Skip to main content
SpringerLink
Log in

Arduino-based low-cost electrical load tracking system with a long-range mesh network

  • Published:
Advances in Manufacturing Aims and scope Submit manuscript

A Correction to this article was published on 28 December 2020

This article has been updated

Abstract

A system that combines the advantage of the long-range (LoRa) communication method and the structural characteristics of a mesh network for an LoRa mesh network-based wireless electrical load tracking system is proposed. The system demonstrates considerable potential in reducing data loss due to environmental factors in far-field wireless energy monitoring. The proposed system can automatically control the function of each node by confirming the data source and eventually adjust the system structure according to real-time monitoring data without manual intervention. To further improve the sustainability of the system in outdoor environments, a standby equipment is designed to automatically ensure the normal operation of the system when the hardware of the base station fails. Our system is based on the Arduino board, which lowers the production cost and provides a simple manufacturing process. After conducting a long-term monitoring of a near-field smart manufacturing process in South Korea and the far-field energy consumption of rural households in Tanzania, we have proven that the system can be implemented in most regions, neither confined to a specific geographic location nor limited by the development of local infrastructure. This system comprises a smart framework that improves the quality of energy monitoring. Finally, the proposed big-data-technology-based power supply policy offers a new approach for prolonging the power supply time of off-grid power plants, thereby providing a guideline for more rural areas with limited power sources to utilize uninterrupted electricity.

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

Similar content being viewed by others

Change history

References

  1. John C, Paul P, Jim D et al (2016) International energy outlook 2016 with projections to 2040. USDOE Energy Information Administration (EIA), Washington, DC

    Google Scholar 

  2. Hansen UE, Pedersen MB, Nygaard I (2015) Review of solar PV policies, interventions and diffusion in East Africa. Renew Sustain Energy Rev 46:236–248

    Article  Google Scholar 

  3. Bhandari B, Lee KT, Chu WS et al (2017) Socio-economic impact of renewable energy-based power system in mountainous villages of Nepal. Int J Precis Eng Manuf Green Technol 4(1):37–44

    Article  Google Scholar 

  4. Bhandari B, Ahn SH, Ahn TB (2016) Optimization of hybrid renewable energy power system for remote installations: case studies for mountain and island. Int J Precis Eng Manuf 17:815–822

    Article  Google Scholar 

  5. Park JW, Yi HC, Park MW et al (2016) A monitoring system architecture and calculation of practical recycling rate for end-of-life vehicle recycling in Korea. Int J Precis Eng Manuf Green Technol 1(1):49–57

    Article  Google Scholar 

  6. Chauhan A, Saini RP (2016) Renewable energy based off-grid rural electrification in Uttarakhand state of India: technology options, modelling method, barriers and recommendations. Renew Sustain Energy Rev 51:662–681

    Article  Google Scholar 

  7. Ravi T, Girish K, Chetan S (2014) Remote monitoring for solar photovoltaic systems in rural application using GSM voice channel. Energy Procedia 57:1526–1535

    Article  Google Scholar 

  8. Parikh P, Mitalkumar K, Sidhu TS (2010) Opportunities and challenges of wireless communication technologies for smart grid applications. In: 2010 IEEE PES general meeting, pp 1–7

  9. Kim WH, Lee S, Hwang J (2011) Realtime energy monitoring and controlling system based on Zigbee sensor networks. Procedia Comput Sci 5:794–797

    Article  Google Scholar 

  10. Elkhorchani H, Grayaa K (2016) Novel home energy management system using wireless communication technologies for carbon emission reduction within a smart grid. J Clean Prod 135:950–962

    Article  Google Scholar 

  11. Zou H, Zhou Y, Jiang H et al (2018) A WiFi-based occupancy-driven lighting control system for smart building. Energy Build 158:924–938

    Article  Google Scholar 

  12. Ascensión LV, Manuel F, Marta V (2018) IoT application for real-time monitoring of solar home systems based on Arduino™ with 3G connectivity. IEEE Sens J 19:679–691

    Google Scholar 

  13. Bhandari B, Lee KT, Lee CS et al (2014) A novel off-grid hybrid power system comprised of solar photovoltaic, wind, and hydro energy sources. Appl Energy 133:236–242

    Article  Google Scholar 

  14. Lee JS, Su YW, Shen CC (2007) A comparative study of wireless protocols: Bluetooth, UWB, ZigBee, and Wi-Fi. In: 2007 33rd annual conference of the IEEE industrial electronics society, pp 46–51

  15. IEEE Standard for Information technology—telecommunications and information exchange between systems local and metropolitan area networks— specific requirements - Part 11: wireless LAN medium access control (MAC) and physical layer (PHY) specifications. In: IEEE Std 802.11-2016 (Revision of IEEE Std 802.11-2012), pp 1–3534. https://doi.org/10.1109/IEEESTD.2016.7786995

  16. Hornbuckle CA (2010) Fractional-N synthesized chirp generator. United States Patent US7791415B2, Semtech Corp (May 2007)

  17. Khaled A, Moustafa Y, Mohamed Y (2002) Energyaware TDMA-based MAC for sensor networks. In: System-level power optimization for wireless multimedia communication, pp 21–40

  18. Gaddam SR, Phoha VV, Balagani KS (2007) K-means+ ID3: a novel method for supervised anomaly detection by cascading K-means clustering and ID3 decision tree learning methods. IEEE Trans Knowl Data Eng 19(3):345–354

    Article  Google Scholar 

  19. Amorim RC, Hennig C (2015) Recovering the number of clusters in data sets with noise features using feature rescaling factors. Inf Sci 324:126–145

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

Funding was provided by the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (MSIP) (Grant No. NRF-2017K1A3A9A04013801) and the Applied Basic Research Foundation of Yunnan Province (CN) (Grant No. 2018R1A4A1059976).

Author information

Authors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul, South Korea

    Xin-Lin Wang, Bora Ha, Frank Andrew Manongi, Woo-Kyun Jung & Sung-Hoon Ahn

  2. School of Materials Energy Water and Environment and Environmental Sciences, Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania

    Yusufu Abeid Chande Jande

  3. Water Infrastructure and Sustainable Energy Futures Centre, Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania

    Yusufu Abeid Chande Jande

  4. Institute of Advanced Machinery and Design, Seoul National University, Seoul, South Korea

    Sung-Hoon Ahn

Authors
  1. Xin-Lin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bora Ha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Frank Andrew Manongi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Woo-Kyun Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yusufu Abeid Chande Jande
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sung-Hoon Ahn
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sung-Hoon Ahn.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, XL., Ha, B., Manongi, F.A. et al. Arduino-based low-cost electrical load tracking system with a long-range mesh network. Adv. Manuf. 9, 47–63 (2021). https://doi.org/10.1007/s40436-020-00310-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40436-020-00310-5

Keywords

Search

Navigation