The Secure and Energy Efficient Data Routing in the IoT Based Networks

Authors

  • Er. Krishan Kumar  Assistant Professor, Department of CSE, JCDM College of Engineering, Sirsa, Haryana, India
  • Saroj  M. Tech. Scholar, Department of CSE, JCDM College of Engineering, Sirsa, Haryana, India

Keywords:

IoT, Data routing, Energy Efficient, Performance, Security

Abstract

Over the course of the last few years, a great number of studies have been carried out in the field of IoT. It has been found that there is an issue with the performance of the secure data transmission that takes place between sensor nodes and IoT devices. There is an urgent need for inventive strategies that can increase the amount of data that can be sent. In addition to this, a strategy that makes better use of energy should be put into action as soon as possible. Research is now looking at both IoT and a deep learning approach as part of the preparation for the next set of activities. In addition to the energy consumptions, there are a number of other aspects that have the ability to impact both the performance and the throughput of the work that is now being provided. A few examples of these considerations include the geographical distance, the rate of motion of the sensor nodes, and the calibre of the transmission devices. Make a recommendation for a method that can combine data compression with machine learning in order to develop a system that is both efficient in terms of energy consumption and has a high throughput. There is now research being conducted on IoT-based systems to study their degree of performance and safety, in addition to the amount of energy they save while routing data.

References

  1. S. A. Kumar and P. Ilango, “The Impact of Wireless Sensor Network in the Field of Precision Agriculture: A Review,” Wirel. Pers. Commun., vol. 98, no. 1, pp. 685–698, 2018, doi: 10.1007/s11277-017-4890-z.
  2. Q. H. Ngo, N. A. Le-Khac, and T. Kechadi, Ontology based approach for precision agriculture, vol. 11248 LNAI. Springer International Publishing, 2018.
  3. A. M. Patokar and V. V. Gohokar, “Precision agriculture system design using wireless sensor network,” Adv. Intell. Syst. Comput., vol. 625, pp. 169–177, 2018, doi: 10.1007/978-981-10-5508-9_16.
  4. T. A. Mohamed, T. Otsuka, and T. Ito, Towards machine learning based IoT intrusion detection service, vol. 10868 LNAI. Springer International Publishing, 2018.
  5. B. A. Erol, A. Majumdar, J. Lwowski, P. Benavidez, P. Rad, and M. Jamshidi, Improved deep neural network object tracking system for applications in home robotics, vol. 777. Springer International Publishing, 2018.
  6. S. H. Oh, G. W. Kim, and K. S. Lim, “Compact deep learned feature-based face recognition for Visual Internet of Things,” J. Supercomput., vol. 74, no. 12, pp. 6729–6741, 2018, doi: 10.1007/s11227-017-2198-0.
  7. H. HaddadPajouh, A. Dehghantanha, R. Khayami, and K. K. R. Choo, “A deep Recurrent Neural Network based approach for Internet of Things malware threat hunting,” Futur. Gener. Comput. Syst., vol. 85, pp. 88–96, 2018, doi: 10.1016/j.future.2018.03.007.
  8. S. Jabbar et al., “Analysis of Factors Affecting Energy Aware System in Wireless Sensor Network,” Wirel. Commun. Mob. Comput., vol. 2018, 2018, doi: 10.1155/2018/9087269.
  9. B. Keswani et al., “Adapting weather conditions based IoT enabled smart irrigation technique in precision agriculture mechanisms,” Neural Comput. Appl., vol. 31, pp. 277–292, 2019, doi: 10.1007/s00521-018-3737-1.
  10. R. Gómez-Chabla, K. Real-Avilés, C. Morán, P. Grijalva, and T. Recalde, “IoT Applications in Agriculture: A Systematic Literature Review,” Adv. Intell. Syst. Comput., vol. 901, pp. 68–76, 2019, doi: 10.1007/978-3-030-10728-4_8.
  11. A. D. Sappa and F. Dornaika, “An Edge-Based Approach to Motion Detection Conference,” Int. Conf. Comput., vol. 11538, no. May, pp. 648–657, 2019, doi: 10.1007/978-3-030-22744-9.
  12. V. Bhanumathi and K. Kalaivanan, The Role of Geospatial Technology with IoT for Precision Agriculture. Springer International Publishing, 2019.
  13. Köksal and B. Tekinerdogan, Architecture design approach for IoT-based farm management information systems, vol. 20, no. 5. Springer US, 2019.
  14. T. Jan, Ada-boosted locally enhanced probabilistic neural network for IoT intrusion detection, vol. 772. Springer International Publishing, 2019.
  15. X. Feng, F. Yan, and X. Liu, “Study of Wireless Communication Technologies on Internet of Things for Precision Agriculture,” Wirel. Pers. Commun., vol. 108, no. 3, pp. 1785–1802, 2019, doi: 10.1007/s11277-019-06496-7.
  16. V. Dubey, P. Kumar, and N. Chauhan, Forest Fire Detection System Using IoT and Artificial Neural Network, vol. 55. Springer Singapore, 2019.
  17. R. Kumar and D. Kumar, “Multi-objective fractional artificial bee colony algorithm to energy aware system protocol in wireless sensor network,” Wirel. Networks, vol. 22, no. 5, pp. 1461–1474, 2016, doi: 10.1007/s11276-015-1039-4.
  18. S. Picek, I. P. Samiotis, J. Kim, A. Heuser, S. Bhasin, and A. Legay, On the performance of convolutional neural networks for side-channel analysis, vol. 11348 LNCS, no. 1. Springer International Publishing, 2018.

Downloads

Published

2023-04-30

Issue

Section

Research Articles

How to Cite

[1]
Er. Krishan Kumar, Saroj, " The Secure and Energy Efficient Data Routing in the IoT Based Networks, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 2, pp.602-607, March-April-2023.