access icon openaccess Artificial intelligence in Internet of things

Functioning of the Internet is persistently transforming from the Internet of computers (IoC) to the ‘Internet of things (IoT)’. Furthermore, massively interconnected systems, also known as cyber-physical systems (CPSs), are emerging from the assimilation of many facets like infrastructure, embedded devices, smart objects, humans, and physical environments. What the authors are heading to is a huge ‘Internet of Everything in a Smart Cyber Physical Earth’. IoT and CPS conjugated with ‘data science’ may emerge as the next ‘smart revolution’. The concern that arises then is to handle the huge data generated with the much weaker existing computation power. The research in data science and artificial intelligence (AI) has been striving to give an answer to this problem. Thus, IoT with AI can become a huge breakthrough. This is not just about saving money, smart things, reducing human effort, or any trending hype. This is much more than that – easing human life. There are, however, some serious issues like the security concerns and ethical issues which will go on plaguing IoT. The big picture is not how fascinating IoT with AI seems, but how the common people perceive it – a boon, a burden, or a threat.

Inspec keywords: Internet of Things; artificial intelligence; cyber-physical systems; security of data

Other keywords: IoT; massively interconnected systems; smart revolution; embedded devices; cyber-physical systems; physical environments; smart objects; data science; AI; Smart Cyber Physical Earth; Internet of Things; artificial intelligence

Subjects: Expert systems and other AI software and techniques; Data security; Mobile, ubiquitous and pervasive computing

References

    1. 1)
      • [20]. Ghosh, A., Jain, L.C.: ‘Evolutionary computation in data mining’, in Ghosh, A., Jain, L.C. (Eds.): ‘Studies in fuzziness and soft computing’ (Springer, Berlin, Heidelberg, 2006).
    2. 2)
      • [14]. Zikopoulos, P., Eaton, C.: ‘Understanding big data: analytics for enterprise class hadoop and streaming data’ (McGraw-Hill Osborne Media, New York, USA, 2011).
    3. 3)
    4. 4)
      • [11]. Marz, N., Warren, J.: ‘Big data: principles and best practices of scalable real-time data systems’ (Manning, New York, USA, 2015).
    5. 5)
      • [31]. Jeong, S.: ‘The internet of garbage’ (Forbes Media, New York, USA, 2015).
    6. 6)
    7. 7)
      • [2]. Witten, I.H., Frank, E.: ‘Data mining: practical machine learning tools and techniques’ (Morgan Kaufmann, Burlington, Massachusetts, 2016).
    8. 8)
      • [33]. Yoskovitz, S., Gal, S.: ‘Automatic mechanical system diagnosis’, 22 October 2012. US Patent App. 13/657,037.
    9. 9)
    10. 10)
      • [4]. Lee, E.A., Seshia, S.A.: ‘Introduction to embedded systems: a cyber-physical systems approach’ (MIT Press, Cambridge, Massachusetts, 2016).
    11. 11)
      • [6]. Fortino, G., Trunfio, P.: ‘Internet of things based on smart objects: technology, middleware and applications’ (Springer, New York, USA, 2014).
    12. 12)
      • [10]. Theodoridis, S., Koutroumbas, K.: ‘Pattern recognition’ (Elsevier Science, USA, 2008).
    13. 13)
      • [5]. Hassan, Q.F., Khan, A.R., Madani, S.A.: ‘Internet of things: challenges, advances, and applications. Chapman & Hall/CRC computer and information science series’ (CRC Press, Boca Raton, Florida, 2017).
    14. 14)
      • [17]. Cohn, D.: ‘Active Learning’, in Sammut, C., Webb, G.I. (Eds.): ‘ Encyclopedia of Machine Learning and Data Mining (Springer, New York, USA, 2017), pp. 914.
    15. 15)
      • [21]. Hastie, T., Tibshirani, R., Friedman, J.: ‘The elements of statistical learning: data mining, inference, and prediction’, in Diggle, P., Gather, U., Zeger, S. (Eds.): ‘Springer series in statistics’ (Springer, New York, 2013).
    16. 16)
      • [23]. Kaplan, J.: ‘Artificial intelligence: what everyone needs to know. What everyone needs to know’ (Oxford University Press, Oxford, UK, 2016).
    17. 17)
    18. 18)
    19. 19)
    20. 20)
    21. 21)
      • [22]. Holler, J., Tsiatsis, V., Mulligan, C., et al: ‘From machine-to-machine to the internet of things: introduction to a new age of intelligence’ (Academic Press, Cambridge, UK, 2014).
    22. 22)
      • [32]. Gershenfeld, N.: ‘When things start to think: integrating digital technology into the fabric of our lives’ (Henry Holt and Company, Augury Systems, Haifa, Israel, 2014).
    23. 23)
      • [25]. Appadurai, A.S., Kumar, D.: ‘Performance analysis of ZigBee and OWC in wireless body area network’, Small, 2016, 5, (3), pp. 564567.
    24. 24)
      • [24]. Câmara, D., Nikaein, N.: ‘Wireless public safety networks 2: a systematic approach’ (Elsevier Science, USA, 2016).
    25. 25)
      • [12]. Leskovec, J., Rajaraman, A., Ullman, J.D.: ‘Mining of massive datasets’ (Cambridge University Press, Cambridge, UK, 2014).
    26. 26)
      • [30]. Bor, M., Vidler, J.E., Roedig, U.: ‘Lora for the internet of things’. Proc. 2016 Int. Conf. Embedded Wireless Systems and Networks, EWSN ‘16, Graz, Austria, 2016, pp. 361366.
    27. 27)
      • [1]. Michalski, R.S., Carbonell, J.G., Mitchell, T.M.: ‘Machine learning: an artificial intelligence approach’ (Springer Science & Business Media, Berlin, Germany, 2013).
    28. 28)
    29. 29)
    30. 30)
      • [8]. Baheti, R., Gill, H.: ‘Cyber-physical systems’, Impact Control Technol., 2011, 12, pp. 161166.
    31. 31)
    32. 32)
      • [9]. Gorman, M.M.: ‘Database management systems: understanding and applying database technology’ (Elsevier Science, USA, 2014).
    33. 33)
      • [7]. Yang, L.T., Di Martino, B., Zhang, Q.: ‘Internet of everything’, Mobile Inf. Syst., 2017, 2017, pp. 13.
    34. 34)
      • [27]. Shelby, Z., Bormann, C.: ‘6LoWPAN: the wireless embedded internet, vol. 43’ (John Wiley & Sons, New Jersey, USA, 2011).
http://iet.metastore.ingenta.com/content/journals/10.1049/trit.2018.1008
Loading

Related content

content/journals/10.1049/trit.2018.1008
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading