The rapid development of technology has empowered us to achieve resilient infrastructure to establish a sustainable ecosystem. The construction site is one of the highest risk jobs for accident-related fatalities and injuries globally. From the previous studies, it is concluded that untrained or inexperienced workers were responsible for 40% of work-related accidents and the Health and Safety Executive (HSE) report concludes that inadequate working experience, knowledge, and safety awareness were the key causes of fatal accidents in the construction industry. Moreover, it is identified from previous studies that digital technology such as IoT with the assistance of wireless sensors can enhance the safety of construction sites. Based on this advantage, this study has implemented the hybrid architecture with the integration of the 2.4 GHz Zigbee, 433 MHz long-range (LoRa), and Wi-Fi communication protocol to monitor the health status of workers and construction sites and also to identify workers’ equipment wearing status in real-time scenarios. The proposed architecture is realized by implementing customized hardware, based on 2.4 GHz Zigbee, 433 MHz long-range (LoRa), and Wi-Fi. Furthermore, in the analysis of the evaluation metrics of LoRa, it is concluded that the lowest sensitivity is observed for SF 12 at BW 41.7 kHz and the highest is observed for SF 7 at BW 500 kHz; the maximum value data rate is observed at BW 500 kHz at CR 1 for SF 7, and the minimum data rate is observed at BW 41.7 at CR 4 for SF 12. In the future, the customized hardware will be implemented in different construction environments resolving possible challenges that empower to implementation of the proposed architecture in wide extensions.

This research addresses the acoustic feedback and stability performance of Active Noise Control (ANC) systems. The Filtered Cross Least Mean Square (FxLMS) algorithm is one of ANC’s most successful adaptive algorithms for reducing undesired noise. The ANC filters have their convergence speeds reduced by significant acoustic feedback, making them unstable in the worst-case scenario. It is possible to eliminate acoustic feedback using a fixed feedback neutralization filter that can be acquired offline. However, the feedback path could change over time, so we might have to make modifications often while the ANC system is online. This paper proposes two enhancements to the FxLMS algorithm to address the ANC’s performance difficulties. To address acoustic feedback problems, the first alteration improves the ANC duct’s feedback path modeling by incorporating the online Feedback Naturalization (FN) concept with an Active function normalized LMS algorithm (AF-NLMS). The second modification, the vector-decimation-based method, is proposed in this paper as a way to improve stability, convergence speed, and robustness. It is achieved by decimating the filter weight vector into small vectors and then updating each vector using the harmonic mean error of the VSS FxLMS. To ensure the enhanced effectiveness of the proposed solution, the AF-FxLMS, online-AFFNFxLMS, and HM-AFFNFxLMS algorithms are used to update the filter weights as a demonstration. The proposed method has superior noise reduction effectiveness compared to other algorithms, which indiscriminately attenuate the control filter across all frequencies. The validation of the proposed approach was achieved via simulations and experimental tests in a PVC duct ANC system, showing that robust stability can be achieved while keeping relatively good performance.