EMERGING TRENDS AND FUTURE COMPUTING TECHNOLOGIES: A VISION FOR SMART ENVIRONMENT

Article Sidebar

PDF
Published: May 2, 2018
DOI: https://doi.org/10.26483/ijarcs.v9i2.5838
Keywords:
emerging trends, future computing technologies, edge computing, artificial general intelligence, deep learning, digital twin.

Main Article Content

Deepti Sehrawat
Maharshi Dayanand University, Rohtak, Haryana (India)
Nasib Singh Gill
Maharshi Dayanand University, Rohtak, Haryana (India)

Abstract

With the rapid technology augmentation, it becomes necessary to find complementary emerging computing technologies. This paper highlights future computing technologies, emerging trends and industry buzz to identify most prominent technologies in India. In the emerging technologies, the market is perceiving the entry of local vendors covering such areas as the Internet of Things (IoT), Robotic Process Automation offerings and Machine Learning based technologies. Some technologies are of transformational nature and results in the foundation of new ecosystem these are, Internet of Things with its associated applications and Machine Learning. Technologies on innovation trigger take more time for wide market acceptance. Main objective of this paper is to present a future vision for smart environment which can provide knowledge accumulation and new directions to new researchers in the related field.

Downloads

Download data is not yet available.

Article Details

Issue
Vol. 9 No. 2 (2018): March-April 2018
Section
Articles

COPYRIGHT

Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
  • The journal allows the author(s) to retain publishing rights without restrictions.
  • The journal allows the author(s) to hold the copyright without restrictions.
Author Biographies

Deepti Sehrawat, Maharshi Dayanand University, Rohtak, Haryana (India)

Research Scholar (Department of Computer Science & Applications Maharshi Dayanand University, Rohtak, Haryana (India)

Nasib Singh Gill, Maharshi Dayanand University, Rohtak, Haryana (India)

Professor (Department of Computer Science & Applications Maharshi Dayanand University, Rohtak, Haryana (India)

References

Lu, Y. (2017). Industry 4.0: a survey on technologies, applications and open research issues. Journal of Industrial Information Integration, 6, 1-10.

Hahanov, V., Gharibi, W., Man, K. L., Iemelianov, I., Liubarskyi, M., Abdullayev, V., ... & Chumachenko, S. (2018). Cyber-Physical Technologies: Hype Cycle 2017. In Cyber Physical Computing for IoT-driven Services (pp. 259-272). Springer, Cham. doi: https://doi.org/10.1007/978-3-319-54825-8_14

Singh, K. J., & Kapoor, D. S. (2017). Create Your Own Internet of Things: A survey of IoT platforms. IEEE Consumer Electronics Magazine, 6(2), 57-68. doi: 10.1109/MCE.2016.2640718

Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future generation computer systems, 29(7), 1645-1660. of Things: A survey of IoT platforms. IEEE Consumer Electronics Magazine, 6(2), 57-68. doi: https://doi.org/10.1016/j.future.2013.01.010

Yaqoob, I., Ahmed, E., Hashem, I. A. T., Ahmed, A. I. A., Gani, A., Imran, M., & Guizani, M. (2017). Internet of things architecture: Recent advances, taxonomy, requirements, and open challenges. IEEE wireless communications, 24(3), 10-16. doi: 10.1109/MWC.2017.1600421

Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637-646. Available Online: 10.1109/JIOT.2016.2579198

Shi, W., & Dustdar, S. (2016). The promise of edge computing. Computer, 49(5), 78-81. doi: 10.1109/MC.2016.145

Satyanarayanan, M. (2017). The emergence of edge computing. Computer, 50(1), 30-39. doi: 10.1109/MC.2017.9

Greenwald, S., Kulik, A., Kunert, A., Beck, S., Frohlich, B., Cobb, S., ... & Snyder, A. (2017). Technology and applications for collaborative learning in virtual reality. doi: https://repository.isls.org/handle/1/210

Rao, B., Gopi, A. G., & Maione, R. (2016). The societal impact of commercial drones. Technology in Society, 45, 83-90. doi: https://doi.org/10.1016/j.techsoc.2016.02.009

Hong, I., Kuby, M., & Murray, A. (2017). A deviation flow refueling location model for continuous space: a commercial drone delivery system for urban areas. In Advances in Geocomputation (pp. 125-132). Springer, Cham.

Akyildiz, I. F., Lin, S. C., & Wang, P. (2015). Wireless software-defined networks (W-SDNs) and network function virtualization (NFV) for 5G cellular systems: An overview and qualitative evaluation. Computer Networks, 93, 66-79. doi: https://doi.org/10.1016/j.comnet.2015.10.013

Akyildiz, I. F., Wang, P., & Lin, S. C. (2015). SoftAir: A software defined networking architecture for 5G wireless systems. Computer Networks, 85, 1-18. doi: https://doi.org/10.1016/j.comnet.2015.05.007

Chen, M., Qian, Y., Mao, S., Tang, W., & Yang, X. (2016). Software-defined mobile networks security. Mobile Networks and Applications, 21(5), 729-743.

Chen, T., Matinmikko, M., Chen, X., Zhou, X., & Ahokangas, P. (2015). Software defined mobile networks: concept, survey, and research directions. IEEE Communications Magazine, 53(11), 126-133. doi: 10.1109/MCOM.2015.7321981

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436. Available Online: DOI: 10.1038/nature14539

Kim, K. G. (2016). Book Review: Deep Learning. Healthcare informatics research, 22(4), 351-354.

Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117. doi: https://doi.org/10.1016/j.neunet.2014.09.003

Boschert, S., & Rosen, R. (2016). Digital twin—the simulation aspect. In Mechatronic Futures (pp. 59-74). Springer, Cham.

Grieves, M., & Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In Transdisciplinary Perspectives on Complex Systems (pp. 85-113). Springer, Cham.

Gutierrez-Garcia, J. O., & López-Neri, E. (2015, July). Cognitive computing: A brief survey and open research challenges. In Applied Computing and Information Technology/2nd International Conference on Computational Science and Intelligence (ACIT-CSI), 2015 3rd International Conference on (pp. 328-333). IEEE. doi: 10.1109/ACIT-CSI.2015.64

Zheng, Z., Xie, S., Dai, H. N., & Wang, H. (2016). Blockchain challenges and opportunities: A survey. Work Pap.–2016.

Dunleavy, M., & Dede, C. (2014). Augmented reality teaching and learning. In Handbook of research on educational communications and technology (pp. 735-745). Springer, New York, NY. doi: https://doi.org/10.1007/978-1-4614-3185-5_59

Al-Ayyoub, M., Jararweh, Y., Benkhelifa, E., Vouk, M., & Rindos, A. (2015, June). Sdsecurity: A software defined security experimental framework. In Communication Workshop (ICCW), 2015 IEEE International Conference on (pp. 1871-1876). IEEE. doi: 10.1109/ICCW.2015.7247453