Edge Computing Architecture for Real-Time Water Quality Monitoring in Aquaculture Ponds

Rina Kusumawati, Hendra Gunawan, Dewi Lestari

Jurnal Teknologi dan Inovasi, Vol. 1 No. 2 (2026), pp. 20-29

Abstract

Shrimp and fish farmers need early warning when dissolved oxygen or pH drifts outside safe ranges, but rural ponds often have unreliable internet connectivity. We propose an edge computing architecture in which low-cost sensor nodes report to a pond-side gateway that detects anomalies locally and synchronises summaries to the cloud when a connection is available. The prototype was deployed in eight ponds for 90 days. Local processing reduced alert latency from a median of 41 seconds with a cloud-only design to 3 seconds, and cut cellular data use by 86%. Farmers received 97% of critical dissolved-oxygen alerts within one minute, including during network outages. The architecture shows that edge processing makes continuous water quality monitoring practical in areas with limited connectivity.

Keywords: edge computing; Internet of Things; water quality; aquaculture; sensor network

1. Introduction

This section introduces the problem of internet of things addressed in this study, reviews the main approaches reported in prior work, and states the research objectives.

2. Methods

We describe the data, the proposed approach, the baselines, and the evaluation protocol used to assess the results summarised in the abstract.

3. Results and Discussion

The results are presented and compared with the baselines. Limitations and practical implications for deployment are discussed.

4. Conclusion

The study shows that the proposed approach is effective for the stated objectives and outlines directions for future work.

References

  1. Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646. https://doi.org/10.1109/JIOT.2016.2579198
  2. Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787–2805. https://doi.org/10.1016/j.comnet.2010.05.010
  3. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.