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

Edge computing, a key component of Internet of Things (IoT) systems, enables data processing close to the data source. In low-power, resource-constrained IoT environments, it can reduce dependency on centralized cloud systems, lower communication load, and minimize overall energy consumption. However, transmitting all sensor data from edge devices still incurs significant communication and energy costs. The key research gap is that existing approaches rarely address this inefficiency through selective transmission; specifically, few studies have explored how filtering and sending only anomalous data can improve energy efficiency. To bridge this gap, we propose an anomaly detection-based data reduction method operating at the edge using LoRa technology. We apply two unsupervised learning algorithms, DBSCAN and Isolation Forest, to identify and transmit only anomalous sensor instances. The proposed methods are evaluated against a full-data transmission (FDT) baseline. The key contributions are demonstrated through experimental results: DBSCAN reduces data volume by 98.19% and energy consumption by 98.10%, while Isolation Forest achieves reductions of 97.32% and 97.32%, respectively. These findings confirm that anomaly-driven selective transmission significantly reduces the communication load while ensuring high energy efficiency in edge computing systems.