Reducing IoT Data at the Edge: A Comparative Evaluation

In resource-constrained Internet of Things environments, reducing data transmissions is essential for minimizing energy consumption, network load, and operational costs. Overly aggressive reduction may compromise accuracy, a critical factor in applications such as industrial control. This paper aims to offer practical guidance for selecting suitable data reduction techniques by experimentally evaluating three promising methods from common reduction categories: Data Filtering, Data Aggregation, and Data Prediction. We perform a parameter sweep for each algorithm across three real-world temperature scenarios: stable, rising, and fluctuating. Each configuration is evaluated in terms of data reduction percentage and accuracy, using Total Accumulated Deviation, Mean Absolute Deviation, and Maximum Deviation. Results show that Data Prediction generally achieves the highest accuracy across all scenarios, while Data Filtering tends to yield the greatest reduction at the expense of accuracy. However, all algorithms can be tuned to meet specific scenario demands or accuracy criteria, underscoring that no one-size-fits-all solution exists. We conclude that context-aware algorithm selection and parameter tuning are critical for effective Internet of Things data management.