Skip to main content

Advertisement

Log in

Co-operative beam forming selection with energy balanced operation for wireless sensor network

  • Original Paper
  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

To make the network more reliable and to address energy imbalance issues the cooperative selection of dynamic relay beamforming and energy balanced operation is proposed. Statistic Autonomous Beamforming (SAB) selection of nodes includes picking dynamic relay’s that is used to transmit data. SAB requires very low feedback load, and this strategy is applied for multi user methods like Multiple Input Multiple Output (MIMO). SAB based cooperative scheme is applied by the base station where maximum number of dynamic beams is selected for the active transmission. With the help of transmission power base station can increase the energy harvest at the receiving hub by focussing the power utilized for data transmission on dynamic relay beams. A close-form statistical distribution is derived to calculate the amount of energy harvested in the selected dynamic beams with respect to MIMO users. The performance trade off of average harvested energy, secrecy rate and residual energy of the dynamic relays and the sum rate of multiple MIMO users are analysed. The energy consumption for the proposed scheme is 28% better compared to the existing methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig.3
Fig.4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Xing, C., Wang, N., Ni, J., Fei, Z., & Kuang, J. (2013). MIMO beamforming designs with partial CSI under energy harvesting constraints. IEEE Signal Processing Letters, 20(4), 363–366.

    Article  Google Scholar 

  2. Chen, X., Wang, X., & Chen, X. (2013). Energy-efficient optimization for wireless information and power transfer in large-scale MIMO systems employing energy beamforming. IEEE Wireless Communications Letters, 2(6), 667–670.

    Article  Google Scholar 

  3. Li, Q., Zhang, Q., & Qin, J. (2014). Secure relay beamforming for simultaneous wireless information and power transfer in nonregenerative relay networks. IEEE Transactions on Vehicular Technology, 63(5), 2462–2467.

    Article  Google Scholar 

  4. Huang, J., Li, Q., Zhang, Q., Zhang, G., & Qin, J. (2014). Relay beamforming for amplify-and-forward multi-antenna relay networks with energy harvesting constraint. IEEE Signal Processing Letters, 21(4), 454–458.

    Article  Google Scholar 

  5. Yang, G., Ho, C. K., & Guan, Y. L. (2014). Dynamic resource allocation for multiple-antenna wireless power transfer. IEEE Transactions on Signal Processing, 62(14), 3565–3577.

    Article  MathSciNet  MATH  Google Scholar 

  6. Berbakov, L., Antón-Haro, C., & Matamoros, J. (2014). Joint optimization of transmission policies for collaborative beamforming with energy harvesting sensors. IEEE Transactions on Wireless Communications, 13(7), 3496–3509.

    Article  Google Scholar 

  7. Li, G., Ren, P., Lv, G., & Du, Q. (2014). High-rate relay beamforming for simultaneous wireless information and power transfer. Electronics Letters, 50(23), 1759–1761.

    Article  Google Scholar 

  8. Khandaker, M. R. A., & Wong, K. (2015). Robust secrecy beamforming with energy-harvesting eavesdroppers. IEEE Wireless Communications Letters, 4(1), 10–13.

    Article  Google Scholar 

  9. Mishra, D., & De, S. (2015). Optimal relay placement in two-hop RF energy transfer. IEEE Transactions on Communications, 63(5), 1635–1647.

    Article  Google Scholar 

  10. Huang, W., Chen, H., Li, Y., & Vucetic, B. (2016). On the performance of multi-antenna wireless-powered communications with energy beamforming. IEEE Transactions on Vehicular Technology, 65(3), 1801–1808.

    Article  Google Scholar 

  11. Gong, S., Duan, L., & Gautam, N. (2016). Optimal scheduling and beamforming in relay networks with energy harvesting constraints. IEEE Transactions on Wireless Communications, 15(2), 1226–1238.

    Article  Google Scholar 

  12. Feng, Y., Yang, Z., Zhu, W., Li, Q., & Lv, B. (2017). Robust cooperative secure beamforming for simultaneous wireless information and power transfer in amplify-and-forward relay networks. IEEE Transactions on Vehicular Technology, 66(3), 2354–2366.

    Article  Google Scholar 

  13. Park, J., Jeon, Y., & Han, S. (2017). Energy beamforming for wireless power transfer in MISO heterogeneous network with power beacon. IEEE Communications Letters, 21(5), 1163–1166.

    Article  Google Scholar 

  14. Zhao, L., Zheng, K., Yang, H., & Xiang, W. (2018). Beamformer design and utility optimization for hybrid information and energy transfer with massive MIMO. IEEE Systems Journal, 12(2), 1981–1992.

    Article  Google Scholar 

  15. Zhao, D., Tian, H., & Zhang, P. (2019). A secure wireless information and energy cooperation transmission strategy in spectrum sharing networks with untrusted dual-relay. IEEE Access, 7, 115487–115504.

    Article  Google Scholar 

  16. Wu, T., Yang, H., & Liang, Y. (2016). Cooperative secondary beam selection for cognitive multiuser MIMO transmission with random beamforming. IEEE Transactions on Cognitive Communications and Networking, 2(2), 141–149.

    Article  Google Scholar 

  17. Liang, S., Fang, Z., Sun, G., Liu, Y., Qu, G., Jayaprakasam, S., & Zhang, Y. (2020). A joint optimization approach for distributed collaborative beamforming in mobile wireless sensor networks. Ad Hoc Networks, 106, 102216.

    Article  Google Scholar 

  18. Zhu, Y., Liu, Y., Zhao, J., Li, M., & Wu, Q. (2021, December). Joint time allocation and beamforming design for IRS-aided coexistent cellular and sensor networks. In 2021 IEEE global communications conference (GLOBECOM) (pp. 1–6). IEEE.

  19. Du, R., Shokri-Ghadikolaei, H., & Fischione, C. (2020). Wirelessly-powered sensor networks: Power allocation for channel estimation and energy beamforming. IEEE Transactions on Wireless Communications, 19(5), 2987–3002.

    Article  Google Scholar 

  20. Yin, Y., Peng, Y., Liu, M., Yang, J., & Gui, G. (2019). Dynamic user grouping-based NOMA over rayleigh fading channels. IEEE Access, 7, 110964–110971.

    Article  Google Scholar 

  21. Sun, G., Zhao, X., Shen, G., Liu, Y., Wang, A., Jayaprakasam, S., & Leung, V. C. (2020). Improving performance of distributed collaborative beamforming in mobile wireless sensor networks: a multiobjective optimization method. IEEE Internet of Things Journal, 7(8), 6787–6801.

    Article  Google Scholar 

  22. Liu, J., Lin, C. H. R., Hu, Y. C., & Donta, P. K. (2022). Joint beamforming, power allocation, and splitting control for SWIPT-enabled IoT networks with deep reinforcement learning and game theory. Sensors, 22(6), 2328.

    Article  Google Scholar 

  23. Kumar, D. P., Amgoth, T., & Annavarapu, C. S. R. (2019). Machine learning algorithms for wireless sensor networks: a survey. Information Fusion, 49, 1–25.

    Article  Google Scholar 

  24. Ratha, D., Bhattacharya, A., Frery, A. C., & Pottier, E. (2019, July). A scattering power factorization framework using a geodesic distance for multi-looked polsar data. In IGARSS 2019–2019 IEEE international geoscience and remote sensing symposium (pp. 5125–5128). IEEE.

  25. Lv, Z., Qiao, L., & You, I. (2020). 6G-enabled network in box for internet of connected vehicles. IEEE transactions on intelligent transportation systems. https://doi.org/10.1109/TITS.2020.3034817

    Google Scholar 

  26. Cao, B., Zhang, J., Liu, X., Sun, Z., Cao, W., Nowak, R., & M. Lv, Z. (2021). Edge-cloud resource scheduling in space-air-ground integrated networks for internet of vehicles. IEEE internet of things journal. https://doi.org/10.1109/JIOT.2021.3065583

    Google Scholar 

  27. Sun, Q., Lin, K., Si, C., Xu, Y., & Li, S.,... Gope, P. (2022). A secure and anonymous communicate scheme over the internet of things. ACM Transactions on Sensor Networks. https://doi.org/10.1145/3508392

    Article  Google Scholar 

  28. Lv, Z., Chen, D., Feng, H., Wei, W., & Lv, H. (2022). Artificial intelligence in underwater digital twins sensor networks. ACM Transactions on Sensor Networks. https://doi.org/10.1145/3519301

    Article  Google Scholar 

  29. Li, Z., Peng, X., Hu, G., Zhang, D., Xu, Z., & Peng, Y Xie, S. (2022). Towards real-time self-powered sensing with ample redundant charges by a piezostack-based frequency-converted generator from human motions. Energy conversion and management, 258, 1. https://doi.org/10.1016/j.enconman.2022.115466

    Article  Google Scholar 

  30. Zhu, B., Zhong, Q., Chen, Y., Liao, S., Li, Z., & Shi, K.,... Sotelo, M. A. (2022). A novel reconstruction method for temperature distribution measurement based on ultrasonic tomography. IEE Transactions on Ultrasonics Ferroelectrics and Frequency Control. https://doi.org/10.1109/TUFFC.2022.3177469

    Article  Google Scholar 

  31. Sun, G., Cong, Y., Dong, J., Liu, Y., & Ding, Z.,... Yu, H. (2021). What and how: generalized lifelong spectral clustering via dual memory. IEEE Transactions on Pattern Analysis and Machine Intelligence, PP. https://doi.org/10.1109/TPAMI.2021.3058852

    Article  Google Scholar 

  32. Li, B., Yang, J., Yang, Y., Li, C., & Zhang, Y. (2021). Sign language/gesture recognition based on cumulative distribution density features using UWB Radar. IEEE Transactions on Instrumentation and Measurement. https://doi.org/10.1109/TIM.2021.3092072

    Google Scholar 

  33. Liu, K., Ke, F., Huang, X., Yu, R., Lin, F., & Wu, Y. Ng, D. W. K. (2021). DeepBAN: A temporal convolution-based communication framework for dynamic WBANs. IEEE Transactions on Communications, 69(10), 6675–6690. https://doi.org/10.1109/TCOMM.2021.3094581

    Article  Google Scholar 

  34. Meng, F., Zheng, Y., Bao, S., Wang, J., & Yang, S. (2022). Formulaic language identification model based on GCN fusing associated information. PeerJ Computer Science, 8, e984. https://doi.org/10.7717/peerj-cs.984

    Article  Google Scholar 

  35. Duy, H., Dengy, Y., Xueyz, J., Mengyz, D., & Zhaoy, Q Xuy, Z. (2022). Robust online CSI estimation in a complex environment. IEEE Transactions on Wireless Communications. https://doi.org/10.1109/TWC.2022.3165588

    Article  Google Scholar 

  36. Wu, M., Zhang, B., Zhou, Y., & Huang, K. (2022). A double-fold 7×8 butler matrix-fed multibeam antenna with a boresight beam for 5G applications. IEEE Antennas and Wireless Propagation Letters, 21(3), 516–520. https://doi.org/10.1109/LAWP.2021.3136913

    Article  Google Scholar 

  37. Jiang, Y., & Li, X. (2022). Broadband cancellation method in an adaptive co-site interference cancellation system. International journal of electronics, 109(5), 854–874. https://doi.org/10.1080/00207217.2021.1941295

    Article  Google Scholar 

  38. Wang, Z., Ramamoorthy, R., Xi, X., Rajagopal, K., & Zhang, P.,... Jafari, S. (2022). The effects of extreme multistability on the collective dynamics of coupled memristive neurons. The European Physical Journal Special Topics. https://doi.org/10.1140/epjs/s11734-022-00558-x

    Article  Google Scholar 

  39. Wang, Z., Ramamoorthy, R., Xi, X., Namazi, H., Intelligence, C. F. A., & C. I. O. T., College Of Engineering And Science, V. U. M. A.,... School Of Engineering, M. U. S. M. (2022). Synchronization of the neurons coupled with sequential developing electrical and chemical synapses. Mathematical biosciences and engineering: MBE, 19(2), 1877–1890. https://doi.org/10.3934/mbe.2022088

    Article  Google Scholar 

  40. Liu, S., Zhang, J., Niu, B., Liu, L., & He, X. (2022). A novel hybrid multi-criteria group decision-making approach with intuitionistic fuzzy sets to design reverse supply chains for COVID-19 medical waste recycling channels. Computers & Industrial Engineering. https://doi.org/10.1016/j.cie.2022.108228

    Google Scholar 

  41. Niu, Z., Zhang, B., Dai, B., Zhang, J., Shen, F., Hu, Y., & ZHANG, Y. (2022). 220 GHz multi circuit integrated front end based on solid-state circuits for high speed communication system. Chinese Journal of Electronics, 31(3), 569–580. https://doi.org/10.1049/cje.2021.00.295

    Article  Google Scholar 

  42. Sui, T., Marelli, D., Sun, X., & Fu, M. (2020). Multi-sensor state estimation over lossy channels using coded measurements. Automatica (Oxford), 111, 108561. https://doi.org/10.1016/j.automatica.2019.108561

    Article  MathSciNet  MATH  Google Scholar 

  43. Zheng, W., Liu, X., & Yin, L. (2021). Research on image classification method based on improved multi-scale relational network. PeerJ Computer Science, 7, e613. https://doi.org/10.7717/peerj-cs.613

    Article  Google Scholar 

  44. Ma, Z., Zheng, W., Chen, X., & Yin, L. (2021). Joint embedding VQA model based on dynamic word vector. PeerJ Computer Science, 7, e353. https://doi.org/10.7717/peerj-cs.353

    Article  Google Scholar 

  45. Zheng, W., Yin, L., Chen, X., Ma, Z., & Liu, S.,... Yang, B. (2021). Knowledge base graph embedding module design for visual question answering model. Pattern recognition, 120, 108153. https://doi.org/10.1016/j.patcog.2021.108153

    Article  Google Scholar 

  46. Du, Y., Qin, B., Zhao, C., Zhu, Y., & Cao, J.,... Ji, Y. (2021). A novel spatio-temporal synchronization method of roadside asynchronous MMW radar-camera for sensor fusion. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2021.3119079

    Article  Google Scholar 

  47. Tian, H., Qin, Y., Niu, Z., Wang, L., & Ge, S. (2021). Summer maize mapping by compositing time series sentinel-1A imagery based on crop growth cycles. Journal of the Indian Society of Remote Sensing, 49(11), 2863–2874. https://doi.org/10.1007/s12524-021-01428-0

    Article  Google Scholar 

  48. Tian, H., Wang, Y., Chen, T., Zhang, L., & Qin, Y. (2021). Early-season mapping of winter crops using sentinel-2 optical imagery. Remote sensing (Basel, Switzerland), 13(19), 3822. https://doi.org/10.3390/rs13193822

    Article  Google Scholar 

  49. Chen, J., Du, L., & Guo, Y. (2021). Label constrained convolutional factor analysis for classification with limited training samples. Information sciences, 544, 372–394. https://doi.org/10.1016/j.ins.2020.08.048

    Article  MathSciNet  MATH  Google Scholar 

  50. Li, Y., Du, L., & Wei, D. (2022). Multiscale CNN based on component analysis for SAR ATR. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–12. https://doi.org/10.1109/TGRS.2021.3100137

    Google Scholar 

  51. Zhou, G., Li, C., Zhang, D., Liu, D., & Zhou, X.,... Zhan, J. (2021). Overview of underwater transmission characteristics of oceanic LiDAR. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 8144–8159. https://doi.org/10.1109/JSTARS.2021.3100395

    Article  Google Scholar 

  52. Zhou, G., Deng, R., Zhou, X., Long, S., Li, W., & Lin, G.,... Li, X. (2021). Gaussian inflection point selection for LiDAR hidden echo signal decomposition. IEEE Geoscience and Remote Sensing Letters. https://doi.org/10.1109/LGRS.2021.3107438

    Google Scholar 

  53. Zhou, G., Yang, F., & Xiao, J. (2022). Study on pixel entanglement theory for imagery classification. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–18. https://doi.org/10.1109/TGRS.2022.3167569

    Google Scholar 

  54. Wu, Z., Cao, J., Wang, Y., Wang, Y., & Zhang, L.,... Wu, J. (2020). hPSD: A hybrid PU-learning-based spammer detection model for product reviews. IEEE Transactions on Cybernetics, 50(4), 1595–1606. https://doi.org/10.1109/TCYB.2018.2877161

    Article  Google Scholar 

  55. Ma, et al. (2022). Voltage regulation with electric taxi based on dynamic game strategy. IEEE Transactions on Vehicular Technology, 71(3), 2413–2426. https://doi.org/10.1109/TVT.2022.3141954

    Article  Google Scholar 

  56. Ma, K., et al. (2021). Reliability-constrained throughput optimization of industrial wireless sensor networks with energy harvesting relay. IEEE Internet of Things Journal, 8(17), 13343–13354. https://doi.org/10.1109/JIOT.2021.3065966

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. Karthikeyan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

James, K.I.A., Prabakaran, R., Karthikeyan, A. et al. Co-operative beam forming selection with energy balanced operation for wireless sensor network. Wireless Netw 28, 3653–3663 (2022). https://doi.org/10.1007/s11276-022-03067-w

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-022-03067-w

Keywords

Navigation