## 5 Conclusions and Perspectives  
This study delved into a critical aspect of wireless sensor network-based water pipeline monitoring (WWPM) systems--the imperative need for low energy consumption. The sustained operability of sensor nodes over extended periods without the requirement for frequent battery replacement is vital in such applications, especially when considering the typically battery-powered and physically inaccessible nature of these nodes. This study was centered on a comprehensive experimental investigation into a hybrid technique that incorporates distributed computing, hierarchical sensing, and duty cycling to optimize the energy consumption of sensor nodes, thereby extending the lifespan of WWPM systems. The energy consumption of three DKFs selected from data fusion strategies--specifically, diffusion, gossip, and consensus--were evaluated in the context of a distributed computing solution for WWPM. In all three DKF implementations, the sensor node's lifetime when powered with a 3.7 V 2000 mAh Li-Po rechargeable battery was between 52 to 54 h. The implementation of hierarchical sensing and duty cycling on the worst-case DKF shows an improvement in energy reduction by 800%. The proposed solution also achieved real-time  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Technique** & **Energy Consumption (mAh)** \\ \hline Distributed Computing only & 37.89 \\ Distributed Computing + Hierarchical Sensing & 37.39 \\ Distributed Computing + Duty Cycling & 0.75 \\ Distributed Computing + Hierarchical Sensing + Duty Cycling & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Energy reduction contribution of different energy conservation techniques on the distributed solution.  
-leak detection and preserved the lifespan of the WWPM system, all without degrading the leak detection reliability.  
In conclusion, combining the results of leak detection performance in [46] and power consumption presented in Section 4.1, a compromise between leak detection performance and energy efficiency shows that EDKF is more optimal for a fully distributed leak detection solution in WWPM when compared to ICF and SGG-ICF, since its estimation performance is less affected by packet loss. Rather than implementing an ICF or SGG-ICF, which have higher sensitivities than an EDKF but have a high communication requirement (which has an adverse impact on the power consumption), we propose that the sensitivity of a WWPM solution implementing an EDKF can be improved by using machine learning (ML) techniques at the decision level [50]. Presently, it is feasible to implement ML on embedded systems with limited computational power, such as microcontrollers [51], as demonstrated in previous works that have employed deep neural networks [52]. Furthermore, ML is currently regarded as a strong candidate for enhancing security in IoT [53] and has already seen partial implementation in water monitoring applications [54]. Therefore, we advocate for the use of EDKF for filtering at the feature extraction level to provide more accurate features. These features can then be classified by an ML algorithm at the decision level to improve leak detection accuracy. Since this approach involves increased computation rather than communication, it is likely to reduce energy consumption at the sensor nodes. Consequently, it emerges as a promising choice for implementation in battery-powered sensor nodes utilized in WWPM, thanks to its energy-aware design. This makes it a strong candidate for a green WSN solution for water pipeline monitoring.  
Conceptualization, V.N.; methodology, V.N.; software, V.N.; validation, V.N. and F.M.; writing--original draft preparation, V.N.; writing--review and editing, V.N., F.M. and P.T.; supervision, F.M. and P.T. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data are contained within the article.  
The authors declare no conflict of interest.