## 4 Discussion and Future Works
### Limitations and Considerations for Future Research
This adaptive calibration, guided by machine learning, can significantly improve the accuracy and reliability of the sensor measurements over time. This adaptability allows the system to adjust its calibration parameters dynamically, compensating for variations caused by environmental factors and ensuring accurate measurements. Furthermore, machine learning algorithms can be trained to predict the maintenance needs of the system  
Comparative analysis with other time synchronization methods is crucial to evaluate the proposed system's performance. In this study, the sampling frequency was set at 100 Hz, making the millisecond-level accuracy provided by Chrony NTP sufficient. However, if the proposed system is applied in applications requiring higher sampling rates, conducting a comparative analysis with methods such as PPS-GPS or PTP would provide valuable insights into synchronization accuracy, latency, and resilience to network variations. It \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Aspect** & **Experimental Study Proposed by Authors** & **Future Research Direction** \\ \hline Validate the system in real-field scenarios & Demonstrated feasibility in controlled environment. & Conduct field tests to validate real-world performance. \\ \hline Explore scalability in larger networks & Focused on local wireless sensor network with star typology. & Investigate scalability to larger networks with different typologies. \\  & & Opting tree and complex cluster-tree typologies are preferable. \\ \hline Address environmental factors & The environmental impact on sensor measurements was not extensively discussed since the study was conducted in a controlled environment. Only linear detrend was applied in this study. & Collect experimental data and analyze the system’s performance under different environmental conditions. \\  & & Explore the integration of ML algorithms to enhance sensor accuracy for adaptive calibration of measurements. \\ \hline Ensure long-term stability and maintenance & The study proposed the initial evaluation on a lab scale. & Investigate the system’s performance over an extended period and develop maintenance strategies. \\  & & Integrate ML algorithms to predict maintenance needs based on environmental data. \\ \hline Synchronization methods & The study focused on Chrony NTP synchronization due to its ease of use and suitability for low sampling rates. & Conduct comparative studies with alternative synchronization methods. \\  & & PTP and PPS-GPS integrated with Chrony can be enhanced the performance, accuracy, and efficiency in higher sampling rate applications. \\ \hline Data security & The experimental study did not discuss data security measures. & Investigate and implement appropriate data security mechanisms, such as AES encryption, authentication, and SSL/TLS protocols, to protect sensitive sensor data from unauthorized access or tampering in real-world applications. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Limitations and Future Research Directions. is important to consider the hardware requirements of these methods, as PTP relies on specialized IEEE 1588-compliant network switches and Network Interface Cards (NICs), while GPS may have limitations in indoor environments. Furthermore, the implementation complexity of PTP is generally higher compared to Chrony. PTP requires careful configuration and calibration of network switches and NICs to ensure accurate time synchronization. The selection of the most suitable synchronization method should be based on the specific requirements of the application. In terms of data security and network protection against unauthorized access, Socket programming in Python emerges as a valuable platform for real-world applications. It offers a wide array of libraries and frameworks equipped with robust security features such as AES encryption, authentication, and support for Secure Sockets Layer and Transport Layer Security (SSL/TLS).