A lightweight hybrid framework for real-time data refinement in resource-constrained underwater and underground wireless sensor networks.

With the help of harsh propagation environments, reliable data acquisition in UWSNs/UGWSNs may suffer from packet loss a few times, extra communication latency, energy scarcity, and retransmission overhead. These elements lead to two common problems for the data quality: missing values attributed to packets lost or nodes going down and duplicate readings introduced by retransmissions/synchronization problems. Current data filtering techniques either solve these issues separately or adopt computationally complex models, which are not appropriate for resource-constrained UWSN/UGWSN scenarios. To address this, we have introduced a computationally simple real-time hybrid data refinement framework that leverages a Kalman filter (KF) for imputation of missing entities and relies on a Sliding Window that is based on Manhattan Distance (SWMD) approach for detection of repetitive entries. The proposed scheme along with existing approaches have been implemented and experimentally validated in the OMNeT++ simulation environment for UWSN-realistic conditions-where, in addition to node energy spanning percentage losses and random delays, 20-40% of packets are lost-the framework jointly considers the two types of anomalies without needing any training data or cloud offloading. Evaluated on 1200 diverse sensor nodes over 20,000 recordings, the proposed method reaches a Mean Absolute Deviation (MAE) of 1.20 and Root Mean Square Error (RMSE) of 1.75 for missing value estimation; it has filtered out 20.5% redundant packets, and enhances the Packet Delivery Ratio (PDR) to up to 88% than existing methodologies. More importantly, the network lifetime achieved was up to 122s (over two-times longer than state-of-the-art baselines methodologies), while the average end-to-end delay is maintained within 11.1 ms. By supporting high-quality, both accurate and complete, as well as energy-efficient data streams, this framework enables robust real-time analytics for long-term monitoring in environments preventing break-in accessibility, such as deep-sea observatories, subterranean infrastructure, and mining systems.