# Leveraging Urban Water Distribution Systems with Smart Sensors for Sustainable Cities
## 2 Related Work  
Several studies explore optimizing water networks using simulations and artificial intelligence (AI) algorithms. Ref. [10] applies EPANET for designing WDS in Jharkhand, focusing on both urban and rural management. Ref. [11] develops a management system for water resources with wireless sensors and a mobile app. Ref. [12] creates a framework with EPANET version 2.0. and MATLAB R2024b for optimal disinfectant dosage in intermittent water supplies. Ref. [13] presents a methodology for the efficient modeling of water distribution networks (WDNs) using EPANET in a preliminary version, aiming to reduce computational load. Ref. [14] uses EPANET 2.0 to simulate water quality in pipelines and assess interventions. Ref. [15] employs EPANET in a digital twin model for comprehensive WDN analysis. Ref. [16] optimizes roughness coefficients with a Genetic Algorithm (GA) but does not focus on water node optimization. Ref. [17] optimizes water quality parameters in WDSs using EPANET. Ref. [18] creates an EPANET-based model for Kuwaiti water networks, evaluating water quality and age. Ref. [19] introduces the Fuzzy C-means Adaptive Differential Evolution (FCADE) algorithm for WDN challenges integrated with EPANET. Ref. [20] presents a mobile app using the Arduino MCU platform for leak detection and pressure measurement. In contrast, our study simulates and optimizes nodes in urban water networks to reduce wastage. Ref. [21] explores the use of LEO satellites for remote water level monitoring and optimizing energy consumption. Ref. [22] integrates the IoT and Blockchain for agricultural water management. Ref. [23] reviews AI applications in water conservation, covering system components and their interdependencies. Ref. [24] designs a solution for renewing critical water infrastructure using Industry 4.0 technologies for Smart Cities. Ref. [25] discuss the development of a web-based EPANET simulation and visualization for large water networks to support water management. In [26], a water monitoring system based on pH measurements has been introduced to modernize urban water distribution systems. However, unlike as proposed here, this system does not demonstrate application in real urban networks. The approach in [26] employs traditional predictive methods and uses a NODE MCU sensor architecture. Recently, [27] presented a series of theoretical approaches for transforming urban water distribution systems. This proposal evaluated technologies and predictive models statistically, focusing on theoretical optimization rather than practical application. Table 1 compares these works with our study, focusing on aspects such as water analysis (study of real water networks), AI (mention or implementation of cognitive algorithms), optimization [Opt.] (improving the performance of current infrastructures), technology [Tech.] (use of communication mechanism, sustainability [Sust.] (reducing carbon footprint and water wasting), and architecture [Arch.] (proposing real designs).  
As can be seen in Table 1, our work covers most of the aspects found in the literature. We aim not only to leverage an open-source simulator to highlight the critical situation of current reservoirs, but also to optimize the nodes and propose a novel architecture to be deployed on urban water networks. Therefore, we utilize EPANET to model an urban water network and Python 3.9 to develop optimization algorithms.