In water quality monitoring networks, efficient sensor operation and network lifetime are crucial for timely and accurate data collection. Existing models often focus on minimizing overall network energy consumption and typically treat all sensors equally, making no distinction based on the information value of the data collected. This may lead to the unnecessary transmission of redundant data from low-value sensors. This research proposes a novel information-centric algorithm specifically designed for Wireless Sensor Networks (WSNs) deployed in such applications. The algorithm leverages information theory and Minimum Redundancy, Maximum Information (MIRI) principles to identify and prioritize data from sensors with high information value. The new algorithm integrates an energy-efficient consumption model with a strategic Cluster Heads selection approach. This approach prioritizes keeping sensors with high information content alive within clusters, further minimizing total energy consumption. By strategically selecting sensors and reducing redundant data collection, the proposed algorithm achieves a higher average residual energy compared to the existing model. This indicates more efficient energy utilization and potentially longer individual sensor lifetimes. These results demonstrate the effectiveness of the information value-centric approach in optimizing WSN performance. The algorithm extends network lifetime and paves the way for further exploration of information-centric techniques for enhancing WSN efficiency and longevity.