Development of Naïve Method 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:

Internet of Things (IoT), network are low-power electronic devices

Abstract

As we know, the devices used in any Internet of Things (IoT) network are low-power electronic devices. The Internet of Things (IoT) network may contain many devices in a small area, such as a playground or a park. These devices can send many traffic Kings to the playground. For example, some runners are running on a track. In this case, IoT devices can be in the player's shoes, and at the same time, a disk thrower is running through a disk on the same ground. In this case, IoT devices can be in any wearable player device and inside the disk. To achieve a continuous pattern of data transmission through active devices, we need to develop a structured method of continuous path estimation that can detect active devices that send signals and calculate their paths precisely on the basis of their signal method. We found that a minimum number of signature sequences is needed to find the user activity path, below which the server (or the server) can not correctly estimate the user activity. We propose an efficient method for detecting active devices and their activity path based on a smoothing method that solves a high-dimensional structured estimation problem. Our method estimates the length of the activity of the signature sequence, the smoothing parameter, the accuracy of the result, and the cost of the computational tradeoff. After the discussion paper, there is a numerical result to prove the accuracy of our theory and results.

References

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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
  16. 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.
  17. 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.
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. 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.
  23.  S. Oymak and B. Hassibi, Sharp MSE bounds for proximal denoising, Found. Comput. Math., vol. 16, no. 4, pp. 965–1029, 2016.
  24. R. Schneider and W. Weil, Stochastic and Integral Geometry. Berlin, Germany: Springer, 2008.
  25. N. Parikh and S. Boyd, Proximal algorithms, Found. Trends R⃝Optim., vol. 1, no. 3, pp. 127–239, 2014.
  26. Y. Shi, J. Zhang, B. O’Donoghue, and K. B. Letaief, Large-scale convex optimization for dense wireless cooperative networks, IEEE Trans. Signal Process., vol. 63, no. 18, pp. 4729–4743, Sep. 2015.
  27. T. Goldstein, B. O’Donoghue, S. Setzer, and R. Baraniuk, Fast alternating direction optimization methods, SIAM J. Imag. Sci., vol. 7, no. 3, pp. 1588–1623, 2014.
  28. M. Pilanci and M. J. Wainwright, Randomized sketches of convex programs with sharp guarantees, IEEE Trans. Inf. Theory, vol. 61, no. 9, pp. 5096–5115, Sep. 2015.
  29. M. Pilanci and M. J. Wainwright, Iterative Hessian sketch: Fast and accurate solution approximation for constrained least-squares, J. Mach. Learn. Res., vol. 17, no. 1, pp. 1842–1879, 2016.
  30. S. Oymak, B. Recht, and M. Soltanolkotabi, Sharp time–data tradeoffs for linear inverse problems, IEEE Trans. Inf. Theory, vol. 64, no. 6, pp. 4129–4158, Jun. 2018.
  31. R. Giryes, Y. C. Eldar, A. M. Bronstein, and G. Sapiro, Tradeoffs between convergence speed and reconstruction accuracy in inverse problems, IEEE Trans. Signal Process., vol. 66, no. 7, pp. 1676–1690, Apr. 2018.
  32. J. J. Bruer, J. A. Tropp, V. Cevher, and S. R. Becker, Designing statistical estimators that balance sample size, risk, and computational cost, IEEE J. Sel. Topics Signal Process., vol. 9, no. 4, pp. 612–624, Jun. 2015.
  33. F. Al-Turjman, Price-based data delivery framework for dynamic and pervasive IoT, Pervasive Mobile Comput., vol. 42, pp. 299–316, Dec. 2017.
  34. F. Al-Turjman, QoS—Aware data delivery framework for safetyinspired multimedia in integrated vehicular-IoT, Comput. Commun., vol. 121, pp. 33–43, Apr. 2018.
  35. M. Z. Hasan, H. Al-Rizzo, and F. Al-Turjman, A survey on multipath routing protocols for QoS assurances in real-time wireless multimedia sensor networks, IEEE Commun. Surveys Tuts., vol. 19, no. 3, pp. 1424–1456, 3rd Quart., 2017.
  36. S. H. Alsamhi and N. S. Rajput, An intelligent hand-off algorithm to enhance quality of service in high altitude platforms using neural network, Wireless Pers. Commun., vol. 82, no. 4, pp. 2059–2073, 2015.
  37. D. Mishra, G. C. Alexandropoulos, and S. De, Energy sustainable IoT with individual QoS constraints through MISO SWIPT multicasting, IEEE Internet Things J., vol. 5, no. 4, pp. 2856–2867, Aug. 2018.
  38. S. H. Alsamhi, O. Ma, M. S. Ansari, and Q. Meng. (May 2018). Greening Internet of Things for Smart Everythings With a Green-Environment Life: A Survey and Future Prospects. [Online]. Available: https://arxiv.org/abs/1805.00844
  39. F. Al-Turjman and S. Alturjman, Context-sensitive access in industrial Internet of Things (IIoT) healthcare applications, IEEE Trans. Ind. Informat., vol. 14, no. 6, pp. 2736–2744, Jun. 2018.
  40. M. J. Wainwright, Structured regularizers for high-dimensional problems: Statistical and computational issues, Annu. Rev. Stat. Appl., vol. 1, no. 1, pp. 233–253, 2014.
  41. S.-W. Hu, G.-X. Lin, S.-H. Hsieh, and C.-S. Lu, Performance analysis of joint-sparse recovery from multiple measurement vectors via convex optimization: Which prior information is better? IEEE Access, vol. 6, pp. 3739–3754, 2018.
  42. M. Grant and S. Boyd. (Mar. 2014). CVX: MATLAB Software for Disciplined Convex Programming, Version 2.1. [Online]. Available: http://cvxr.com/cvx
  43. B. B. Yilmaz and A. T. Erdogan, Compressed training adaptive equalization: Algorithms and analysis, IEEE Trans. Commun., vol. 65, no. 9, pp. 3907–3921, Sep. 2017.
  44. R. T. Rockafellar, Convex Analysis. Princeton, NJ, USA: Princeton Univ. Press, 2015.

International Journal of Scientific Research in Science and Technology

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Published

2022-11-04

Issue

Volume 9, Issue 6, November-December-2022

Section

Research Articles

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How to Cite

[1]
Aditi Mishra, Shailesh Kumar Singh, " Development of Naïve Method 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.39-47, November-December-2022.