Skip to main content

Advertisement

SpringerLink
Log in

Reporting Mechanisms for Internet of Things

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

Energy saving is one of the most important issues for Internet of Things (IoT). An intuitive way to save energy of IoT devices is to reduce the reporting frequency to the IoT server. However, to do so, the time-variant values are distorted, which may be influential to the measured results. In this paper, we take PM2.5 application as an example to discuss the relation between energy efficiency and data accuracy. Through analyzing PM2.5 concentration collected via LoRa at National Chiao Tung University (NCTU) from 2016 to present, two reporting mechanisms based on timer and threshold, respectively, are proposed. The experimental results demonstrate that the threshold-based reporting outperforms the timer-based reporting by more than 37% in energy saving when the accuracies of these two reporting mechanisms are the same.

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

Similar content being viewed by others

Notes

  1. PM2.5 is the suspended particulate matter smaller than 2.5 micrometers in diameter.

  2. The maximum working power for PMS7003 is 100mA × 5V = 0.5W at most. The RF power with 20dBm for GL6509 is 0.45W. In other words, almost 50% of the device’s energy is consumed by the LoRa transmission.

  3. The observation period lasted 4 months with 174,240 sampled data (121 days × 24 hours × 60 minutes). Figure 3 illustrates partial results.

  4. Error reversal is defined as an incident where the reporting accuracy obtained by a smaller timer is worse than that by a larger timer.

  5. Here, we only demonstrate the results where the packet loss probability is set to 0.5. The results with other packet loss probabilities are similar.

References

  1. 3GPP (2017) Introduction of NB-IoT, TS 36.201

  2. LoRa Alliance [Online]. Available: https://www.lora-alliance.org/lorawan-white-papers

  3. Sigfox website [Online]. Available: http://www.sigfox.com

  4. Chang C-W, Chen J-C (2017) UM paging: unified M2M paging with optimal DRX cycle. IEEE Trans Mobile Comput 16(3):886–900

    Article  Google Scholar 

  5. Chang C-W (2016) Adjustable extended discontinuous reception (eDRX) Cycle For Idle-State Users in LTE-A. IEEE Commun Lett 20(11):2288–2291

    Article  Google Scholar 

  6. Liu B, Yan S, Li J, Li Y (2016) Forecasting PM2.5 concentration using spatio-temporal extreme learning machine. In: Proc IEEE ICMLA, pp 950–953

  7. Tang M, Wu X, Agrawal P, Pongpaichet S, Jain R (2017) Integration of diverse data sources for spatial PM2.5 data interpolation. IEEE Trans Multimedia 19(2):408–417

    Article  Google Scholar 

  8. Chang J-H, Tseng C-Y (2017) Analysis of correlation between secondary PM2.5 and factory pollution sources by using ANN and the correlation coefficient. IEEE Access 5:22812–22822

    Article  Google Scholar 

  9. Kumar A, Kumar A, Singh A (2017) Energy efficient and low cost air quality sensor for smart buildings. In: Proc. IEEE CICT, pp 1–4

  10. NCTU PM2.5 Air Quality Monitoring Web Site of National Chiao Tung University, Note: administrators of this web site and project members can use passwords to log into the system to see historic and more detailed PM2.5 and LoRa loss rate measurement results. [Online]. Available: http://pm25.cs.nctu.edu.tw

  11. Chen L-J, Ho Y-H, Lee H-C, Wu H-C, Liu H-M, Hsieh H-H, Huang Y-T, Lung S-CC (2017) An open framework for participatory PM2.5 monitoring in smart cities. IEEE Access 5:14441–14454

    Article  Google Scholar 

  12. Gemtek IoT products web site. [Online]. Available: http://www.giotnetwork.com

  13. Higgins JR (1996) Sampling theory in fourier and signal analysis: foundations. Oxford Clarendon Press

  14. Wang S-Y, Chen Y-R, Chen T-Y, Chang C-H, Cheng Y-H, Hsu C-C, Lin Y-B (2017) Performance of LoRa-based IoT applications on campus. In: Proc. IEEE VTC-FALL, pp 24–27

Download references

Author information

Authors and Affiliations

  1. National Chiao Tung University, Hsinchu, 300, Taiwan

    Chia-Wei Chang, Yi-Bing Lin & Jyh-Cheng Chen

Authors
  1. Chia-Wei Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yi-Bing Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jyh-Cheng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jyh-Cheng Chen.

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

Chang, CW., Lin, YB. & Chen, JC. Reporting Mechanisms for Internet of Things. Mobile Netw Appl 27, 118–123 (2022). https://doi.org/10.1007/s11036-020-01713-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-020-01713-1

Keywords

Search

Navigation