How Cloud Computing and Business Intelligence support UAV Technology

Nader Shahata

Strategic Cyber Resilience Research and Development Center, National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan

In recent years, the majority of UAVs are implemented using technologies that require a lot of effort, budget and time. This will consume the available resources in an exaggerated manner and will therefore affect the overall performance of the UAV itself. In this paper, we present the use of cloud computing in conjunction with business intelligence for the aim of adding more efficiency to the UAV technology in the high speed era and the use of 5G technology. The recent introduction of the cloud computing is a very good opportunity which can support business intelligence. Through this paper, we will discuss the procedure of mapping cloud computing to the UAVs and the challenges that will be emerged when doing so. Many of these challenges are not limited to the UAVs energy levels, motion, and GPS settings.

UAV, Cloud Computing, Business Intelligence, Knowledge Management, Internet

Nader Shahata (2022). How Cloud Computing and Business Intelligence support UAV Technology. Journal of Networking and Network Applications, Volume 2, Issue 3, pp. 116–119. https://doi.org/10.33969/J-NaNA.2022.020303.

[1] Ranjit Bose. Advanced analytics: opportunities and challenges. Indus-trial Management & Data Systems, 2009.

[2] Rajkumar Buyya, Chee Shin Yeo, and Srikumar Venugopal. Market-oriented cloud computing: Vision, hype, and reality for delivering it services as computing utilities. In 2008 10th IEEE international conference on high performance computing and communications, pages 5–13. Ieee, 2008.

[3] Grzegorz Chmaj and Henry Selvaraj. Distributed processing applications for uav/drones: a survey. In Progress in Systems Engineering, pages 449–454. Springer, 2015.

[4] Fausto G Costa, Jo´ Ueyama, Torsten Braun, Gustavo Pessin, Fernando S Os´orio, and Patr´ıcia A Vargas. The use of unmanned aerial vehicles and wireless sensor network in agricultural applications. In 2012 IEEE International Geoscience and Remote Sensing Symposium, pages 5045–5048. IEEE, 2012.

[5] Kemal A Delic and Martin Anthony Walker. Emergence of the academic computing clouds. Ubiquity, 2008(August):1–1, 2008.

[6] Nikos Kalatzis, Marios Avgeris, Dimitris Dechouniotis, Konstantinos Papadakis-Vlachopapadopoulos, Ioanna Roussaki, and Symeon Papavas-siliou. Edge computing in iot ecosystems for uav-enabled early fire detection. In 2018 IEEE international conference on smart computing (SMARTCOMP), pages 106–114. IEEE, 2018.

[7] Baichuan Liu, Weikun Zhang, Wuhui Chen, Huawei Huang, and Song Guo. Online computation offloading and traffic routing for uav swarms in edge-cloud computing. IEEE Transactions on Vehicular Technology, 69(8):8777–8791, 2020.

[8] Chunbo Luo, James Nightingale, Ekhorutomwen Asemota, and Christos Grecos. A uav-cloud system for disaster sensing applications. In 2015 IEEE 81st Vehicular Technology Conference (VTC Spring), pages 1–5. IEEE, 2015.

[9] Sara Mahmoud, Imad Jawhar, Nader Mohamed, and Jie Wu. Uav and wsn softwarization and collaboration using cloud computing. In 2016 3rd Smart Cloud Networks & Systems (SCNS), pages 1–8. IEEE, 2016.

[10] Sara Mahmoud and Nader Mohamed. Collaborative uavs cloud. In 2014 international conference on unmanned aircraft systems (ICUAS), pages 365–373. IEEE, 2014.

[11] Gervasio Varela, Pilar Caama˜no, Felix Orjales, Alvaro Deibe, Fernando Lopez-Pena, and Richard J Duro. Swarm intelligence based approach for real time uav team coordination in search operations. In 2011 Third World Congress on Nature and Biologically Inspired Computing, pages 365–370. IEEE, 2011.

[12] Jingjing Wang, Chunxiao Jiang, Zuyao Ni, Sanghai Guan, Shui Yu, and Yong Ren. Reliability of cloud controlled multi-uav systems for on-demand services. In GLOBECOM 2017-2017 IEEE Global Communications Conference, pages 1–6. IEEE, 2017.