JoWUA

Abstract

Environmental monitoring systems for remote areas and areas with limited resources are constantly generating large volumes of time-series sensor data, resulting in excessive energy consumption, storage overhead and scalability issues. Traditional static and event-driven sampling techniques are likely to yield redundant information, reducing operational sustainability and efficiency. To address the above-mentioned limitations, the present study proposes a novel framework, Adaptive Time-Based Data Reduction with Blockchain (ATDR-BC), which combines dynamic timestamp-based data sampling, Isolation Forest-based anomaly detection, and Merkle Tree-enabled decentralised storage. The proposed framework is an intelligent way to adjust sampling intervals based on environmental stability, without compromising critical anomaly detection or tamper-resistant data integrity in a hybrid on-chain/off-chain blockchain model. Experimental evaluation based on actual environmental sensor data shows that ATDR-BC can achieve an average data reduction rate of 45-60%, and the maximum data reduction rate is 70% under stable conditions. The approach leads to 35-50% reduction in energy consumption compared to conventional sampling methods with high reconstruction accuracy (96.59%) and RMSE values that are smaller than 5%. These results confirm that ATDR-BC provides a good balance between energy efficiency, data reliability and storage optimisation and can be used for applications involving long-term deployment in remote and resource-limited applications such as environmental monitoring.