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The Revolutionary Internet of Things Read chapter Read first chapter

2018 | OriginalPaper | Chapter

3. Agile Deep Learning UAVs Operating in Smart Spaces: Collective Intelligence Versus “Mission-Impossible”

Authors : Michael Cochez, Jacques Periaux, Vagan Terziyan, Tero Tuovinen

Published in: Computational Methods and Models for Transport

Publisher: Springer International Publishing

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Abstract

The environments, in which we all live, are known to be complex and unpredictable. The complete discovery of these environments aiming to take full control over them is a “mission-impossible”, however, still in our common agenda. People intend to make their living spaces smarter utilizing innovations from the Internet of Things and Artificial Intelligence. Unmanned aerial vehicles (UAVs) as very dynamic, autonomous and intelligent things capable to discover and control large areas are becoming important “inhabitants” within existing and future smart cities. Our concern in this paper is to challenge the potential of UAVs in situations, which are evolving fast in a way unseen before, e.g., emergency situations. To address such challenges, UAVs have to be “intelligent” enough to be capable to autonomously and in near real-time evaluate the situation and its dynamics. Then, they have to discover their own missions and set-up suitable own configurations to perform it. This configuration is the result of flexible plans which are created in mutual collaboration. Finally, the UAVs execute the plans and learn from the new experiences for future reuse. However, if to take into account also the Big Data challenge, which is naturally associated with the smart cities, UAVs must be also “wise” in a sense that the process of making autonomous and responsible real-time decisions must include continuous search for a compromise between efficiency (acceptable time frame to get the decision and reasonable resources spent for that) and effectiveness (processing as much of important input information as possible and to improve the quality of the decisions). To address such a “skill” we propose to perform the required computations using Cloud Computing enhanced with Semantic Web technologies and potential tools (“agile” deep learning) for compromising, such as, e.g., focusing, filtering, forgetting, contextualizing, compressing and connecting.

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Literature
Abadi DJ, Marcus A, Madden SR, Hollenbach K (2007) Scalable semantic web data management using vertical partitioning. In: Proceedings of the 33rd international conference on very large data bases. VLDB’07. VLDB Endowment, pp. 411–422
Akerkar R (ed) (2013) Big data computing. Taylor & Francis group-Chapman and Hall/CRC
Atmosukarto I, Ghanem B, Ahuja N (2012) Trajectory-based Fisher kernel representation for action recognition in videos. In: 2012 21st international conference on pattern recognition (ICPR), pp 3333–3336
Baader F, Calvanese D, McGuinness DL, Nardi D, Patel-Schneider PF (eds) (2003) The description logic handbook: theory, implementation, and applications. Cambridge University Press, CambridgeMATH
Berners-Lee T, Hendler J, Lassila O et al (2001) The semantic web. Scientific american 284(5):28–37CrossRef
Broekstra J, Kampman A, Harmelen Van F (2002) Sesame: a generic architecture for storing and querying rdf and rdf schema. In: The Semantic Web—ISWC 2002, pp 54–68
Buyya R, Yeo CS, Venugopal S, Broberg J, Brandic I (2009) Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Futur Gener Comput Syst 25(6):599–616CrossRef
Cochez M (2012) Semantic agent programming language: use and formalization
Cochez M, Periaux J, Terziyan V, Kamlyk K, Tuovinen T (2014) Evolutionary cloud for cooperative UAV coordination. Software and Computational Engineering, No. C, Reports of the Department of Mathematical Information Technology, Series C, p 1
Damjanovic V, Kurz T, Westenthaler R, Behrendt W, Gruber A, Schaffert S (2011) Semantic enhancement: the key to massive and heterogeneous data pools. In: Proceeding of the 20th international IEEE ERK (electrotechnical and computer science) conference
Darwin C (1859) On the origins of species by means of natural selection. Murray, London
Dean D, Webb C (2011) Recovering from information overload. McKinsey Quarterly
Deelman E, Gannon D, Shields M, Taylor I (2009) Workflows and e-Science: an overview of workflow system features and capabilities. Futur Gener Comput Syst 25(5):528–540CrossRef
Dias MB, Zlot R, Kalra N, Stentz A (2006) Market-based multirobot coordination: a survey and analysis. Proc IEEE 94(7):1257–1270CrossRef
Doherty P, Heintz F, Kvarnström J (2013) High-level mission specification and planning for collaborative unmanned aircraft systems using delegation. Unmanned Syst 01(01):75–119CrossRef
Ermolayev V, Akerkar R, Terziyan V, Cochez M (2013) Chap. Towards evolving knowledge ecosystems for big data understanding. In: Akerkar R (ed) Big data computing (2013)
Evangelinos C, Hill C (2008) Cloud Computing for parallel Scientific HPC applications: feasibility of running coupled atmosphere-ocean climate models on Amazon’s EC2. Ratio, 2(2.40):2–34
Fayyad UM, Piatetsky-Shapiro G, Smyth P, et al (1996) Knowledge discovery and data mining: towards a unifying framework, vol. 96. KDD, pp 82–88
Ferber J (1999) Multi-agent systems: an introduction to distributed artificial intelligence, vol. 1. Addison-Wesley Reading
Fok K-L, Wong T-T, Wong M-L (2007) Evolutionary computing on consumer graphics hardware. IEEE Intell Syst 22(2):69–78CrossRef
Gonzalez LF, Lee D-S, Walker RA, Periaux J (2009) Optimal mission path planning (MPP) for an air sampling unmanned aerial system. In: Scheding S (ed) 2009 Australasian conference on robotics & automation. Robotics & Automation Association, Sydney, Australian, pp 1–9
Ito T, Chakraborty S, Kanamori R, Otsuka T (2012) Innovating multiagent algorithms for smart city: an overview. In: 2012 5th IEEE international conference on service-oriented computing and applications (SOCA), pp 1–8
Jackson KR, Ramakrishnan L, Muriki K, Canon S, Cholia S, Shalf J, Wasserman HJ, Wright NJ (2010) Performance analysis of high performance computing applications on the amazon web services cloud. In: 2010 IEEE second international conference on cloud computing technology and science (CloudCom). IEEE, pp 159–168
Kang K, Prasad JVR (2013) Development and flight test evaluations of an autonomous obstacle avoidance system for a rotary-wing UAV. Unmanned Syst 01(01):3–19CrossRef
Katasonov A, Kaykova O, Khriyenko O, Nikitin S, Terziyan V (2008) Smart semantic middleware for the internet of things. In: Proceedings of the 5-th international conference on informatics in control, automation and robotics, pp 11–15
Kopeikin AN, Ponda SS, Johnson LB, How JP (2013) Dynamic mission planning for communication control in multiple unmanned aircraft teams. Unmanned Syst 01(01):41–58CrossRef
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444CrossRef
Lee CA (2010) A perspective on scientific cloud computing. In: Proceedings of the 19th ACM international symposium on high performance distributed computing. ACM, pp 451–459
Li Z, Chen C, Wang K (2011) Cloud computing for agent-based urban transportation systems. IEEE Intell Syst 26(1):73–79CrossRef
Matus R (2016) Neurala becomes largest supplier of deep learning software running in real time on a drone. (Sept 28)
Mell P, Grance T (2009) The NIST definition of cloud computing, version 15. Information Technology Laboratory. National Institute of Standards and Technology (NIST). http://​www.​csrc.​nist.​gov
Nardi D, Brachman RJ (2003) The description logic handbook: theory, implementation, and applications. In: Chap. An introduction to description logics, pp 1–40
Nash J (1951) Non-cooperative games. In: Annals of mathematics, pp 286–295
Nimmagadda Y, Kumar K, Lu Y-H, Lee CSG (2010) Real-time moving object recognition and tracking using computation offloading. In: 2010 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 2449–2455
Ostermann S, Iosup A, Yigitbasi N, Prodan R, Fahringer T, Epema D (2010) A performance analysis of EC2 cloud computing services for scientific computing. In: Cloud computing. Springer, pp 115–131
Pathak PH, Dutta R (2011) A survey of network design problems and joint design approaches in wireless mesh networks. IEEE Commun Surv Tutor 13(3):396–428CrossRef
Peng D, Dabek F (2010) Large-scale incremental processing using distributed transactions and notifications. In: Proceedings of the 9th USENIX symposium on operating systems design and implementation
Petillot Y, Sotzing C, Patron P, Lane D, Cartright J (2009) Multiple system collaborative planning and sensing for autonomous platforms with shared and distributed situational awareness. In: Proceedings of the UAVSI’s unmanned systems Europe
Sadashiv N, Kumar SMD (2011) Cluster, grid and cloud computing: a detailed comparison. In: 2011 6th international conference on computer science & Education (ICCSE). IEEE, pp 477–482
Simmhan Y, van Ingen C, Subramanian G, Li J (2010) Bridging the gap between desktop and the cloud for eScience applications. In: 2010 IEEE 3rd international conference on cloud computing (CLOUD). IEEE, pp 474–481
Song Z (2016) Cloud-based control system for unmanned aerial vehicles. US Patent App. 15/046,560. (Feb18)
Sorokin A, Forsyth D (2008) Utility data annotation with amazon mechanical turk. In: IEEE computer society conference on computer vision and pattern recognition workshops, 2008. CVPRW’08, pp 1–8
Sycara K, Norman TJ, Giampapa JA, Kollingbaum MJ, Burnett C, Masato D, McCallum M, Strub MH (2010) Agent support for policy-driven collaborative mission planning. Comput J 53(5):528–540CrossRef
Taleb NN (2010) The black swan: the impact of the highly improbable fragility. Random House Digital, Inc
Terziyan V (2008) SmartResource-Proactive self-maintained resources in semantic web: Lessons learned. Int J Smart Home 2(2):33–57
Trigui S, Koubaa A, Ben Jamaa M, Chaari I, Al-Shalfan K (2012) Coordination in a multi-robot surveillance application using Wireless Sensor Networks. In: 2012 16th IEEE mediterranean electrotechnical conference (MELECON), pp 989–992
Vecchiola C, Pandey S, Buyya R (2009). High-performance cloud computing: a view of scientific applications. In: 2009 10th international symposium on pervasive systems, algorithms, and networks (ISPAN). IEEE, pp 4–16
Walker E (2008) Benchmarking Amazon EC2 for high-performance scientific computing. Usenix Login 33(5):18–23
Wang K, Shen Z (2011) Artificial societies and GPU-based cloud computing for intelligent transportation management. IEEE Intell Syst 26(4):22–28MathSciNetCrossRef
Zhang Q, Cheng L, Boutaba R (2010) Cloud computing: state-of-the-art and research challenges. J Internet Serv Appl 1(1):7–18CrossRef
Zikopoulos PC, Eaton C, deRoos D, Deutsch T, Lapis G (2012) Understanding big data. Mc Graw Hill
Metadata
Title
Agile Deep Learning UAVs Operating in Smart Spaces: Collective Intelligence Versus “Mission-Impossible”
Authors
Michael Cochez
Jacques Periaux
Vagan Terziyan
Tero Tuovinen
Copyright Year
2018
Book
Computational Methods and Models for Transport

Print ISBN: 978-3-319-54489-2

Electronic ISBN: 978-3-319-54490-8

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-54490-8_3

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