Skip to main content
SpringerLink
Log in

A Triangle-Based Localization Scheme in Wireless Multimedia Sensor Network

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Wireless Multimedia Sensor Networks (WMSNs) have emerged as a significant research topic aimed at improving the flexibility of power distribution networks. The stability of a power distribution network may be greatly influenced by the precision and energy use of sensor node localization, especially when using Wireless Sensor Networks (WSNs). This work introduces a suggestion for a modified triangle-based localization scheme (MTBLS) with the objective of enhancing the performance of the conventional mid-normal-based localization scheme (MBLS) and the triangle-based localization method (TBLS). The aim of this study is to effectively meet the communication needs associated with smart distribution automation. The proposed strategy aims to create a triangular configuration by establishing a connection between two anchor nodes located on one side of the triangle. The RSSI (Received Signal Strength Indication) value is used to measure the signal strength between the two anchor nodes and the unknown node, which are positioned on the remaining two sides of the triangular configuration. The simulation was performed using the NS-2 Tool, and afterwards, the acquired outcomes were compared with the performance of the existing MBLS and TBLS algorithms. The results indicate that the use of MTBLS has resulted in improved accuracy in the localization of nodes when compared to presently available methods. In addition, there has been a significant decrease in energy consumption across the whole of the power distribution network, which has coincided with a reduction in the quantity of iterations.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data Availability

No data associated.

References

  1. Bulusu, N., Heidemann, J., & Estrin, D. (2007). GPS-less low cost outdoor localization for very small devices. IEEE Personal Communications Magazine, 7(5), 28–34.

    Article  Google Scholar 

  2. Jin, L. (2009). Research on range-based localization algorithm of wireless sensor networks. Aeronautical Computing Technique, 39(6), 124–126.

    Google Scholar 

  3. Peihua, D., Xiaoping, X., & Yuhua, S. (2012). Midnormal-based area localization algorithm. Computer Engineering and Application, 35(2), 105–108.

    Google Scholar 

  4. Vo, D.M.N., Vo, D., Challa, S., & Lee, S.Y. (2008) Nonmetric MDS for sensor localization. In 3rd International Symposium on Wireless Pervasive Computing 2008 (ISWPC 2008), Ubiquitous Computing Lab, Kyung Hee University, pp. 396–400.

  5. Weike, C., Wenfeng, L., Heng, S., et al. (2009). Weighted centroid localization algorithm based on RSSI for wireless sensor networks. Journal of Wuhan University of Technology (Transportation Science & Engineering), 30(2), 265–268.

    Google Scholar 

  6. Xun, A., Ting, J., & Zheng, Z. (2011). Centroid localization algorithm for wireless sensor networks. Computer Engineering and Application, 43(20), 136–138.

    Google Scholar 

  7. Zhao, Z., Xu, G., Zhang, N., & Zhang, Q. (2022). Performance analysis of the hybrid satellite-terrestrial relay network with opportunistic scheduling over generalized fading channels. IEEE Transactions on Vehicular Technology, 71(3), 2914–2924. https://doi.org/10.1109/TVT.2021.3139885

    Article  Google Scholar 

  8. Xu, K., Guo, Y., Liu, Y., Deng, X., Chen, Q., & Ma, Z. (2021). 60-GHz compact dual-mode on-chip bandpass filter using GaAs technology. IEEE Electron Device Letters, 42(8), 1120–1123. https://doi.org/10.1109/LED.2021.3091277

    Article  Google Scholar 

  9. Li, A., Masouros, C., Swindlehurst, A. L., & Yu, W. (2021). 1-bit massive MIMO transmission: Embracing interference with symbol-level precoding. IEEE Communications Magazine, 59(5), 121–127. https://doi.org/10.1109/MCOM.001.2000601

    Article  Google Scholar 

  10. Li, A., Masouros, C., Vucetic, B., Li, Y., & Swindlehurst, A. L. (2021). Interference exploitation precoding for multi-level modulations: Closed-form solutions. IEEE Transactions on Communications, 69(1), 291–308. https://doi.org/10.1109/TCOMM.2020.3031616

    Article  Google Scholar 

  11. Min, H., Fang, Y., Wu, X., Lei, X., Chen, S., Teixeira, R., Zhu, B., & Zhao, X. (2023). A fault diagnosis framework for autonomous vehicles with sensor self-diagnosis. Expert Systems with Applications, 224, 120002. https://doi.org/10.1016/j.eswa.2023.120002

    Article  Google Scholar 

  12. Pan, S., Lin, M., Xu, M., Zhu, S., Bian, L., & Li, G. (2022). A low-profile programmable beam scanning holographic array antenna without phase shifters. IEEE Internet of Things Journal, 9(11), 8838–8851. https://doi.org/10.1109/JIOT.2021.3116158

    Article  Google Scholar 

  13. Li, B., Zhang, M., Rong, Y., & Han, Z. (2021). Transceiver optimization for wireless powered time-division duplex MU-MIMO systems: Non-robust and robust designs. IEEE Transactions on Wireless Communications, 21(6), 4594–4607. https://doi.org/10.1109/TWC.2021.3131595

    Article  Google Scholar 

  14. Ding, G., Anselmi, N., Xu, W., Li, P., & Rocca, P. (2023). Interval-bounded optimal power pattern synthesis of array antenna excitations robust to mutual coupling. IEEE Antennas and Wireless Propagation Letters. https://doi.org/10.1109/LAWP.2023.3291428

    Article  Google Scholar 

  15. Zhang, X., Wang, Y., Yuan, X., Shen, Y., Lu, Z., & Wang, Z. (2022). Adaptive dynamic surface control with disturbance observers for battery/supercapacitor-based hybrid energy sources in electric vehicles. IEEE Transactions on Transportation Electrification. https://doi.org/10.1109/TTE.2022.3194034

    Article  Google Scholar 

  16. Wang, B., Zhu, D., Han, L., Gao, H., Gao, Z., & Zhang, Y. (2023). Adaptive fault-tolerant control of a hybrid canard rotor/wing UAV under transition flight subject to actuator faults and model uncertainties. IEEE Transactions on Aerospace and Electronic Systems. https://doi.org/10.1109/TAES.2023.3243580

    Article  Google Scholar 

  17. Wang, B., Zhang, Y., & Zhang, W. (2022). A composite adaptive fault-tolerant attitude control for a quadrotor UAV with multiple uncertainties. Journal of Systems Science and Complexity, 35(1), 81–104. https://doi.org/10.1007/s11424-022-1030-y

    Article  MathSciNet  Google Scholar 

  18. 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 

  19. Li, Q., Lin, H., Tan, X., & Du, S. (2020). H∞ consensus for multiagent-based supply chain systems under switching topology and uncertain demands. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(12), 4905–4918. https://doi.org/10.1109/TSMC.2018.2884510

    Article  Google Scholar 

  20. Wang, F., Wang, H., Zhou, X., & Fu, R. (2022). A driving fatigue feature detection method based on multifractal theory. IEEE Sensors Journal, 22(19), 19046–19059. https://doi.org/10.1109/JSEN.2022.3201015

    Article  Google Scholar 

  21. Liu, A., Zhai, Y., Xu, N., Nie, W., Li, W., & Zhang, Y. (2022). Region-aware image captioning via interaction learning. IEEE Transactions on Circuits and Systems for Video Technology, 32(6), 3685–3696. https://doi.org/10.1109/TCSVT.2021.3107035

    Article  Google Scholar 

  22. Mao, Y., Zhu, Y., Tang, Z., & Chen, Z. (2022). A novel airspace planning algorithm for cooperative target localization. Electronics, 11(18), 2950. https://doi.org/10.3390/electronics11182950

    Article  Google Scholar 

  23. Mao, Y., Sun, R., Wang, J., Cheng, Q., Kiong, L. C., & Ochieng, W. Y. (2022). New time-differenced carrier phase approach to GNSS/INS integration. GPS Solutions, 26(4), 122. https://doi.org/10.1007/s10291-022-01314-3

    Article  Google Scholar 

  24. Liu, J., Fan, C., Peng, Y., Du, J., Wang, Z., & Chu, C. (2023). Emergent leader-follower relationship in networked multiagent systems. SCIENCE CHINA Information Sciences. https://doi.org/10.1007/s11432-022-3741-3

    Article  Google Scholar 

  25. Jiang, H., Wang, M., Zhao, P., Xiao, Z., & Dustdar, S. (2021). A utility-aware general framework with quantifiable privacy preservation for destination prediction in LBSs. IEEE/ACM Transactions on Networking, 29(5), 2228–2241. https://doi.org/10.1109/TNET.2021.3084251

    Article  Google Scholar 

  26. 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 

  27. Jiang, Y., Liu, S., Li, M., Zhao, N., & Wu, M. (2022). A new adaptive co-site broadband interference cancellation method with auxiliary channel. Digital Communications and Networks. https://doi.org/10.1016/j.dcan.2022.10.025

    Article  Google Scholar 

  28. Zhao, J., Gao, F., Jia, W., Yuan, W., & Jin, W. (2023). Integrated sensing and communications for UAV communications with jittering effect. IEEE Wireless Communications Letters. https://doi.org/10.1109/LWC.2023.3243590

    Article  Google Scholar 

  29. Jiang, S., Zhao, C., Zhu, Y., Wang, C., Du, Y., Lei, W., et al. (2022). A practical and economical ultra-wideband base station placement approach for indoor autonomous driving systems. Journal of advanced transportation, 2022, 1–12. https://doi.org/10.1155/2022/3815306

    Article  Google Scholar 

  30. Zhang, C., Xiao, P., Zhao, Z., Liu, Z., Yu, J., Hu, X., et al. (2023). A wearable localized surface plasmons antenna sensor for communication and sweat sensing. IEEE Sensors Journal, 23(11), 11591–11599. https://doi.org/10.1109/JSEN.2023.3266262

    Article  Google Scholar 

  31. Liu, L., Zhang, S., Zhang, L., Pan, G., & Yu, J. (2022). Multi-UUV maneuvering counter-game for dynamic target scenario based on fractional-order recurrent neural network. IEEE Transactions on Cybernetics. https://doi.org/10.1109/TCYB.2022.3225106

    Article  Google Scholar 

  32. Cheng, B., Zhu, D., Zhao, S., & Chen, J. (2016). Situation-aware IoT service coordination using the event-driven SOA paradigm. IEEE Transactions on Network and Service Management, 13(2), 349–361. https://doi.org/10.1109/TNSM.2016.2541171

    Article  Google Scholar 

  33. Cheng, B., Wang, M., Zhao, S., Zhai, Z., Zhu, D., & Chen, J. (2017). Situation-aware dynamic service coordination in an IoT environment. IEEE/ACM Transactions on Networking, 25(4), 2082–2095. https://doi.org/10.1109/TNET.2017.2705239

    Article  Google Scholar 

  34. Lu, S., Liu, M., Yin, L., Yin, Z., Liu, X., Zheng, W., & Kong, X. (2023). The multi-modal fusion in visual question answering: A review of attention mechanisms. PeerJ Computer Science, 9, e1400. https://doi.org/10.7717/peerj-cs.1400

    Article  Google Scholar 

  35. Liu, X., Shi, T., Zhou, G., Liu, M., Yin, Z., Yin, L., & Zheng, W. (2023). Emotion classification for short texts: An improved multi-label method. Humanities and Social Sciences Communications, 10(1), 306. https://doi.org/10.1057/s41599-023-01816-6

    Article  Google Scholar 

  36. Liu, X., Zhou, G., Kong, M., Yin, Z., Li, X., Yin, L., & Zheng, W. (2023). Developing multi-labelled corpus of twitter short texts: A semi-automatic method. Systems, 11(8), 390. https://doi.org/10.3390/systems11080390

    Article  Google Scholar 

  37. Lu, S., Ding, Y., Liu, M., Yin, Z., Yin, L., & Zheng, W. (2023). Multiscale feature extraction and fusion of image and text in VQA. International Journal of Computational Intelligence Systems, 16(1), 54. https://doi.org/10.1007/s44196-023-00233-6

    Article  Google Scholar 

  38. Lv, Z., & Kumar, N. (2020). Software defined solutions for sensors in 6G/IoE. Computer Communications, 153, 42–47. https://doi.org/10.1016/j.comcom.2020.01.060

    Article  Google Scholar 

  39. Lv, Z., Chen, D., Feng, H., Wei, W., & Lv, H. (2022). Artificial intelligence in underwater digital twins sensor networks. ACM Transactions on Sensor Networks (TOSN), 18(3), 1–27. https://doi.org/10.1145/3519301

    Article  Google Scholar 

  40. Hou, X., Zhang, L., Su, Y., Gao, G., Liu, Y., Na, Z., Xu, Q. Z., Ding, T., Xiao, L., Li, L., & Chen, T. (2023). A space crawling robotic bio-paw (SCRBP) enabled by triboelectric sensors for surface identification. Nano Energy, 105, 108013. https://doi.org/10.1016/j.nanoen.2022.108013

    Article  Google Scholar 

  41. Yang, M., Wang, Y., Liang, Y., & Wang, C. (2022). A new approach to system design optimization of underwater gliders. IEEE/ASME Transactions on Mechatronics, 27(5), 3494–3505. https://doi.org/10.1109/TMECH.2022.3143125

    Article  Google Scholar 

  42. Yan, Z., Bao, W., & Jianxun, L. (2012). Closer nodes weighted centroid localization for wireless sensor networks. Computer Engineering and Applications, 48(1), 87–89.

    Google Scholar 

  43. Yu, H., & Weizhao, Y. (2011). Linear-regression-based weighted centroid localization algorithm in wireless sensor network. Journal of Taiyuan University of Technology, 42(5), 499–502.

    Google Scholar 

  44. Yu, P., & Dan, W. (2011). A review: Wireless sensor networks localization. Journal of Electronic Measurement and Instrument, 25(5), 389–399.

    Article  Google Scholar 

  45. Yunjie, L., Minglu, J., & Chengyi, C. (2009). Modified weighted centroid localization algorithm based on RSSI for WSN. Chinese Journal of Sensors and Actuators, 23(5), 717–721.

    Google Scholar 

  46. Zheng, Z. (2008). Self-localization technologies for wireless sensor network nodes. ZTE Communications, 11(4), 51–56.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Engineering and Technology, ITER, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, 751030, India

    Rahul Priyadarshi

  2. Department of Computer Science and Engineering, C. V. Raman Global University, Bhubaneswar, 752054, India

    Raj Vikram

Authors
  1. Rahul Priyadarshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Raj Vikram
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rahul Priyadarshi.

Ethics declarations

Conflict of interest

The authors have no conflict of interest to declare that are relevant to the content of this article.

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 (e.g. a society or other partner) 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

Priyadarshi, R., Vikram, R. A Triangle-Based Localization Scheme in Wireless Multimedia Sensor Network. Wireless Pers Commun 133, 525–546 (2023). https://doi.org/10.1007/s11277-023-10777-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-023-10777-7

Keywords

Search

Navigation