http://iet.metastore.ingenta.com
1887

Collaborative data aggregation using multiple antennas sensors and fusion centre with energy harvesting capability in WSN

Collaborative data aggregation using multiple antennas sensors and fusion centre with energy harvesting capability in WSN

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Communications — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

In this study, the authors study the collaborative data aggregation using multiple antennas sensors and fusion centre (FC) with energy harvesting capability in the wireless sensor network (WSN). The optimisation problem is formulated to improve the data transfer rate, based on the parameters of collaboration among sensors, the energy harvesting, and storage of each sensor. In particular, they observe several practical constraints for energy harvesting and capability battery energy storage to maintain network connectivity. They propose three scenarios based on the number of antennas for transferring, collecting, and sharing the data on sensor and FC. It is shown these optimisation problems are a non-convex and to resolve this issue, the objective function is converted to a convex function using a relaxation method. The numerical results show the impact of different parameters on the data rate at FC and improvement in network connection and throughput by using proposed collaborative data aggregation techniques compared to their counterparts.

References

    1. 1)
      • 1. Buzzi, S.C.I., Klein, T.E., Poor, H., et al: ‘A survey of energy-efficient techniques for 5G networks and challenges ahead’, IEEE J. Sel. Areas Commun., 2016, 34, (4), pp. 697709.
    2. 2)
      • 2. Mahapatra, R., Nijsure, Y., Kaddoum, G., et al: ‘Energy efficiency tradeoff mechanism towards wireless green communication: a survey’, IEEE Commun. Surv. Tutor., 2016, 18, (1), pp. 686705.
    3. 3)
      • 3. Ericsson, L.: ‘More than 50 billion connected devices’, White Paper, 2011, 14, (1), p. 124.
    4. 4)
      • 4. Nikoletseas, S., Yang, Y., Georgiadis, A.: ‘Wireless power transfer algorithms, technologies and applications in ad hoc communication networks’ (Springer International Publishing, AG, Cham, Switzerland, 2016).
    5. 5)
      • 5. Liu, L., Zhang, R., Chua, K.C.: ‘Wireless information transfer with opportunistic energy harvesting’, IEEE Trans. Wirel. Commun., 2013, 12, (1), pp. 288300.
    6. 6)
      • 6. Li, Q., Zhang, Q., Qin, J.: ‘Secure relay beamforming for simultaneous wireless information and power transfer in nonregenerative relay networks’, IEEE Trans. Veh. Technol., 2014, 63, (5), pp. 24622467.
    7. 7)
      • 7. Guo, S., Wang, F., Yang, Y., et al: ‘Energy-efficient cooperative for simultaneous wireless information and power transfer in clustered wireless sensor networks’, IEEE Trans. Commun., 2015, 63, (11), pp. 44054417.
    8. 8)
      • 8. Ng, D.W.K., Lo, E.S., Schober, R.: ‘Wireless information and power transfer: energy efficiency optimization in OFDMA systems’, IEEE Trans. Wirel. Commun., 2013, 12, (12), pp. 63526370.
    9. 9)
      • 9. Lee, K., Hong, J.: ‘Energy-efficient resource allocation for simultaneous information and energy transfer with imperfect channel estimation’, IEEE Trans. Veh. Technol., 2016, 65, (4), pp. 27752780.
    10. 10)
      • 10. Yu, H., Zhang, Y., Guo, S., et al: ‘Energy efficiency maximization for WSNs with simultaneous wireless information and power transfer’, Sensors, 2017, 17, (8), pp. 190206.
    11. 11)
      • 11. Lu, X., Wang, P., Niyato, D., et al: ‘Wireless charging technologies: fundamentals, standards, and network applications’, IEEE Commun. Surv. Tutor., 2016, 18, (2), pp. 14131452.
    12. 12)
      • 12. Krikidis, I.: ‘Simultaneous information and energy transfer in large-scale networks with/without relaying’, IEEE Trans. Commun., 2014, 62, (3), pp. 900912.
    13. 13)
      • 13. Liu, S., Wang, Y., Fardad, M., et al: ‘Optimal energy allocation and storage control for distributed estimation with sensor collaboration’. 2016 Annual Conf. on IEEE Proc. Conf. Information Science and Systems (CISS), Princeton, NJ, USA, March 2016, pp. 4247.
    14. 14)
      • 14. Liu, S., Kar, S., Fardad, M., et al: ‘Optimized sensor collaboration for estimation of temporally correlated parameters’, IEEE Trans. Signal Process., 2016, 64, (24), pp. 66136626.
    15. 15)
      • 15. Jiang, F., Chen, J., Swindlehurst, A.L., et al: ‘Massive MIMO for wireless sensing with a coherent multiple access channel’, IEEE Trans. Signal Process., 2015, 63, (12), pp. 30053017.
    16. 16)
      • 16. Proakis, J.G., Salehi, M.: ‘Digital communications’ (McGraw-Hill Companies, Inc., New York, 5th edn., 2008).
    17. 17)
      • 17. Cui, S., Xiao, J.J., Goldsmith, A.J., et al: ‘Estimation diversity and energy efficiency in distributed sensing’, IEEE Trans. Signal Process., 2007, 55, (9), pp. 46834695.
    18. 18)
      • 18. Hosseini, S., Kahaei, M.: ‘Target detection in cluster based WSN with massive MIMO systems’, Electron. Lett., 2016, 53, (1), pp. 5052.
    19. 19)
      • 19. Huang, L., Walrand, J., Ramchandran, K.: ‘Optimal demand response with energy storage management’. 2016 IEEE Global Conf., Proc. Conf. Global Signal and Information Processing (GlobalSIP), Tainan, Taiwan, December 2016, pp. 500504.
    20. 20)
      • 20. Wang, Y., Lin, X., Pedram, M.: ‘A near-optimal model-based control algorithm for households equipped with residential photovoltaic power generation and energy storage systems’, IEEE Trans. Sustain. Energy, 2016, 7, (1), pp. 7786.
    21. 21)
      • 21. Boyd, S., Vandenberghe, L.: ‘Convex optimization’ (Cambridge University Press, Cambridge, 2004, 1st edn.).
    22. 22)
      • 22. CVX Research Inc.: ‘CVX: Matlab software for disciplined convex programming, version 2.0’. Web page and software available at http://cvxr.com/cvx, accessed September, 2017.
    23. 23)
      • 23. Sedighi, S., Taherpour, A., Gazor, S., et al: ‘Eigenvalue-based multiple antenna spectrum sensing: higher order moments’, IEEE Trans. Wirel. Commun., 2017, 16, (2), pp. 11681184.
    24. 24)
      • 24. Pourgharehkhan, Z., Taherpour, A., Gazor, S.: ‘Spectrum sensing using a uniform uncalibrated linear antenna array for cognitive radios’, IEEE Trans. Wirel. Commun., 2019, 18, (2), pp. 741752.
    25. 25)
      • 25. Alibeigi, M., Taherpour, A.: ‘Optimisation of secrecy rate in cooperative device to device communications underlaying cellular networks’, IET Commun., 2018, 13, (5), pp. 512519.
    26. 26)
      • 26. Messenger, R.A., Abtahi, A.: ‘Photovoltaic systems engineering’ (CRC Press, Taylor & Francis Group, New York, 4th ed., 2017).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-com.2019.0004
Loading

Related content

content/journals/10.1049/iet-com.2019.0004
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address