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Membraninių skaičiavimų ir daiktų interneto technologijos / Membrane computing and internet of things technologies

Abstract

The Internet of Things collects a variety of sensor data, independently offers problem-solving solutions and allows them to be avoided. In order for these systems to function continuously, it is necessary to apply intelligent information routing algorithms. In order to discover new algorithms for the routing of the Internet of Things, this article reviews bioinspired algorithms, their advantages and disadvantages. We introduce membrane computing, P system and its membrane structure. Paper analyses different types of communication on the Internet of Things and classification of routing algorithms for the Internet of Things communication. The practical application of membrane computing and the possibility of applying membrane computing on the Internet of Things is also reviewed.


Santrauka


Daiktų internetas renka įvarius jutiklių duomenis, savarankiškai siūlo problemų sprendimus ir leidžia jų išvengti. Kad šios sistemos veiktų nenutrūkstamai, būtina taikyti intelektualiuosius informacijos valdymo ir skirstymo algoritmus. Siekiant atrasti naujus daiktų interneto informacijos skirstymo ir valdymo algoritmus, šiame straipsnyje apžvelgiami biotechnologiniai algoritmai ir jų taikymo privalumai bei trūkumai. Pristatomi membraniniai skaičiavimai, P sistema ir jos membraninė struktūra. Apžvelgiami komunikacijos tipai daiktų internete ir klasifikuojami informacijos skirstymo algoritmai, skirti daiktų interneto komunikacijai. Taip pat apžvelgiamas praktinis membraninių skaičiavimų taikymas ir galimybė taikyti membraninius skaičiavimus daiktų internete.


Reikšminiai žodžiai: daiktų internetas, membraniniai skaičiavimai, P sistema, gamtiniai skaičiavimai.

Keyword : internet of things, membrane computing, P system, natural computing

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Apr 18, 2019
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References

Abowd, G. D., et al. (1999). Towards a better understanding of context and context-awareness. In H.-W. Gellersen, Handheld and ubiquitous computing. Berlin, Heidelberg, Springer Berlin Heidelberg (pp. 304-307). ISBN 978-3-540-48157-7. https://doi.org/10.1007/3-540-48157-5_29

Al-Fuqaha, A. I., et al. (2015). Internet of things, a survey on enabling technologies, protocols, and applications. IEEE Communications Surveys and Tutorials, 17(4), 2347-2376. https://doi.org/10.1109/COMST.2015.2444095

Ardelean, I. I., & Cavaliere, M. (2003). Modelling biological processes by using a probabilistic P system software. Natural Computing, 2(2), 173-197. ISSN 1572-9796. https://doi.org/10.1023/A:1024943605864

Atakan, B., & Akan, O. B. (2007). Biologically-inspired spectrum sharing in cognitive radio networks. In 2007 IEEE Wireless Communications & Networking Conference, 1-9, 43-48. ISSN 1525-3511. https://doi.org/10.1109/WCNC.2007.14

Beale, R., & Jackson, T. (1990). Neural computing – an introduction. Institute of Physics Publishing (240 p.). ISBN 0852742622. https://doi.org/10.1887/0852742622

Bello, O., & Zeadally, S. (2013). Communication Issues in the Internet of Things (IoT). Next-Generation Wireless Technologies, 4G and Beyond. London, Springer London (pp. 189-219). https://doi.org/10.1007/978-1-4471-5164-7_10

Bello, O., & Zeadally, S. (2016). Intelligent device-to-device communication in the internet of things. IEEE Systems Journal, 10(3), 1172-1182. ISSN 1932-8184. https://doi.org/10.1109/JSYST.2014.2298837

Ciobanu, G., et al. (2005). Applications of membrane computing (Natural Computing Series). Springer-Verlag. ISBN 978-3-540-29937-0. https://doi.org/10.1007/3-540-29937-8

Das, M. K., & Dai, H. K. (2007). A survey of DNA motif finding algorithms. Bmc Bioinformatics, 8, 13. ISSN 1471-2105. https://doi.org/10.1186/1471-2105-8-S7-S21

Davis, G. (2018). 2020: Life with 50 billion connected devices. In 2018 IEEE International Conference on Consumer Electronics (ICCE). Las Vegas, NV. https://doi.org/10.1109/ICCE.2018.8326056

De Poorter, E., Moerman, I., & Demeester, P. (2011). Enabling direct connectivity between heterogeneous objects in the internet of things through a network-service-oriented architecture. EURASIP Journal on Wireless Communications and Networking, 2011(1), 61. ISSN 1687-1499. https://doi.org/10.1186/1687-1499-2011-61

Di Caro, G., Ducatelle, F., & Gambardella, L. M. (2005). AntHocNet, an adaptive nature-inspired algorithm for routing in mobile ad hoc networks. European Transactions on Telecommunications, 16(5), 443-455. https://doi.org/10.1002/ett.1062

Dressler, F., & Akan, O. B. (2010). A survey on bio-inspired networking. Computer Networks, 54(6), 881-900. ISSN 1389-1286. https://doi.org/10.1016/j.comnet.2009.10.024

Farooq, M., & Di Caro, G. A. (2008). Routing protocols for next-generation networks inspired by collective behaviors of insect societies (An overview). Swarm Intelligence, Introduction and Applications. C. Blum & D. Merkle. Berlin, Heidelberg, Springer Berlin Heidelberg (pp. 101-160). https://doi.org/10.1007/978-3-540-74089-6_4

Gao, S. C., et al. (2016). Ant colony optimization with clustering for solving the dynamic location routing problem, Applied Mathematics and Computation, 285, 149-173. ISSN 0096-3003. https://doi.org/10.1016/j.amc.2016.03.035

Hunter, L. (1993). Molecular biology for computer scientists. Artificial intelligence and molecular biology. H. Lawrence, American Association for Artificial Intelligence (pp. 1-46).

Klugl, F. (2001). Swarm intelligence, from natural to artificial systems. Jasss-the Journal of Artificial Societies and Social Simulation, 4(1), U153-U156. ISSN 1460-7425.

Liotta, A. (2013). Why the internet needs cognitive protocols. New York, NY, USA, IEEE Spectrum. Retrieved from https://spectrum.ieee.org/computing/networks/why-the-internet-needs-cognitive-protocols

Liton, M. (2018). How much data comes from the internet of things?. Machine data and analytics. Retrieved from https://www.sumologic.com/blog/machine-data-analytics/iot-devices-data-volume

Lott, C., & Teneketzis, D. (2006). Stochastic routing in ad-hoc networks. IEEE Transactions on Automatic Control, 51(1), 52-70. ISSN 0018-9286. https://doi.org/10.1109/TAC.2005.860280

Machado, K., et al. (2013). A routing protocol based on energy and link quality for internet of things applications. Sensors, 13(2), 1942-1964. ISSN 1424-8220. https://doi.org/10.3390/s130201942

Martin-Vide, C., et al. (2003). Tissue P systems. Theoretical Computer Science, 296(2), 295-326. ISSN 0304-3975. https://doi.org/10.1016/S0304-3975(02)00659-X

Minerva, R., Biru, A., & Rotondi, D. (2015). Towards a definition of the Internet of Things (IoT). IEEE Internet Initiative. Retrieved from https://iot.ieee.org/images/files/pdf/IEEE_IoT_Towards_Definition_Internet_of_Things_Revision1_27MAY15.pdf

Musolesi, M., & Mascolo, C. (2006). Evaluating context information predictability for autonomic communication. In Proceedings of the 2006 International Symposium on on World of Wireless, Mobile and Multimedia Networks. IEEE Computer Society, Buffalo-Niagara Falls, NY, (pp. 495-499). https://doi.org/10.1109/WOWMOM.2006.41

Nasser, N., et al. (2017). Routing in the internet of things. In 2017 IEEE Global Communications Conference. New York, IEEE. https://doi.org/10.1109/GLOCOM.2017.8253955

López-Matencio, P. J. V.-A., & Costa-Montenegro, E. (2017). ANT: agent stigmergy-based iot-network for enhanced tourist mobility. Mobile Information Systems, 2017, 15. https://doi.org/10.1155/2017/1328127

Paun, G. (2000). Computing with membranes. Journal of Computer and System Sciences, 61(1), 108-143. ISSN 0022-0000. https://doi.org/10.1006/jcss.1999.1693

Paun, G., & Rozenberg, G. (2002). A guide to membrane computing. Theoretical Computer Science, 287(1), 73-100. ISSN 0304-3975. https://doi.org/10.1016/S0304-3975(02)00136-6

Paun, G., Rozenberg, G., & Salomaa, A. (2010). The Oxford handbook of membrane computing. Oxford University Press, Inc. (696 p.). ISBN 0199556679, 9780199556670. https://doi.org/10.1007/978-3-642-11467-0

Zhang, G. (2017). Real-life applications with membrane computing. New York, NY, Springer Berlin Heidelberg. ISBN 9783319559872. https://doi.org/10.1007/978-3-319-55989-6

Zhang, G. X., et al. (2014). Evolutionary membrane computing, a comprehensive survey and new results. Information Sciences, 279, 528-551. ISSN 0020-0255. https://doi.org/10.1016/j.ins.2014.04.007