## 4 Applications of WSN in Urban Areas
### Solid Waste Monitoring
The designed mechanism incorporated weight sensors to facilitate efficient garbage collection. The study identified challenges such as inaccessibility to required data and delayed unloading in existing garbage collection and management systems. To address these issues, the proposed method utilized real-time data from smart garbage bins deployed across the city and tackled waste overflow problems. This methodology was implemented using NetLogo in multi-agent modeling environments, aiming to improve the efficiency and effectiveness of solid waste management in urban settings. Thus, this work aimed to enhance waste collection processes and optimize resource utilization in urban areas, ultimately contributing to cleaner and more sustainable cities. [PERSON] et al. (2022) [50] introduced PortiK, a system designed to help and optimize waste management operations. PortiK utilizes short-range monitoring combined with deep learning and statistical analysis to provide continuous, real-time, and non-intrusive measurements of waste streams. By employing image analysis and object detection techniques, this technique offers an end-to-end solution for waste management, covering every essential step from hardware requirements to data gathering and analysis. This methodology addresses waste management operations challenges, such as inefficient monitoring methods and a lack of real-time data insights. Through its innovative approach, PortiK offers a promising solution to optimize waste management processes and improve overall efficiency in handling waste streams. [PERSON] et al. (2022) [51] introduced a solid waste management architecture using deep learning and IoT technologies. The architecture used Global System for Mobile Communications (GSM) tracking sensors and Bluetooth options for data tracking, while waste sorting was performed using Convolutional Neural Networks (CNNs). The system incorporated smart trash bins equipped with multiple sensors for efficient waste management. By employing IoT and Bluetooth connections for data monitoring, the system aimed to improve waste classification accuracy and overall efficiency. The evaluation metrics used, including waste label classification accuracy, sensor data evaluation, and System Usability Scale (SUS), contributed to enhancing the system's performance and usability. [PERSON] et al. (2020) [52] introduced an ICT-based Solid Waste Collection and Classification System (SWCCS) to address waste gathering issues. This methodology used proximity sensors to improve waste collection efficiency. A multi-objective hybrid algorithm, combining whale optimization and genetic algorithm (MOGWOA), was designed to optimize waste collection routes. By employing fast, non-dominated sorting methods, the algorithm aimed to minimize costs and emissions associated with waste collection. The SWC route optimization model introduced by [PERSON] et al. (2020) [53] further enhanced collection efficiency by employing Fixed Route Optimization (FRO) and Variable Route Optimization (VRO). The model utilized Global System for Mobile Communication (GSM) tracking sensors in conjunction with FRO and VRO to optimize waste collection routes using mixed-integer linear programming methods. Through efficient optimization techniques, the SWC route optimization model aimed to improve waste collection efficiency and reduce associated costs and emissions, ultimately contributing to more sustainable waste management practices. Table 9 compares various methodologies for solid waste management, detailing their objectives, sensor types, network configurations, results, and limitations. These methods utilize customized sensor nodes, matter concentration, oxides of nitrogen (NOx), sulfur dioxide sensors, weight sensors, short-range data monitoring, GSM tracking sensors, and proximity sensors within decentralized or centralized networks featuring tree, star, mesh, and cluster-based topologies.