## 5 Conclusions and Future Directions for Deploying IoT
Once transmitted, the data can be securely stored and analyzed within a cloud-based system using machine learning algorithms developed to generate predictive insights and automated alerts for healthcare providers and community health workers. To adhere to HIPAA compliance and patient privacy, we recommend using an Advanced Encryption Standard with a 256-bit key (AES-256) encryption before transmission, with secure communication protocols such as TLS 1.2/1.3 protecting data in transit. As an enhanced security feature, anonymizing patient identifiers and other Protected Health Information (PHI) using de-identification and tokenization can minimize privacy risks. We recommend deploying machine learning models in containerized environments (e.g., Docker, Kubernetes) on HIPAA-compliant cloud platforms such as AWS HealthLake, Google Cloud Healthcare API, or Microsoft Azure for Healthcare. For model inference, secure enclaves (e.g., AWS Nitro, Intel SGX) can help prevent unauthorized access by leveraging role-based access controls (RBAC) and multi-factor authentication (MFA), restricting access to authorized healthcare providers. Finally, depending on the laws and compliance requirements, we also recommend adding a layer of security where PHI data can be encrypted at rest using FIPS 140-2 compliant encryption, with regular data integrity checks and audit logs monitoring access and modifications. Upon detecting high-risk cases, the system will trigger an alert system to medical providers. Furthermore, an SMS-based alert system can notify local clinics or CHWs about high-risk patients, facilitating timely medical care [45; 46]. These security measures allow real-time maternal health monitoring while maintaining HIPAA compliance, data confidentiality, and integrity, facilitating timely and secure healthcare interventions in underserved regions. While AI-driven adaptive models capable of functioning with limited or intermittent data can enhance the quality and speed of care, making it a viable solution for improving maternal health outcomes globally [47; 48], there are several barriers to implementation, such as affordability, digital literacy, and data security. When considering resource-constrained settings such as rural communities and LMICs, the affordability of devices is a significant factor, and the cost of IoT devices must be minimized through strategies such as public-private partnerships and subsidized programs to ensure accessibility in low-resource settings [47; 49]. Training community health workers and traditional birth attendants to use new health tools is essential for their successful implementation, as is building trust with patients about how their personal information is kept private and secure. Future research should focus on leveraging larger and more diverse data from LMICs, particularly in sub-Saharan Africa, where maternal health challenges are most severe and often overlooked. Furthermore, researchers should make a conscious effort to consider additional factors, such as social determinants, medical history, and other health issues, which could provide deeper insights into the risks faced by mothers, especially those at moderate risk. It is also important to study how factors like access to healthcare and living conditions influence maternal health in different regions, including resource-limited areas in developed countries. Testing these approaches in various healthcare settings, such as large hospitals and private clinics, can help determine their effectiveness in reducing complications and deaths during pregnancy. Additionally, research should examine how these strategies can be integrated into existing community health systems and whether they can be adapted to address other health concerns. Conceptualization, A.M., V.G.P. and R.P. ; methodology, A.M. and V.G.P. ; formal analysis, A.M. and D.B. ; writing--original draft preparation, V.G.P., A.M., C.R. and D.B. ; writing--review and editing, M.H., R.P. and C.R. ; visualization, A.M. and V.G.P.