## 4 Discussion and Future Works
### Limitations and Considerations for Future Research  
While this experimental study provides evidence of the feasibility and effectiveness of the proposed TCP/IP socket programming-based mimic broadcasting system with Chrony NTP synchronization and DTW precise time alignment using low-cost ADXL345 sensors, several limitations and areas for future research should be acknowledged. The detailed summary can be found in Table 2, which outlines these limitations and suggests potential avenues for further investigation. To apply the proposed system in field applications, it will be necessary to validate the system in SHM scenarios within dense sensor networks. This validation process should involve comprehensive testing that considers various environmental effects, long-term stability, network security, and analysis using a wider range of techniques and data parameters. Furthermore, exploring the integration of artificial intelligence and machine learning techniques will enable the system to detect anomalies and make informed decisions, leading to more advanced and automated SHM processes. In terms of the scalability of the proposed system, particularly in larger and densely populated sensor networks, such as those commonly found in urban environments where interference and network congestion may arise, it is crucial to explore alternative network topologies. While the star topology used in this study served as a starting point, investigating other network configurations like tree or complex cluster-tree typologies can optimize network performance and mitigate potential scalability issues. Tree or complex cluster-tree typologies provide a hierarchical structure that can enhance network scalability by enabling data to traverse multiple paths, reducing reliance on a single central node. This approach facilitates the efficient management of network traffic and ensures robustness against interference and congestion in urban environments. The assessment of the system's applicability and reliability across diverse scenarios and environmental conditions is indeed essential, considering factors such as temperature variations, humidity, external vibrations, and potential sensor drift. In the conducted experimental study, only linear detrending was used to shift the time history data to have a zero mean, without incorporating a digital signal filter. However, for real-world applications where sensors are exposed to varying environmental factors, including those mentioned earlier, it is crucial to ensure data accuracy by implementing appropriate sensor calibration and compensation techniques. These techniques may include temperature compensation, humidity correction, vibration isolation, and signal filtering to mitigate the effects of environmental influences on sensor readings. By employing such techniques, the system can maintain its accuracy and reliability across a wide range of environmental conditions, enhancing its suitability for practical deployment in diverse applications. In addition to addressing environmental factors, long-term stability and maintenance requirements are critical considerations for the proposed system. Power management,particularly utilizing solar energy, can play a significant role in ensuring sustained and reliable operation. Furthermore, regular network maintenance protocols are essential to maintain the system's reliability and longevity. This includes monitoring the network performance, addressing any potential issues or failures promptly, and conducting periodic maintenance activities to ensure optimal system functionality. One promising approach to enhance the performance of low-cost sensors in the presence of environmental factors and ensure long-term stability is the utilization of machine learning techniques. By collecting data on environmental conditions alongside sensor measurements, machine learning algorithms can be trained to adaptively calibrate the sensor outputs and predict the system's maintenance needs.