Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

An Approach to Optimize LoRa Network Performance for Efficient IoT Applications

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

The evolution of LoRa for wireless sensor networks focused on IoT concept is the most adopted LPWAN technology as it has the potential to resolve the requisites of diverse IoT applications. There has been extensive research for analyzing and optimizing LoRa network performance over 868 and 915 MHz ISM bands. However, the IN865-867 channel plan has been less explored. Thus, the current work analyses and optimizes the performance of the LoRa network in terms of time on air (TOA), received power, and received signal strength indicator (RSSI) over IN865-867 channel plan. A mathematical model relating to transmission rate is formulated where the dependence of TOA, received power, and RSSI on the transmission parameters such as bandwidth, coding rate, and spreading factor is evaluated. Artificial Neural Network is implemented using nftool for simulation in MATLAB and the mean square error value (MSE) is obtained. MSE is further utilized to optimize TOA, received power, and RSSI. A critical comparative analysis is carried out to illustrate the benefits of the proposed approach with that of the existing LoRa network. Simulation results show that TOA, received power and RSSI improves by 48, 12 and 16% respectively.

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

Access this article

Log in via an institution

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Availability of Data and Material

Not applicable.

Code Availability

Yes.

References

  1. Kingsy Grace, R., & Manju, S. (2019, October 1). A Comprehensive Review of Wireless Sensor Networks Based Air Pollution Monitoring Systems. Wireless Personal Communications. Springer New York LLC. Doi: https://doi.org/10.1007/s11277-019-06535-3

  2. Vikash, Mishra, L., & Varma, S. (2020, August 25). Middleware Technologies for Smart Wireless Sensor Networks towards Internet of Things: A Comparative Review. Wireless Personal Communications. Springer. Doi: https://doi.org/10.1007/s11277-020-07748-7

  3. Akpakwu, G. A., Silva, B. J., Hancke, G. P., & Abu-Mahfouz, A. M. (2017). A survey on 5G networks for the internet of things: Communication technologies and challenges. IEEE Access, 6, 3619–3647. https://doi.org/10.1109/ACCESS.2017.2779844

    Article  Google Scholar 

  4. Dai, H. N., Zheng, Z., & Zhang, Y. (2019). Blockchain for internet of things: A survey. IEEE Internet of Things Journal, 6(5), 8076–8094. https://doi.org/10.1109/JIOT.2019.2920987

    Article  Google Scholar 

  5. Liu, Z. (2018). Research on the internet of things and the development of smart city industry based on big data. Cluster Computing, 21(1), 789–795. https://doi.org/10.1007/s10586-017-0910-8

    Article  Google Scholar 

  6. Qadir, Q. M., Rashid, T. A., Al-Salihi, N. K., Ismael, B., Kist, A. A., & Zhang, Z. (2018). Low power wide area networks: A survey of enabling technologies, applications and interoperability needs. IEEE Access, 6, 77454–77473. https://doi.org/10.1109/ACCESS.2018.2883151

    Article  Google Scholar 

  7. Raza, U., Kulkarni, P., & Sooriyabandara, M. (2017). Low power wide area networks: An overview. IEEE Communications Surveys and Tutorials, 19(2), 855–873. https://doi.org/10.1109/COMST.2017.2652320

    Article  Google Scholar 

  8. Mekki, K., Bajic, E., Chaxel, F., & Meyer, F. (2019). A comparative study of LPWAN technologies for large-scale IoT deployment. ICT Express, 5(1), 1–7. https://doi.org/10.1016/j.icte.2017.12.005

    Article  Google Scholar 

  9. Qin, Z., Liu, Y., Li, G. Y., & McCann, J. A. (2017). Modelling and analysis of low-power wide-area networks. In IEEE International Conference on Communications. Institute of Electrical and Electronics Engineers Inc. Doi: https://doi.org/10.1109/ICC.2017.7996589

  10. Adelantado, F., Vilajosana, X., Tuset-Peiro, P., Martinez, B., & Melia, J. (2016). Understanding the limits of LoRaWAN. In: Proceedings of the 2016 International Conference on Embedded Wireless Systems and Networks, (September), pp. 8–12. Retrieved from: http://dl.acm.org/citation.cfm?id=2893711.2893802

  11. Centenaro, M., Vangelista, L., Zanella, A., & Zorzi, M. (2016). Long-range communications in unlicensed bands: The rising stars in the IoT and smart city scenarios. IEEE Wireless Communications, 23(5), 60–67. https://doi.org/10.1109/MWC.2016.7721743

    Article  Google Scholar 

  12. Ikpehai, A., Adebisi, B., Rabie, K. M., Anoh, K., Ande, R. E., Hammoudeh, M., & Mbanaso, U. M. (2019). Low-power wide area network technologies for internet-of-things: A comparative review. IEEE Internet of Things Journal, 6(2), 2225–2240. https://doi.org/10.1109/JIOT.2018.2883728

    Article  Google Scholar 

  13. Kufakunesu, R., Hancke, G. P., & Abu-Mahfouz, A. M. (2020). A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges. Sensors 2020, Vol. 20, Page 5044, 20(18), 5044. Doi: https://doi.org/10.3390/S20185044

  14. Ghanbari, Z., Jafari Navimipour, N., Hosseinzadeh, M., & Darwesh, A. (2019). Resource allocation mechanisms and approaches on the internet of things. Cluster Computing, 22(4), 1253–1282. https://doi.org/10.1007/s10586-019-02910-8

    Article  Google Scholar 

  15. Shanmuga Sundaram, J. P., Du, W., & Zhao, Z. (2020). A survey on LoRa networking: Research problems, current solutions, and open issues. IEEE Communications Surveys and Tutorials, 22(1), 371–388. https://doi.org/10.1109/COMST.2019.2949598

    Article  Google Scholar 

  16. Fialho, V., & Azevedo, F. (2018). Wireless Communication Based on Chirp Signals for LoRa IoT Devices. i-ETC : ISEL Academic Journal of Electronics Telecommunications and Computers, 4(1), ID-6.

  17. LoRa Frequency Bands in India | LoRa | LoRaWAN - Ensemble Tech. (n.d.). Retrieved March 13, 2020, from http://www.ensembletech.in/lora-frequency-bands-india/

  18. Sandoval, R. M., Garcia-Sanchez, A. J., & Garcia-Haro, J. (2019). Performance optimization of LoRa nodes for the future smart city/industry. Eurasip Journal on Wireless Communications and Networking, 2019(1), 1–13. https://doi.org/10.1186/s13638-019-1522-1

    Article  Google Scholar 

  19. Hoeller, A., Souza, R. D., Alcaraz López, O. L., Alves, H., De Noronha Neto, M., & Brante, G. (2018). Analysis and performance optimization of LoRa networks with time and antenna diversity. IEEE Access, 6, 32820–32829. https://doi.org/10.1109/ACCESS.2018.2839064

    Article  Google Scholar 

  20. Sun, Y., Hu, J., Liu, Y., & Tian, Z. (2017). Theoretical analysis and performance testing of LoRa technology. Proceedings - 2017 International Conference on Computer Technology, Electronics and Communication, ICCTEC 2017, 686–690. Doi: https://doi.org/10.1109/ICCTEC.2017.00153

  21. Bor, M., & Roedig, U. (2018). LoRa transmission parameter selection. Proceedings - 2017 13th International Conference on Distributed Computing in Sensor Systems, DCOSS 2017, 2018-Janua, 27–34. Doi: https://doi.org/10.1109/DCOSS.2017.10

  22. Lavric, A., & Popa, V. (2017). A LoRaWAN: Long Range Wide Area Networks study. 2017 11th International Conference on Electromechanical and Power Systems, SIELMEN 2017 - Proceedings, 2017-Janua, 417–420. Doi: https://doi.org/10.1109/SIELMEN.2017.8123360

  23. Sandoval, R. M., Garcia-Sanchez, A. J., & Garcia-Haro, J. (2019). Optimizing and updating LoRa communication parameters: A Machine Learning approach. IEEE Transactions on Network and Service Management, 16(3), 884–895. https://doi.org/10.1109/TNSM.2019.2927759

    Article  Google Scholar 

  24. Adnan, Rizal, M., & Ilham, A. A. (2018). Performance of LoRa Gateway based Energy Consumption and Different Frame Sizes. Proceedings - 2nd East Indonesia Conference on Computer and Information Technology: Internet of Things for Industry, EIConCIT 2018, 159–162. Doi: https://doi.org/10.1109/EIConCIT.2018.8878628

  25. Behera, T. M., Samal, U. C., & Mohapatra, S. K. (2018). Energy-efficient modified LEACH protocol for IoT application. IET Wireless Sensor Systems, 8(5), 223–228. https://doi.org/10.1049/iet-wss.2017.0099

    Article  Google Scholar 

  26. Bouguera, T., Diouris, J. F., Chaillout, J. J., Jaouadi, R., & Andrieux, G. (2018). Energy consumption model for sensor nodes based on LoRa and LoRaWAN. Sensors (Switzerland), 18(7), 1–23. https://doi.org/10.3390/s18072104

    Article  Google Scholar 

  27. Ali, Z., Henna, S., Akhunzada, A., Raza, M., & Kim, S. W. (2019). Performance Evaluation of LoRaWAN for Green Internet of Things. IEEE Access, 7, 164102–164112. https://doi.org/10.1109/ACCESS.2019.2943720

    Article  Google Scholar 

  28. Waret, A., Kaneko, M., Guitton, A., & El Rachkidy, N. (2019). LoRa throughput analysis with imperfect spreading factor orthogonality. IEEE Wireless Communications Letters, 8(2), 408–411. https://doi.org/10.1109/LWC.2018.2873705

    Article  Google Scholar 

  29. Elshabrawy, T., & Robert, J. (2019). Interleaved chirp spreading LoRa-based modulation. IEEE Internet of Things Journal, 6(2), 3855–3863. https://doi.org/10.1109/JIOT.2019.2892294

    Article  Google Scholar 

  30. Leonardi, L., Battaglia, F., & Lo Bello, L. (2019). RT-LoRa: A medium access strategy to support real-time flows over LoRa-based networks for industrial IoT applications. IEEE Internet of Things Journal, 6(6), 10812–10823. https://doi.org/10.1109/JIOT.2019.2942776

    Article  Google Scholar 

  31. Ertürk, M. A., Aydın, M. A., Büyükakkaşlar, M. T., & Evirgen, H. (2019). A Survey on LoRaWAN architecture. Protocol and Technologies. Future Internet, 11(10), 216. https://doi.org/10.3390/fi11100216

    Article  Google Scholar 

  32. Shi, Y., Shi, W., Liu, X., & Xiao, X. (2020). An RSSI classification and tracing algorithm to improve trilateration-based positioning. Sensors (Basel, Switzerland), 20(15), 1–17. https://doi.org/10.3390/S20154244

    Article  Google Scholar 

  33. Savazzi, P., Goldoni, E., Vizziello, A., Favalli, L., & Gamba, P. (2019). A wiener-based rssi localization algorithm exploiting modulation diversity in lora networks. IEEE Sensors Journal, 19(24), 12381–12388. https://doi.org/10.1109/JSEN.2019.2936764

    Article  Google Scholar 

  34. Zappone, A., Di Renzo, M., Debbah, M., Lam, T. T., & Qian, X. (2019). Model-aided wireless artificial intelligence: Embedding expert knowledge in deep neural networks for wireless system optimization. IEEE Vehicular Technology Magazine, 14(3), 60–69. https://doi.org/10.1109/MVT.2019.2921627

    Article  Google Scholar 

  35. Chen, M., Challita, U., Saad, W., Yin, C., & Debbah, M. (2019). Artificial neural networks-based machine learning for wireless networks: A TUTORIAL. IEEE Communications Surveys and Tutorials, 21(4), 3039–3071. https://doi.org/10.1109/COMST.2019.2926625

    Article  Google Scholar 

  36. Sun, Y., Peng, M., Zhou, Y., Huang, Y., & Mao, S. (2019). Application of machine learning in wireless networks: Key techniques and open issues. IEEE Communications Surveys and Tutorials, 21(4), 302–3108. https://doi.org/10.1109/COMST.2019.2924243

    Article  Google Scholar 

  37. Chaudhary, S. K., Sharma, J., Gupta, D. K., Srivastava, P. K., Prasad, R., & Pandey, D. K. (2021). Artificial neural network for the estimation of soil moisture using earth observation datasets. Agricultural Water Management, 227–239,. https://doi.org/10.1016/B978-0-12-812362-1.00012-6

  38. Xu, C., Gordan, B., Koopialipoor, M., Armaghani, D. J., Tahir, M. M., & Zhang, X. (2019). Improving performance of retaining walls under dynamic conditions developing an optimized ANN based on ant colony optimization technique. IEEE Access, 7, 94692–94700. https://doi.org/10.1109/ACCESS.2019.2927632

    Article  Google Scholar 

  39. Salahat, S. (2017). Short-term forecasting of electricity consumption in palestine using artificial neural networks. International Journal of Artificial Intelligence & Applications, 8(2), 11–21. https://doi.org/10.5121/ijaia.2017.8202

    Article  Google Scholar 

  40. Dev, K., Maddikunta, P. K. R., Gadekallu, T. R., Bhattacharya, S., Hegde, P., & Singh, S. (2022). Energy optimization for green communication in IoT using harris hawks optimization. IEEE Transactions on Green Communications and Networking, 6(2), 685–694. https://doi.org/10.1109/TGCN.2022.3143991

    Article  Google Scholar 

  41. Pingale, R. P., & Shinde, S. N. (2021). Multi-objective Sunflower Based Grey Wolf Optimization Algorithm for Multipath Routing in IoT Network. Wireless Personal Communications 2021 117:3, 117(3), 1909–1930. Doi: https://doi.org/10.1007/S11277-020-07951-6

  42. Kumar, M., Kashyap, P. K., & Kumar, S. (2021). Fuzzy Q-Reinforcement Learning-Based Energy Optimization in IoT Network. Lecture Notes in Networks and Systems, 185 LNNS, 139–153. Doi: https://doi.org/10.1007/978-981-33-6081-5_13/COVER

  43. Oun, A. (2022). Hardware Security Design, and Vulnerability Analysis of FPGA based PUFs to Machine Learning and Swarm Intelligence based ANN Algorithm Attacks. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=toledo1651595714554771

Download references

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Amity University, Sector-125, Noida, India

    Gagandeep Kaur & Sindhu Hak Gupta

  2. Department of Computer Science and Engineering, School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi, India

    Harleen Kaur

Authors
  1. Gagandeep Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sindhu Hak Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Harleen Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version.

Corresponding author

Correspondence to Gagandeep Kaur.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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

Kaur, G., Gupta, S.H. & Kaur, H. An Approach to Optimize LoRa Network Performance for Efficient IoT Applications. Wireless Pers Commun 128, 209–229 (2023). https://doi.org/10.1007/s11277-022-09950-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-022-09950-1

Keywords

Search

Navigation