivery Decision Tool to Consider Transporting Medical Supplies via Drone. Global Health, Science and Practice, 7(4), 500–506. DOI: https://doi.org/10.9745/GHSP-D-19-00119
  12. Thiels, C. A., Aho, J. M., Zietlow, S. P., & Jenkins, D. H. (2015). Use of unmanned aerial vehicles for medical product transport. Air Medical Journal, 34(2), 104–108. DOI: https://doi.org/10.1016/j.amj.2014.10.011
  13. U.S. Department of Transport. (2020). Blockchain for Unmanned Aircraft Systems.
  14. Bandala, A.A., Dadios, E.P., Vicerra, R.R., & Lim, L.A. (2014). Swarming Algorithm for Unmanned Aerial Vehicle (UAV) Quadrotors - Swarm Behavior for Aggregation, Foraging, Formation, and Tracking. J. Adv. Comput. Intell. Intell. Informatics, 18, 745-751. DOI: https://doi.org/10.20965/jaciii.2014.p0745
  15. Belkadi, A., Ciarletta, L., & Theilliol, D. (2015). Particle Swarm Optimization Method for the Control of a Fleet of Unmanned Aerial Vehicles. Journal of Physics: Conference Series, 659(1). DOI: https://doi.org/10.1088/1742-6596/659/1/012015
  16. Chowdhury, S., Marufuzzaman, M., Tunc, H., Bian, L., & Bullington, W. (2019). A Modified Ant Colony Optimization algorithm to Solve a Dynamic Traveling Salesman Problem : A Case Study with Drones for Wildlife Surveillance. Journal of Computational Design and Engineering, 6(3), 368–386. DOI: https://doi.org/10.1016/j.jcde.2018.10.004
  17. Gonzalez, L.F.; Montes, G.A.; Puig, E.; Johnson, S.; Mengersen, K.; Gaston, K.J. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence Revolutionizing Wildlife Monitoring and Conservation. Sensors 2016, 16, 97. DOI: https://doi.org/10.3390/s16010097
  18. Muñoz, G., Barrado, C., Çetin, E., & Salami, E. (2019). Deep reinforcement learning for drone delivery. Drones, 3(3) DOI: https://doi.org/10.3390/drones3030072
  19. Aziez, S.A.; Al-Hemeary, N.; Reja, A.H.; Zsedrovits, T.; Cserey, G. Using KNN Algorithm Predictor for Data Synchronization of Ultra-Tight GNSS/INS Integration. Electronics 2021, 10, 1513. DOI: https://doi.org/10.3390/electronics10131513
  20. Gupta R, Kumari A, Tanwar S.(2021) Fusion of blockchain and artificial intelligence for secure drone networking underlying 5G communications. Trans Emerging Tel Tech. 2021;32:e4176. DOI: https://doi.org/10.1002/ett.4176
  21. Kumar, M. S., Vimal, S., Jhanjhi, N. Z., Sundar, S., & Alhumyani, H. A. (2021). Blockchain based peer to peer communication in autonomous drone operation. Energy Reports, 7, 7925–7939. DOI: https://doi.org/10.1016/j.egyr.2021.08.073
  22. Silva, L., & Magaia, N. (2021). Blockchain-Based Solutions for UAV-Assisted Connected Vehicle Networks in Smart Cities : A Review , Open Issues , and Future Perspectives. 108–140. DOI: https://doi.org/10.3390/telecom2010008

Most read articles by the same author(s)