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Anomaly-based data reduction for energy-efficient edge computing in IoT with LoRa.

Fadime Karadas¹, Bilal Usanmaz²

¹Faculty of Engineering/Computer Engineering, Ataturk University, Erzurum, 25240, Türkiye. fadimekaradas@atauni.edu.tr.

Scientific Reports

|May 7, 2026

Summary

Edge computing in Internet of Things (IoT) systems can be optimized by transmitting only anomalous data. This anomaly detection method significantly reduces data volume and energy consumption for resource-constrained edge devices.

Keywords:

Anomaly Detection DBSCAN Edge Computing Energy Efficiency IoT Isolation Forest LoRa

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Area of Science:

Computer Science
Electrical Engineering
Data Science

Background:

Edge computing is crucial for Internet of Things (IoT) systems, enabling localized data processing.
Resource-constrained IoT environments face challenges with high communication and energy costs from transmitting all sensor data.
Existing methods often fail to address inefficiencies in selective data transmission for energy saving.

Purpose of the Study:

To propose and evaluate an anomaly detection-based data reduction method for edge computing.
To reduce communication load and energy consumption in IoT edge devices by transmitting only anomalous data.
To leverage LoRa technology for efficient edge data transmission.

Main Methods:

Implemented an anomaly detection approach at the edge using unsupervised learning algorithms.
Utilized DBSCAN and Isolation Forest algorithms to identify anomalous sensor data instances.
Compared the proposed methods against a full-data transmission (FDT) baseline through experiments.

Main Results:

DBSCAN achieved a 98.19% reduction in data volume and a 98.10% reduction in energy consumption.
Isolation Forest resulted in a 97.32% reduction in both data volume and energy consumption.
Both methods significantly outperformed the FDT baseline.

Conclusions:

Anomaly-driven selective data transmission is highly effective in reducing communication load and enhancing energy efficiency in edge computing.
The proposed methods offer a viable solution for optimizing low-power, resource-constrained IoT environments.
Unsupervised learning algorithms are suitable for identifying and transmitting critical data at the edge.