## 5 Conclusions and Future Directions for Deploying IoT  
Integrating IoT devices and m-Health applications for maternal health monitoring and prenatal care offers a possibility for early risk detection and medical intervention, particularly in resource-constrained communities, including LMICs that may have limited access to medical care. IoT-enabled wearable and portable biosensors, such as smart blood pressure monitors, pulse oximeters, and non-invasive glucose monitors, can continuously collect real-time physiological data from pregnant women. The data collected via these devices using built-in sensors are transmitted using communication protocols such as Wi-Fi, Bluetooth, cellular (4G/5G), or LoRaWAN. Given the sensitive nature of data and complying with HIPAA, the data are secured using encryption protocols SSL/TLS to the respective cloud storage. The patient's medical care provider can access the data from the cloud through the secure EHR as necessary for proactive monitoring. Specifically, combining machine learning models like those developed in this study with data collected from IoT devices allows for real-time monitoring and alerting medical providers and patients with any early warning, such as preeclampsia, gestational diabetes, or hypertensive disorders. By leveraging IoT technology, maternal health risk prediction can shift from periodic clinical assessments to a continuous, real-time system that identifies adverse trends before they escalate into life-threatening conditions [38, 39]. Additionally, integrating the framework discussed above can empower community health workers (CHWs) and traditional birth attendants (TBAs) with actionable insights, enabling them to make data-driven decisions and intervene early in high-risk cases to provide appropriate medical care. Furthermore, given the critical role CHWs and TBAs play in LMICs and resource-constrained settings, equipping them with IoT-enabled devices linked to AI risk prediction systems can significantly improve maternal health outcomes and reduce the burden on overcrowded hospitals. To implement the machine learning models developed in this research in LMICs and other research-constrained settings, it is critical to have a robust IoT architecture while considering the challenges faced in these settings, such as limited internet connectivity, power supply constraints and affordability. Additionally, a scalable and cost-effective IoT framework should integrate multiple components, including real-time data collection, edge computing, cloud analytics, and decision support systems. Wearable or portable IoT devices should capture maternal health indicators such as blood pressure, heart rate, oxygen saturation, and temperature while offline to accommodate areas with poor network coverage [40; 41]. As discussed above, while it is preferable to have machine learning models deployed on the cloud for anomaly detection and assessing maternal health risk based on data collected from IoT devices, edge computing devices, such as mobile phones or low-power processing hubs, can enable preliminary data analysis at the point of collection, reducing reliance on cloud computing and connectivity issues while ensuring real-time anomaly detection [42]. While local edge computing devices enable real-time anomaly detection from biosensors, maintaining privacy and offline functionality, these devices have computational, storage, and battery limitations, making scaling challenging for anomaly detection and long-term monitoring. Future researchers should prefer cloud-based computing for early maternal risk detection and continuous patient monitoring. For data transmission to cloud servers, we recommend a hybrid communication system using Bluetooth, GSM, and LoRaWAN to enhance connectivity in remote areas and LMICs where traditional internet access could be unreliable [43; 44].