## 4 Applications of WSN in Urban Areas
### Water Pipeline Monitoring
(2019) proposed a leakage detection method based on Machine Learning (ML) and WSNs, aiming to enhance the accuracy and efficiency of leak detection in water pipelines. This method utilized pressure sensors and water leak sensors in decentralized networks with a star-connected network topology. This study aimed to minimize energy consumption while improving the system's lifecycle efficiency by employing a leakage-triggered networking technique and leveraging ML algorithms. A leakage identification method was introduced, utilizing intrinsic mode functions and entropy to construct a signal aspect set for leakage detection. The results demonstrated the effectiveness of the proposed method in accurately detecting leaks in water pipelines, offering valuable insights for enhancing pipeline monitoring systems and preventing water loss due to leaks. [PERSON] et al. (2022) [PERSON] et al. (2022) introduced a heterogeneous two-tiered routing method to improve the network lifetime and efficiency of WSNs in water pipeline monitoring applications. This method uses pressure sensors in centralized networks with a Peer-to-Peer (P2P) connected network topology. By using two routing algorithms in a tiered model, the study aimed to optimize power consumption while enhancing the system's performance capability. Through simulations and real-world prototype validation, the study demonstrated the effectiveness of the proposed routing method in improving the reliability and efficiency of water pipeline monitoring systems, offering promising solutions for sustainable water resource management. [PERSON] et al. (2018) [PERSON] et al. (2018) developed a WSN node prototype termed WiRoTip for water pipeline applications and addressed the need for robust and efficient sensor nodes for monitoring pipelines. WiRoTip utilized acoustic sensors in decentralized networks with a P2P connected network topology. The prototype was designed with different node components to ensure adequate design and functionality. By using acoustic sensors and decentralized network architectures, WiRoTip offered a reliable and efficient solution for monitoring water pipelines, contributing to improved leak detection and pipeline maintenance efforts. [PERSON] et al. (2017) [PERSON] et al. (2018) introduced a wireless sensor network testbed named EarnArdui for water pipeline monitoring. It aims to provide insights into the physical behavior of water distribution systems. The testbed utilized pressure and water level sensors in centralized networks with a star-connected network topology. This study facilitated data collection from sensors and transmission to the base station for analysis. By offering a comprehensive understanding of water distribution system behavior, the testbed provided valuable insights for optimizing pipeline monitoring and management strategies, ultimately contributing to more efficient and sustainable water resource management practices. Table 10 compares water pipeline monitoring methods by objectives, sensors, network configurations, results, and limitations. It includes pressure, pH, water level, and acoustic sensors in star, distributed star, P2P, and heterogeneous topologies. The objectives focus on leakage detection, network efficiency, and physical monitoring. The results show improvements in NL, E, Delay, R, and EC, but the limitations include high energy consumption, delays, and inefficiencies in monitoring and lifetime optimization. Each method addresses specific challenges but has distinct implementation constraints. The \(\swarrow\) in Table 10 shows that the method increases the performance of the particular metric, whereas the blank space indicates that the method does not consider the performance of the particular metric. The summary conclusions of water pipeline monitoring are listed below.