## 4 Applications of WSN in Urban Areas
### Commercial Asset Tracking  
In urban areas, commercial businesses rely heavily on efficient asset tracking during storage and transportation to ensure the timely delivery of products to customers. However, traditional tracking methods, such as manually recording identification numbers or barcodes, are time-consuming, prone to errors, and inefficient. These lead to delays, misplaced shipments, and customer dissatisfaction. WSNs offer a solution by providing real-time monitoring and tracking of assets throughout the supply chain. By deploying  
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sensor nodes equipped with RFID technology, businesses can automatically track the movement and location of products as they pass through predefined entry points. However, challenges such as signal interference and fluctuating item coordinates may arise in urban environments with dense infrastructure. Precautions such as proper placement of sensor nodes, signal amplification, and regular maintenance can help mitigate these issues. Additionally, implementing backup tracking systems and data encryption measures can enhance the reliability and security of asset tracking in WSNs, ensuring seamless operations and customer satisfaction in commercial businesses. [PERSON] et al. (2016) [59] proposed a new asset tracking architecture for tracking assets in construction sites and addressing the challenges of efficiently monitoring equipment and materials in dynamic environments. The designed architecture utilized a hybrid RFID thermal sensor in distributed networks, enhancing asset tracking accuracy and reliability. The architecture facilitated seamless asset tracking and searching processes by incorporating radio frequency recognition, Bluetooth low-energy tags, and smartphones. Using an Android application further streamlined asset management tasks, improving overall operational efficiency at construction sites. This architecture also optimized battery life and distance estimation accuracy, enhancing asset identification in challenging construction environments. [PERSON]. [PERSON] et al. (2008) [60] introduced an optimization decomposition framework for industrial asset tracking applications in WSN, aiming to optimize energy consumption while maintaining estimation quality. The framework utilized pressure sensors in distributed networks to monitor industrial assets and track their movements. By using optimization decomposition techniques, the framework effectively managed sensor node energy consumption, ensuring prolonged network operation without compromising estimation accuracy. The results demonstrated the feasibility and effectiveness of the framework in enhancing industrial asset tracking capabilities while maximizing energy efficiency in WSN deployments. [PERSON] et al. (2013) [61] developed an optimization framework for tracking medical assets in hospital environments using wireless sensors, addressing the challenges of asset management and patient safety. This methodology used statistical simulation models to analyze asset movements and identify potentially hazardous positions within the hospital. By optimizing sensor placement based on coverage features, the framework ensured comprehensive asset tracking while minimizing deployment costs and energy consumption. The optimization algorithm determined the optimal placement of sensors to maximize coverage and enhance asset monitoring efficiency. Thus, the framework provided valuable insights into optimizing asset tracking systems in hospital environments, ultimately contributing to improved patient care and operational efficiency. Table 11 compares asset tracking methods by objectives, sensors, network types, results, and limitations. It includes hybrid RFID, thermal, pressure, and wireless sensors in mesh and star topologies.