A Survey on Techniques to Identify Active Devices and their Channels in an IoT Networks

Authors

  • Aditi Mishra  Research Scholar,Sheat College of Engineering, Varanasi, Uttar Pradesh, India
  • Shailesh Kumar Singh  Sheat College of Engineering, Varanasi, Uttar Pradesh, India

Keywords:

Guaranteed Time Slots, Internet of Things

Abstract

Before nodes may send data in an IoT (Internet of Things) network, they must first access the network's resources. The node shouldn't become stalled when trying to access resources if the data being transferred needs real-time assurances. For timely data communication, quick and effective access to the network resources is preferred. In order to do this, we investigated the IEEE 802.15.4 and IEEE 802.15.4e-TSCH standards and put forth energy-saving algorithms to guarantee that the nodes may access the resources as soon as possible. For IEEE 802.15.4 networks, we have put forth the "Device Registration" approach, which intends to improve access to the Guaranteed Time Slots (GTSs) included in the superframe. The settings of the current MAC framework can be slightly altered to implement the method. For IEEE 802.15.4e-TSCH networks, we've also suggested a "Sparse Beacon Advertisement," a beacon scheduling technique that tries to shorten the wait time for a new node before it enters the network, even when there aren't many beacons being advertised. Both of these algorithms have undergone in-depth testing on a testbed utilising simulations and experimentation. Our findings demonstrate that nodes are twice as effective as they were before in gaining access to GTS resources in an IEEE 802.15.4 network when using the suggested approach. Similar to this, in an IEEE 802.15.4e-TSCH network, sparse beacon advertisement cuts joining times by at least 60%.

References

  1. A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash, “Internet of Things: A survey on enabling technologies, protocols, and applications,” IEEE Commun. Surveys Tuts., vol. 17, no. 4, pp. 2347–2376, 4th Quart., 2015.
  2. H. S. Dhillon, H. Huang, and H. Viswanathan, “Wide-area wireless communication challenges for the Internet of Things,” IEEE Commun. Mag., vol. 55, no. 2, pp. 168–174, Feb. 2017.
  3. L. Liu et al., “Sparse signal processing for grant-free massive connectivity: A future paradigm for random access protocols in the Internet of Things,” IEEE Signal Process. Mag., vol. 33, no. 5, pp. 88–89, Sep. 2018.
  4. T. P. C. de Andrade, C. A. Astudillo, L. R. Sekijima, and N. L. da Fonseca, “The random access procedure in long term evolution networks for the Internet of Things,” IEEE Commun. Mag., vol. 55, no. 3, pp. 124–131, Mar. 2017.
  5. A. Rajandekar and B. Sikdar, “A survey of MAC layer issues and protocols for machine-to-machine communications,” IEEE Internet Things J., vol. 2, no. 2, pp. 175–186, Apr. 2015.
  6. T. Xu and I. Darwazeh, “Nonorthogonal narrowband Internet of Things: A design for saving bandwidth and doubling the number of connected devices,” IEEE Internet Things J., vol. 5, no. 3, pp. 2120–2129, Jun. 2018.
  7. A. Ghosh, J. Zhang, J. G. Andrews, and R. Muhamed, Fundamentals of LTE. Upper Saddle River, NJ, USA: Pearson Educ., 2010.
  8. G. Wunder, H. Boche, T. Strohmer, and P. Jung, “Sparse signal processing concepts for efficient 5G system design,” IEEE Access, vol. 3, pp. 195–208, 2015.
  9. L. Dai et al., “Nonorthogonal multiple access for 5G: Solutions, challenges, opportunities, and future research trends,” IEEE Commun. Mag., vol. 53, no. 9, pp. 74–81, Sep. 2015.
  10. Z. Chen, F. Sohrabi, and W. Yu, “Sparse activity detection for massive connectivity,” IEEE Trans. Signal Process., vol. 66, no. 7, pp. 1890–1904, Apr. 2018.
  11. J. W. Choi, B. Shim, Y. Ding, B. Rao, and D. I. Kim, “Compressed sensing for wireless communications: Useful tips and tricks,” IEEE Commun. Surveys Tuts., vol. 19, no. 3, pp. 1527–1550, 3rd Quart., 2017.
  12. Z. Qin, J. Fan, Y. Liu, Y. Gao, and G. Y. Li, “Sparse representation for wireless communications: A compressive sensing approach,” IEEE Signal Process. Mag., vol. 35, no. 3, pp. 40–58, May 2018. 6224 IEEE INTERNET OF THINGS JOURNAL, VOL. 6, NO. 4, AUGUST 2019
  13. J.-C. Shen, J. Zhang, E. Alsusa, and K. B. Letaief, “Compressed CSI acquisition in FDD massive MIMO: How much training is needed?” IEEE Trans. Wireless Commun., vol. 15, no. 6, pp. 4145–4156, Jun. 2016.
  14. X. Liu, Y. Shi, J. Zhang, and K. B. Letaief, “Massive CSI acquisition for dense cloud-RANs with spatial-temporal dynamics,” IEEE Trans. Wireless Commun., vol. 17, no. 4, pp. 2557–2570, Apr. 2018.
  15. M. Hasan, E. Hossain, and D. Niyato, “Random access for machineto- machine communication in LTE-advanced networks: Issues and approaches,” IEEE Commun. Mag., vol. 51, no. 6, pp. 86–93, Jun. 2013.
  16. E. Björnson, E. De Carvalho, J. H. Sørensen, E. G. Larsson, and P. Popovski, “A random access protocol for pilot allocation in crowded massive MIMO systems,” IEEE Trans. Wireless Commun., vol. 16, no. 4, pp. 2220–2234, Apr. 2017.
  17. Y. Shi, J. Zhang, W. Chen, and K. B. Letaief, “Generalized sparse and low-rank optimization for ultra-dense networks,” IEEE Commun. Mag., vol. 56, no. 6, pp. 42–48, Jun. 2018.
  18. F. Al-Turjman, E. Ever, and H. Zahmatkesh, “Small cells in the forth coming 5G/IoT: Traffic modelling and deployment overview,” IEEE Commun. Surveys Tuts., to be published.
  19. X. Chen, T.-Y. Chen, and D. Guo, “Capacity of Gaussian many-access channels,” IEEE Trans. Inf. Theory, vol. 63, no. 6, pp. 3516–3539, Jun. 2017.
  20. H. Zhu and G. B. Giannakis, “Exploiting sparse user activity in multiuser detection,” IEEE Trans. Commun., vol. 59, no. 2, pp. 454–465, Feb. 2011.
  21. S. Alsamhi, O. Ma, and M. Ansari. (May 2018). Predictive Estimation of the Optimal Signal Strength From Unmanned Aerial Vehicle Over Internet of Things Using ANN. [Online]. Available: https://arxiv.org/abs/1805.07614
  22. H. F. Schepker, C. Bockelmann, and A. Dekorsy, “Efficient detectors  or joint compressed sensing detection and channel decoding,” IEEE Trans. Commun., vol. 63, no. 6, pp. 2249–2260, Jun. 2015.
  23. Y. Du et al., “Efficient multiuser detection for uplink grant-free NOMA: Prior-information aided adaptive compressive sensing perspective,” IEEE J. Sel. Areas Commun., vol. 35, no. 12, pp. 2812–2828, Dec. 2017.
  24. L. Liu and W. Yu, “Massive connectivity with massive MIMO—Part I: Device activity detection and channel estimation,” IEEE Trans. Signal Process., vol. 66, no. 11, pp. 2933–2946, Jun. 2018.
  25. L. Liu and W. Yu, “Massive connectivity with massive MIMO—Part II: Achievable rate characterization,” IEEE Trans. Signal Process., vol. 66, no. 11, pp. 2947–2959, Jun. 2018.
  26. S. H. Alsamhi and N. S. Rajput, “An efficient channel reservation technique for improved QoS for mobile communication deployment using high altitude platform,” Wireless Pers. Commun., vol. 91, no. 3, pp. 1095–1108, 2016.
  27. Q. He, T. Q. Quek, Z. Chen, and S. Li, “Compressive channel estimation and multiuser detection in C-RAN,” in Proc. IEEE Int. Conf. Commun. (ICC), Paris, France, May 2017, pp. 1–6.
  28. S. Foucart and H. Rauhut, A Mathematical Introduction to Compressive Sensing, vol. 1. New York, NY, USA: Birkhäuser, 2013.
  29. V. Chandrasekaran, B. Recht, P. A. Parrilo, and A. S. Willsky, “The convex geometry of linear inverse problems,” Found. Comput. Math., vol. 12, no. 6, pp. 805–849, 2012.
  30. D. Amelunxen, M. Lotz, M. B. McCoy, and J. A. Tropp, “Living on the edge: Phase transitions in convex programs with random data,” Inf. Inference, vol. 3, pp. 224–294, Jun. 2014.

International Journal of Scientific Research in Science and Technology

Downloads

Published

2022-11-04

Issue

Volume 9, Issue 6, November-December-2022

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Aditi Mishra, Shailesh Kumar Singh, " A Survey on Techniques to Identify Active Devices and their Channels in an IoT Networks, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 9, Issue 6, pp.32-38, November-December-2022.