## 4 Applications of WSN in Urban Areas
### Healthcare Applications  
The application of WSN in healthcare addresses challenges in urban areas, such as congested hospital crisis rooms and traffic delays. Tele-healthcare, successfully used in countries like China and India, leverages WSN technology to remotely monitor patients' vital signs and transmit data to doctors, improving efficiency and reducing the need for physical visits. Key considerations include ensuring data security, reliable network connectivity, and accurate sensor measurements. Integrating WSN with telemedicine platforms and training healthcare professionals can further enhance healthcare access and reduce hospital congestion. Figure 4 illustrates the healthcare applications in WSN. \begin{table}
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[PERSON] et al. (2022) [35] employed a cross-sectional descriptive design in electronic healthcare applications to enhance healthcare quality and patient security. The study utilized video, acoustic, and RFID sensors within a decentralized network type and a star-connected network topology. The designed application facilitated safe and confidential access to health data, allowing individuals to monitor their health outcomes. By leveraging these sensors and network architecture, the study aimed to address challenges related to healthcare accessibility and patient data security. The results demonstrated the effectiveness of the electronic healthcare application in improving healthcare delivery and patient satisfaction, ultimately contributing to enhanced healthcare quality and patient security. [PERSON] et al. (2018) [36] conducted an initial bibliometric analysis focusing on an e-health electronic application and program in Riyadh. The study employed a cross-sectional descriptive design targeting healthcare professionals. Using Electromyogram (EMG), Electroencephalogram (EEG), Electrocardiogram (ECG), and Electrooculogram (EOG) sensors within a decentralized network type and a star-connected network topology, the study aimed to analyze data for healthcare applications efficiently. Deep learning techniques were utilized for classification purposes. The study addressed issues related to data analysis and classification in healthcare settings, aiming to improve diagnostic accuracy and treatment efficacy. The results highlighted the potential of deep learning and sensor technologies in enhancing healthcare outcomes and contributing to advancements in medical research and practice. [PERSON] et al. (2023) [37] explored the impact of sustainable healthcare systems on user satisfaction and intention, focusing on the use of environmental and physiological sensors within a centralized network and star-connected topology to implement energy-saving techniques. The technical approach involved integrating these sensors to monitor health data efficiently while conserving energy. The problem addressed was the challenge of enhancing user experience and satisfaction in mobile health (mHealth) platforms. The solution proposed involved analyzing the moderating effects of personal features on perceived value and user satisfaction, identifying key factors that influence user contentment. Advantages of this approach include improved healthcare outcomes, enhanced user satisfaction, and efficient energy usage in mHealth applications. However, limitations may include  
Figure 4: Healthcare applications in WSN. the complexity of accurately capturing and analyzing personal features, potential privacy concerns, and the need for robust infrastructure to support the centralized network system. [PERSON] et al. (2023) [38] introduced an energy-saving technique in a Very-Large-Scale Integration (VLSI) design to increase the battery capability of IoT devices. The study used battery sensors within a centralized network type and a cluster-based network topology for energy-saving methods.