Skip to main content

Advertisement

SpringerLink
Log in

Distance Estimation-Based PSO Between Patient with Alzheimer’s Disease and Beacon Node in Wireless Sensor Networks

  • Research Article-Electrical Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

In recent years, research in wireless sensor networks and their application in health care and environmental monitoring have attracted significant interest. In such applications, the accuracy of the distance estimation between a patient and a beacon node is crucial for determining patient location. In this study, the distance between the mobile node (carried by the Alzheimer’s patient) and the beacon node was measured using the received signal strength indicator (RSSI) with ZigBee technology in indoor environments. The distance estimation was determined by two path loss models: a log-normal shadowing model (LNSM) and a derived model using a polynomial function (the POLYN function) obtained with the MATLAB curve fitting tool. Next, particle swarm optimization (PSO) was merged with the polynomial function (called the PSO–POLYN function) to obtain the optimal coefficient values for the POLYN function. The resulting path loss model can improve the distance error between a patient with Alzheimer’s disease and a beacon node. The results revealed that the merging of the PSO–POLYN model enhanced the mean absolute error (MAE) by 20% relative to the LNSM, where the MAE for distance was 1.6 m for the PSO–POLYN model and 2 m for the LNSM. In addition, after applying PSO, the correlation coefficient (R2) of the regression line between RSSI and the estimated distance improved to 0.99, while that obtained through the LNSM was 0.94. The presented method based on PSO–POLYN outperformed models in the literature, both in terms of MAE and correlation coefficient in indoor environments.

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
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Fakhri, A.B.; Gharghan, S.K.; Mohammed, S.L.: Path-loss modelling for WSN deployment in indoor and outdoor environments for medical applications. Int. J. Eng. Technol. 7(3), 1666–1671 (2018)

    Google Scholar 

  2. Alobaidy, H.A.; Mandeep, J.; Nordin, R.; Abdullah, N.F.: A review on ZigBee based WSNs: concepts, infrastructure, applications, and challenges. Int. J. Electric. Electron. Eng. Telecommun. 9, 189–198 (2019). https://doi.org/10.18178/ijeetc.190907

    Article  Google Scholar 

  3. Musayyanah, M.; Susanto, P.; Kusumawati, W.I.: Delay analysis in ZigBee wireless communication for manipulated data and finger clip sensor data using XBee Pro S2C. J. Electric. Eng. Comput. Sci. 2(2), 247–254 (2017)

    Google Scholar 

  4. Cicioğlu, M.; Çalhan, A.: Channel aware wireless body area network with cognitive radio technology in disaster cases. Int. J. Commun Syst 33, e4565 (2020). https://doi.org/10.1002/dac.4565

    Article  Google Scholar 

  5. Ceken, C.: An energy efficient and delay sensitive centralized MAC protocol for wireless sensor networks. Comput Stand Interfaces 30(1–2), 20–31 (2008)

    Google Scholar 

  6. Wu, P.; Su, S.; Zuo, Z.; Guo, X.; Sun, B.; Wen, X.: Time difference of arrival (TDoA) localization combining weighted least squares and firefly algorithm. Sensors 19(11), 2554 (2019)

    Google Scholar 

  7. Li, W.; Li, Y.; Wei, P.; Tai, H.-M.: A closed-form localization algorithm using angle-of-arrival and difference time of scan time measurements in scan-based radar. IEEE Trans. Aerosp. Electron. Syst. 55(1), 511–515 (2019)

    Google Scholar 

  8. Mahapatra, R.K.; Shet, N.: Experimental analysis of RSSI-based distance estimation for wireless sensor networks. In: IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), Mangalore, India, 13–14 Aug. 2016, pp. 211–215. IEEE

  9. Gharghan, S.K.; Nordin, R.; Ismail, M.; Ali, J.A.: Accurate wireless sensor localization technique based on hybrid PSO-ANN algorithm for indoor and outdoor track cycling. IEEE Sens. J. 16(2), 529–541 (2016). https://doi.org/10.1109/JSEN.2015.2483745

    Article  Google Scholar 

  10. Shue, S.; Johnson, L.E.; Conrad, J.M.: Utilization of XBee ZigBee modules and MATLAB for RSSI localization applications. In: SoutheastCon 2017, Charlotte, NC, USA, 30 March–2 April 2017, pp. 1–6. IEEE

  11. Ou, C.-W.; Chao, C.-J.; Chang, F.-S.; Wang, S.-M.; Liu, G.-X.; Wu, M.-R.; Cho, K.-Y.; Hwang, L.-T.; Huan, Y.-Y.: A ZigBee position technique for indoor localization based on proximity learning. In: 2017 IEEE International Conference on Mechatronics and Automation (ICMA), Takamatsu, Japan, 6–9 Aug. 2017, pp. 875–880. IEEE

  12. Hevrdejs, K.; Knoll, J.; Miah, S.: A ZigBee-based framework for approximating sensor range and bearing. In: IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), Windsor, ON, Canada, 30 April–3 May 2017, pp. 1–4. IEEE

  13. Hashim, H.A.; Mohammed, S.L.; Gharghan, S.K.: Path loss model-based PSO for accurate distance estimation in indoor environments. J. Commun. 13(12), 712–722 (2018)

    Google Scholar 

  14. Phunthawornwong, M.; Pengwang, E.; Silapunt, R.: Indoor location estimation of wireless devices using the log-distance path loss model. In: IEEE Region 10 Conference TENCON, Jeju, Korea (South), Korea (South), 28–31 Oct. 2018, pp 0499–0502. IEEE

  15. Omer, M.; Tian, G.Y.: Indoor distance estimation for passive UHF RFID tag based on RSSI and RCS. Measurement 127, 425–430 (2018)

    Google Scholar 

  16. Mounir, T.A.; Mohamed, P.S.; Cherif, B. Amar, B.: Positioning system for emergency situation based on RSSI measurements for WSN. In: International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN), Paris, France, 28–30 Nov. 2017, pp. 1–6. IEEE

  17. Sung, Y.: RSSI-based distance estimation framework using a Kalman filter for sustainable indoor computing environments. Sustainability 8(11), 1136 (2016)

    Google Scholar 

  18. Park, J.; Kim, J.; Kang, S.: BLE-based accurate indoor location tracking for home and office. Comput. Sci. Inf. Technol. (CS&IT) 2015, 173–182 (2015)

    Google Scholar 

  19. Taniura, Y.; Oguchi, K.: Indoor location recognition method using RSSI values in system with small wireless nodes. In: 40th International Conference on Telecommunications and Signal Processing (TSP), Barcelona, Spain, 5–7 July 2017, pp. 52–55. IEEE

  20. Xu, J.; He, J.; Zhang, Y.; Xu, F.; Cai, F.: A distance-based maximum likelihood estimation method for sensor localization in wireless sensor networks. Int. J. Distrib. Sens. Netw. 12(4), 2080536 (2016)

    Google Scholar 

  21. Li, G.; Geng, E.; Ye, Z.; Xu, Y.; Lin, J.; Pang, Y.: Indoor positioning algorithm based on the improved RSSI distance model. Sensors 18(9), 2820 (2018)

    Google Scholar 

  22. Grzechca, D.E.; Pelczar, P.; Chruszczyk, L.: Analysis of object location accuracy for iBeacon technology based on the RSSI path loss model and fingerprint map. Int. J. Electron. Telecommun. 62(4), 371–378 (2016)

    Google Scholar 

  23. Luo, X.; O’Brien, W.J.; Julien, C.L.: Comparative evaluation of received signal-strength index (RSSI) based indoor localization techniques for construction jobsites. Adv. Eng. Inform. 25(2), 355–363 (2011)

    Google Scholar 

  24. Luca, D.G.; Alberto, M.: Towards accurate indoor localization using iBeacons, fingerprinting and particle filtering. In: International Conference on Indoor Positioning and Indoor Navigation (IPIN), Alcalá de Henares, Spain, 4–7 October 2016

  25. Peng, Y.; Fan, W.; Dong, X.; Zhang, X.: An iterative weighted KNN (IW-KNN) based indoor localization method in bluetooth low energy (BLE) environment. In: IEEE Conferences on Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), Toulouse, France, 18–21 July 2016 18–21 July 2016, pp. 794–800. https://doi.org/10.1109/uic-atc-scalcom-cbdcom-iop-smartworld.2016.0127

  26. Li, N.; Chen, J.; Yuan, Y.; Tian, X.; Han, Y.; Xia, M.: A Wi-Fi indoor localization strategy using particle swarm optimization based artificial neural networks. Int. J. Distrib. Sens. Netw. 12(3), 4583147 (2016)

    Google Scholar 

  27. Yu, Z.-z.; Guo, G.-z.: Improvement of positioning technology based on RSSI in ZigBee networks. Wirel. Pers. Commun. 95(3), 1943–1962 (2017)

    Google Scholar 

  28. Uradzinski, M.; Guo, H.; Liu, X.; Yu, M.: Advanced indoor positioning using zigbee wireless technology. Wirel. Pers. Commun. 97(4), 6509–6518 (2017)

    Google Scholar 

  29. Adewumi, O.G.; Djouani, K.; Kurien, A.M.: RSSI based indoor and outdoor distance estimation for localization in WSN. In: IEEE International Conference on Industrial Technology (ICIT), Cape Town, South Africa, 25–28 Feb. 2013 25–28 Feb. 2013, pp. 1534–1539. IEEE

  30. Daniş, F.S.; Cemgil, A.T.: Model-based localization and tracking using bluetooth low-energy beacons. Sensors 17(11), 2484 (2017)

    Google Scholar 

  31. Chen, L.; Pei, L.; Kuusniemi, H.; Chen, Y.; Kröger, T.; Chen, R.: Bayesian fusion for indoor positioning using bluetooth fingerprints. Wirel. Pers. Commun. 70(4), 1735–1745 (2013)

    Google Scholar 

  32. Awad, A.; Frunzke, T.; Dressler, F: Adaptive distance estimation and localization in WSN using RSSI measures. In: 10th Euromicro Conference on Digital System Design Architectures, Methods and Tools (DSD 2007), Lubeck, Germany, 29–31 Aug. 2007. pp. 471–478

  33. Azenha, A.; Peneda, L.; Carvalho, A.: A neural network approach for radio frequency based indoors localization. In: 38th Annual Conference on IEEE Industrial Electronics Society (IECON), Montreal, QC, Canada, 25–28 Oct. 2012 25–28 Oct. 2012, pp. 5990–5995. https://doi.org/10.1109/iecon.2012.6389103

  34. Gharghan, S.K.; Mohammed, S.L.; Al-Naji, A.; Abu-AlShaeer, M.J.; Jawad, H.M.; Jawad, A.M.; Chahl, J.: Accurate fall detection and localization for elderly people based on neural network and energy-efficient wireless sensor network. Energies 11(11), 2866 (2018)

    Google Scholar 

  35. Reis, S.; Pesch, D.; Wenning, B.; Kuhn, M.: Empirical path loss model for 2.4 GHz IEEE 802.15.4 wireless networks in compact cars. In: IEEE Wireless Communications and Networking Conference (WCNC), Barcelona, Spain, 15–18 April 2018 15–18 April 2018. pp. 1–6. https://doi.org/10.1109/wcnc.2018.8377277

  36. Miao, Q.; Huang, B.; Jia, B.: Estimating distances via received signal strength and connectivity in wireless sensor networks. Wirel. Netw. 26(2), 971–982 (2018)

    Google Scholar 

  37. Erdemir, E.; Tuncer, T.E.: Path planning for mobile-anchor based wireless sensor network localization: static and dynamic schemes. Ad Hoc Netw. 77, 1–10 (2018)

    Google Scholar 

  38. Cicioğlu, M.; Çalhan, A.: Internet of Things based Firefighters for Disaster Case Management. IEEE Sens. J. 21, 612–619 (2020). https://doi.org/10.1109/jsen.2020.3013333

    Article  Google Scholar 

  39. Jawad, H.M.; Jawad, A.M.; Nordin, R.; Gharghan, S.K.; Abdullah, N.F.; Ismail, M.; Abu-Al Shaeer, M.J.: Accurate empirical path-loss model based on particle swarm optimization for wireless sensor networks in smart agriculture. IEEE Sens. J. 20(1), 552–561 (2019)

    Google Scholar 

  40. Koopialipoor, M.; Fallah, A.; Armaghani, D.J.; Azizi, A.; Mohamad, E.T.: Three hybrid intelligent models in estimating flyrock distance resulting from blasting. Eng. Comput. 35(1), 243–256 (2019)

    Google Scholar 

  41. Nagireddy, V.; Parwekar, P.; Mishra, T.K.: Velocity adaptation based PSO for localization in wireless sensor networks. Evol. Intell. pp. 1–9 (2018)

  42. Edla, D.R.; Kongara, M.C.; Cheruku, R.: A PSO based routing with novel fitness function for improving lifetime of WSNs. Wirel. Pers. Commun. 104(1), 73–89 (2019)

    Google Scholar 

  43. Gumaida, B.F.; Luo, J.: A hybrid particle swarm optimization with a variable neighborhood search for the localization enhancement in wireless sensor networks. Appl. Intell. 49(10), 3539–3557 (2019)

    Google Scholar 

  44. Kaswan, A.; Singh, V.; Jana, P.K.: A multi-objective and PSO based energy efficient path design for mobile sink in wireless sensor networks. Pervasive Mob. Comput. 46, 122–136 (2018)

    Google Scholar 

  45. Phoemphon, S.; So-In, C.; Leelathakul, N.: A hybrid localization model using node segmentation and improved particle swarm optimization with obstacle-awareness for wireless sensor networks. Expert Syst. Appl. 143, 113044 (2020)

    Google Scholar 

  46. Zubaidi, S.L.; Gharghan, S.K.; Dooley, J.; Alkhaddar, R.M.; Abdellatif, M.: Short-term urban water demand prediction considering weather factors. Water Resour. Manage 32(14), 4527–4542 (2018). https://doi.org/10.1007/s11269-018-2061-y

    Article  Google Scholar 

  47. Tarrío, P.; Bernardos, A.M.; Casar, J.R.: An energy-efficient strategy for accurate distance estimation in wireless sensor networks. Sensors 12(11), 15438–15466 (2012)

    Google Scholar 

  48. Sahu, P.K.; Wu, E.H.; Sahoo, J.: DuRT: dual RSSI trend based localization for wireless sensor networks. IEEE Sens. J. 13(8), 3115–3123 (2013). https://doi.org/10.1109/JSEN.2013.2257731

    Article  Google Scholar 

  49. Mekki, K.; Bajic, E.; Meyer, F.: Indoor positioning system for IoT device based on BLE technology and MQTT protocol. In: IEEE 5th World Forum on Internet of Things (WF-IoT), Limerick, Ireland, Ireland, 15–18 April 2019 15–18 April 2019. pp. 787–792. https://doi.org/10.1109/wf-iot.2019.8767287

  50. Munadhil, Z.; Gharghan, S.K.; Mutlag, A.H.; Al-Naji, A.; Chahl, J.: Neural network-based Alzheimer’s patient localization for wireless sensor network in an indoor environment. IEEE Access 8, 150527–150538 (2020)

    Google Scholar 

  51. Wang, Y.; Hang, J.; Cheng, L.; Li, C.; Song, X.: A hierarchical voting based mixed filter localization method for wireless sensor network in mixed LOS/NLOS environments. Sensors 18(7), 2348 (2018)

    Google Scholar 

  52. Gumaida, B.F.; Luo, J.: Novel localization algorithm for wireless sensor network based on intelligent water drops. Wirel. Netw. 25(2), 597–609 (2019)

    Google Scholar 

  53. Luoh, L.: ZigBee-based intelligent indoor positioning system soft computing. Soft. Comput. 18(3), 443–456 (2014)

    Google Scholar 

  54. Barai, S.; Biswas, D.; Sau, B.: Estimate distance measurement using NodeMCU ESP8266 based on RSSI technique. In: IEEE Conference on Antenna Measurements and Applications (CAMA), Tsukuba, Japan, 4–6 Dec. 2017 4–6 Dec. 2017. pp 170–173. https://doi.org/10.1109/cama.2017.8273392

  55. Wang, Y.; Wu, X.; Cheng, L.: A novel non-line-of-sight indoor localization method for wireless sensor networks. J. Sens. 2018, 10 (2018)

    Google Scholar 

  56. Goldoni, E.; Prando, L.; Vizziello, A.; Savazzi, P.; Gamba, P.: Experimental data set analysis of RSSI-based indoor and outdoor localization in LoRa networks. Internet Technol. Lett. 2(1), e75 (2019)

    Google Scholar 

  57. Zhang, L.; Yang, Z.; Zhang, S.; Yang, H.: Three-dimensional localization algorithm of WSN nodes based on RSSI-TOA and single mobile anchor node. J. Electric. Comput. Eng. 2019, 8 (2019). https://doi.org/10.1155/2019/4043106

    Article  Google Scholar 

  58. Dong, Z.Y.; Xu, W.M.; Zhuang, H.: Research on ZigBee indoor technology positioning based on RSSI. Proc. Comput. Sci. 154, 424–429 (2019)

    Google Scholar 

  59. Lam, C.H.; She, J.: Distance estimation on moving object using BLE beacon. In: International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), Barcelona, Spain, Spain, 21–23 Oct. 2019, pp 1–6. IEEE

  60. Cannizzaro, D.; Zafiri, M.; Jahier Pagliari, D.; Patti, E.; Macii, E.; Poncino, M.; Acquaviva, A.: A comparison analysis of BLE-based algorithms for localization in industrial environments. Electronics 9(1), 44 (2019)

    Google Scholar 

  61. Ismail, M.I.M.; Dzyauddin, R.A.; Samsul, S.; Azmi, N.A.; Yamada, Y.; Yakub, M.F.M.; Salleh, N.A.B.A.: An RSSI-based Wireless Sensor Node Localisation using Trilateration and Multilateration Methods for Outdoor Environment. arXiv:191207801 (2019)

  62. Tian, Z.; Lian, Y.; Zhou, M.; Pu, Q.: SAPIL: single access point based indoor localisation using Wi-Fi L-shaped antenna array. IET Wirel. Sens. Syst. 9(3), 119–131 (2019)

    Google Scholar 

  63. Verma, V.; Singh, A.: Indoor location determination using radio signal strength model for distance estimation. In: International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, Tamil Nadu, India, India, 23–25 Jan. 2019 23–25 Jan. 2019, pp. 1–4. https://doi.org/10.1109/iccci.2019.8821939

  64. Subhan, F.; Ahmed, S.; Haider, S.; Saleem, S.; Khan, A.; Ahmed, S.; Numan, M.: Hybrid indoor position estimation using K-NN and MinMax. KSII Trans. Internet Inf. Syst. 13(9), 4408–4428 (2019)

    Google Scholar 

  65. Qiua, Q.; Daib, Z.: Research on RFID indoor positioning algorithm based on GRNN neural network. In: 2nd International Conference on Mechanical, Electronic and Engineering Technology (MEET 2019), Xi’an, Shaanxi, China, 19 January 2019. CSP, pp 464–470

  66. Abadleh, A.: Wi-Fi RSS-based approach for locating the position of indoor Wi-Fi access point. Commun. Sci. Lett. Univ. Zilina 21(4), 69–74 (2019)

    Google Scholar 

  67. Chen, P.; Zheng, X.; Gu, F.; Shang, J.: Path distance-based map matching for Wi-Fi fingerprinting positioning. Future Gener. Comput. Syst. 107, 82–94 (2020). https://doi.org/10.1016/j.future.2020.01.053

    Article  Google Scholar 

  68. Yu, X.; Zhang, Z.; Chai, R.: RSSI estimation for wireless sensor network through-the-earth communication at frequency 433 MHz. J. Intell. Fuzzy Syst. 38(2), 1401–1410 (2020)

    Google Scholar 

  69. Jondhale, S.R.; Shubair, R.; Labade, R.P.; Lloret, J.; Gunjal, P.R.: Application of supervised learning approach for target localization in wireless sensor network. In: Singh, P.K., Bhargava, B.K., Paprzycki, M., Kaushal, N.C., Hong, W.-C. (eds.) Handbook of wireless sensor networks: issues and challenges in current Scenario’s, pp. 493–519. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-40305-8_24

    Chapter  Google Scholar 

  70. Wang, B.; Gan, X.; Liu, X.; Yu, B.; Jia, R.; Huang, L.; Jia, H.: A novel weighted KNN algorithm based on RSS similarity and position distance for Wi-Fi fingerprint positioning. IEEE Access 8, 30591–30602 (2020)

    Google Scholar 

  71. Wang, Y.; Gao, J.; Li, Z.; Zhao, L.: Robust and accurate Wi-Fi fingerprint location recognition method based on deep neural network. Appl. Sci. 10(1), 321 (2020)

    Google Scholar 

  72. Huang, P.; Zhao, H.; Liu, W.; Jiang, D.: MAPS: indoor localization algorithm based on multiple AP selection. Mob. Netw. Appl., pp. 1–8 (2020)

  73. Spachos, P.; Plataniotis, K.N.: BLE beacons for indoor positioning at an interactive IoT-based smart museum. IEEE Syst. J. arXiv: 2001.07686v1 (2020)

  74. Wu, H.; Zhang, L.; Miao, Y.: The propagation characteristics of radio frequency signals for wireless sensor networks in large-scale farmland. Wirel. Pers. Commun. 95(4), 3653–3670 (2017)

    Google Scholar 

  75. Raheemah, A.; Sabri, N.; Salim, M.; Ehkan, P.; Ahmad, R.B.: New empirical path loss model for wireless sensor networks in mango greenhouses. Comput. Electron. Agric. 127, 553–560 (2016)

    Google Scholar 

  76. Cheffena, M.; Mohamed, M.: Empirical path loss models for wireless sensor network deployment in snowy environments. IEEE Antennas Wirel. Propag. Lett. 16, 2877–2880 (2017)

    Google Scholar 

  77. Olasupo, T.O.; Otero, C.E.; Olasupo, K.O.; Kostanic, I.: Empirical path loss models for wireless sensor network deployments in short and tall natural grass environments. IEEE Trans. Antennas Propag. 64(9), 4012–4021 (2016)

    MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

The author would like to thank the staff of the Department of Medical Instrumentation Techniques Engineering, Electrical Engineering Technical College, Middle Technical University for their support during this study.

Funding

This research received no external funding.

Author information

Authors and Affiliations

  1. Middle Technical University, Electrical Engineering Technical College, Baghdad, Iraq

    Zainab Munadhil, Sadik Kamel Gharghan & Ammar Hussein Mutlag

Authors
  1. Zainab Munadhil
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sadik Kamel Gharghan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ammar Hussein Mutlag
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

ZM contributed to conceptualization, methodology, data curation, resources, writing of original draft. SKG contributed to investigation, formal analysis, visualization, software, writing of original draft. AHM contributed to supervision, validation, resources, writing—reviewing and editing, writing of original draft.

Corresponding author

Correspondence to Sadik Kamel Gharghan.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this study.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Munadhil, Z., Gharghan, S.K. & Mutlag, A.H. Distance Estimation-Based PSO Between Patient with Alzheimer’s Disease and Beacon Node in Wireless Sensor Networks. Arab J Sci Eng 46, 9345–9362 (2021). https://doi.org/10.1007/s13369-020-05283-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-020-05283-y

Keywords

Search

Navigation