Sensor data is susceptible to faults, noise, and malicious attacks, posing a significant operational and security threat. Therefore, ensuring reliability of sensor data is critical for real-time monitoring systems. Prior research on sensor data reliability relies on edge or upper-layer devices for data fusion from multiple sensors, employing architectures with major overheads and latency due to transmission and storage demands. An alternative approach is to have the sensor estimate and declare its own reliability. While some methods involve sensors computing data confidence and including it in payloads, limitations arise in the absence of neighboring sensor data, and communication overheads are incurred. To address this problem, this paper proposes an innovative approach to enhance the reliability of sensor data using an intelligent self-declaration process. Proposed reliability estimation is evaluate with three lightweight estimation algorithms, namely, Kalman Filter, Holt-Winters Method, and Mahalanobis Distance using sensor's historical data. The reliability level is then added to the three reserved bits of a TCP packet header which results in zero additional overhead. Experiments conducted using real-world sensor data (from water quality monitoring systems) obtained from our IoT lab demonstrate the effectiveness of our proposal and the potential for application in real-world sensor-based applications.