Center for IT R&D

TECHNICAL ABSTRACT

TIAKO_CITRD_TA_2015_0001

Unmanned Aircraft Systems Predictive Analyctics and Perspectives

Jocelyn Nembe and Pierre F. Tiako

November 03, 2015

Due to their passive nature, Unmanned Aircraft Systems (UAS) are remotely controlled and monitored objects inherently different from manned aircraft. So far military UAS have shared our navigational airspace without major incidents nor defined governmental regulation.

The United States under Federal Aviation Administration (FAA) is taking actions to introduce private commercial and recreational UAS in our skies [1]. That is a big challenge from safety, security and liability point of views, knowing that the US has one of the most complex and busiest airspace in the world. FAA and the aviation community are currently taking incremental approaches to safe private UAS integration in our skies.

Dr. Jocelyn Nembe

Prof. Dr. Pierre F. Tiako

Because UAS come to serve diverse purposes, scientists must not only help improve UAS design and shape to ease their integration but also provide UAS with robust military, commercial and recreational capabilities. One of the main features when it comes to capabilities building for the sky is the real-time handling of big data [2]. The main focus of this research is all about UAS survivability concerns and real-time handling of big data for diverse purposes.

UAS survivability concerns include, but not limited to:

• Improve communication speed with remote units [5]
• Survivability concerns [3]
• Independent analysis capabilities [6]

UAS real-time handling of big data includes, but not limited to:

• Predictive Analytics [7]
• Security and surveillance [3, 4]
• Contextual analysis [8]
• Intelligent navigation and decision making [9]

References

[1] The Economic Impact of Aircraft Systems Integration in the United States. Association of Unmanned Aircraft Systems Integration in the United States. March 2013.

[2] Wargo, C.A.; Church, G.C.; Glaneueski, J.; Strout, M., Unmanned Aircraft Systems (UAS) research and future analysis, 2014 IEEE Aerospace Conference, pp.1-16, March 2014

[3] P.F Tiako. Modeling and Distributing the Security and Survivability Concerns of Sensor Networks. 2006 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA ’06)- Workshop on Building Software for Sensor Networks, Sunday, Oct 22, Portland, Oregon, USA

[4] P.F. Tiako and L. Gruenwald. “Collaboration Framework for Data Compensation in Sensor Networks”, Proceedings of 2008 IEEE International Conference on Electro-Information Technology Conference. May 2008.

[5] Rahman, M.A., Enabling drone communications with WiMAX Technology, in IISA 2014, The 5th International Conference on Information, Intelligence, Systems and Applications, pp.323-328, July 2014.

[6] Jimenez Lugo, J.; Zell, A., Framework for autonomous onboard navigation with the AR.Drone, in 2013 International Conference on Unmanned Aircraft Systems (ICUAS), pp.575-583, May 2013

[7] Afzal, U.; Mahmood, T., Using predictive analytics to forecast drone attacks in Pakistan, in 2013 5th International Conference on Information & Communication Technologies (ICICT), pp.1-6, Dec. 2013.

[8] Apvrille, L.; Tanzi, T.; Dugelay, J.-L., Autonomous drones for assisting rescue services within the context of natural disasters, in 2014 XXXIth URSI General Assembly and Scientific Symposium (URSI GASS), pp.1-4, 16-23 Aug. 2014.

[9] Ben Moshe, B.; Shvalb, N.; Baadani, J.; Nagar, I.; Levy, H., Indoor positioning and navigation for micro UAV drones — Work in progress, in 2012 IEEE 27th Convention of Electrical & Electronics Engineers in Israel (IEEEI), pp.1-5, 14-17 Nov. 2012.