Skip to main content

Advertisement

Log in

Leveraging data aggregation algorithm in LoRa networks

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Long Range (LoRa) is an interference-free, single-hop, low-power wide area network (LPWAN) technology. LoRa offers customization of its physical layer parameters like spreading factor, coding rate, bandwidth, and transmission power to achieve high network coverage. Requirements for high coverage precipice the problem of high energy consumption. As a solution, our article presents an energy-efficient data packet aggregation scheme for LoRa communication to reduce high energy consumption. We propose a load balancing algorithm that yields better results in network communication. On comparing data aggregation strategy in LoRa with conventional star connected LoRa communication, and with other existing protocols our approach shows significant improvement in performance and energy saving. By adopting our strategy, conventional LoRa networks can achieve a longer lifetime and network stability.

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

Access this article

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. Yannuzzi M, Milito R, Serral-Gracià R, Montero D, Nemirovsky M (2014) Key ingredients in an IoT recipe: fog computing, cloud computing, and more fog computing. In: 2014 IEEE 19th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD) pp. 325–329. IEEE

  2. De Donno M, Tange K, Dragoni N (2019) Foundations and evolution of modern computing paradigms: Cloud, IoT, edge, and fog. IEEE Access 7:150936–150948

    Article  Google Scholar 

  3. Barot V, Kapadia V, Pandya S (2020) QoS enabled IoT based low cost air quality monitoring system with power consumption optimization. Cybern Inf Technol 20(2):122–140

    Google Scholar 

  4. Brogi A, Forti S (2017) QoS-aware deployment of IoT applications through the fog. IEEE Internet Things J 4(5):1185–1192

    Article  Google Scholar 

  5. Dhand G, Tyagi S (2016) Data aggregation techniques in WSN: survey. Procedia Comput Sci 92:378–384

    Article  Google Scholar 

  6. Pourghebleh B, Navimipour NJ (2017) Data aggregation mechanisms in the internet of things: a systematic review of the literature and recommendations for future research. J Netw Comput Appl 97:23–34

    Article  Google Scholar 

  7. Sasirekha S, Swamynathan S (2015) A comparative study and analysis of data aggregation techniques in WSN. Indian J Sci Technol 8(26):1–10

    Article  Google Scholar 

  8. Dehkordi SA, Farajzadeh K, Rezazadeh J, Farahbakhsh R, Sandrasegaran K, Dehkordi MA (2020) A survey on data aggregation techniques in IoT sensor networks. Wirel Netw 26(2):1243–1263

    Article  Google Scholar 

  9. Alliance L (2015) White paper: a technical overview of Lora and Lorawan. The LoRa Alliance, San Ramon, pp 7–11

    Google Scholar 

  10. Bor MC, Roedig U, Voigt T, Alonso JM (2016) Do Lora low-power wide-area networks scale? In: Proceedings of the 19th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, pp 59–67

  11. Bor M, Roedig U (2017) Lora transmission parameter selection. In: 2017 13th International Conference on Distributed Computing in Sensor Systems (DCOSS), pp 27–34. IEEE

  12. Ducrot N, Ray D, Saadani A (2016) LoRa device developer guide, Orange

  13. Farooq MO (2020) Clustering-based layering approach for uplink multi-hop communication in Lora networks. IEEE Netw Lett 2(3):132–135

    Article  MathSciNet  Google Scholar 

  14. Dias J, Grilo A (2020) Multi-hop Lorawan uplink extension: specification and prototype implementation. J Ambient Intell Hum Comput 11(3):945–959

    Article  Google Scholar 

  15. Krishnamachari L, Estrin D, Wicker S (2002) The impact of data aggregation in wireless sensor networks. In: Proceedings 22nd International Conference on Distributed Computing Systems Workshops, pp 575–578. IEEE

  16. Kuo T-W, Lin KC-J, Tsai M-J (2015) On the construction of data aggregation tree with minimum energy cost in wireless sensor networks: Np-completeness and approximation algorithms. IEEE Trans Comput 65(10):3109–3121

    Article  MathSciNet  Google Scholar 

  17. Zguira Y, Rivano H, Meddeb A (2018) Internet of bikes: a DTN protocol with data aggregation for urban data collection. Sensors 18(9):2819

    Article  Google Scholar 

  18. Tran HP, Jung W-S, Yoon T, Yoo D-S, Oh H (2020) A two-hop real-time Lora protocol for industrial monitoring and control systems. IEEE Access 8:126239–126252

    Article  Google Scholar 

  19. Dwijaksara MH, Jeon WS, Jeong DG (2019) Multihop gateway-to-gateway communication protocol for Lora networks. In: 2019 IEEE International Conference on Industrial Technology (ICIT), pp 949–954. IEEE

  20. Madureira ALR, Araújo FRC, Sampaio LN (2020) On supporting IoT data aggregation through programmable data planes. Comput Netw 177:107330

    Article  Google Scholar 

  21. Egidius PM, Abu-Mahfouz AM, Ndiaye M, Hancke GP (2019) Data aggregation in software-defined wireless sensor networks: a review. In: 2019 IEEE International Conference on Industrial Technology (ICIT), pp 1749–1754 . IEEE

  22. Gupta S, Snigdh I (2021) Clustering in Lora networks, an energy-conserving perspective. Wirel Pers Commun 122:1–14

    Google Scholar 

  23. Alenezi M, Chai KK, Jimaa S, Chen Y (2019) Use of unsupervised learning clustering algorithm to reduce collisions and delay within Lora system for dense applications. In: 2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pp 1–5. IEEE

Download references

Funding

No funding was received for conducting this study.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sakshi Gupta.

Ethics declarations

Conflict of interest

The authors have no conflict of interest to declare.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gupta, S., Snigdh, I. Leveraging data aggregation algorithm in LoRa networks. J Supercomput 78, 16861–16875 (2022). https://doi.org/10.1007/s11227-022-04534-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-022-04534-z

Keywords

Navigation