A Reliable IoT-Based Embedded Health Care System for Diabetic Patients

This paper introduces a reliable health care system for diabetic patients based on the Internet of Things technology. A diabetic health care system with a hardware implementation is presented. The proposed work employs Alaris 8100 infusion pump, Keil LPC-1768 board, and IoT-cloud to monitor the diabetic patients. The security of diabetic data over the cloud and the communication channel between health care system components are considered as part of the main contributions of this work. Moreover, an easy way to control and monitor the diabetic insulin pump is implemented. The \mbox{patient\textquotesingle s} records are stored in the cloud using the Keil board that is connected to the infusion pump. The reliability of the proposed scheme is accomplished by testing the system for five performance characteristics (availability, confidentiality, integrity, authentication, and authorization). The Kiel board is embedded with Ethernet port and Cortex-M3 micro-controller that controls the insulin infusion pump. The secure hash algorithm and secure socket shell are employed to achieve the reliability components of the proposed scheme. The results show that the proposed design is reliable, secure and authentic according to different test experiments and a case study of the Markov model. Moreover, a 99.3% availability probability has been achieved after analyzing the case study.

💡 Research Summary

The paper presents an IoT‑enabled embedded health‑care system specifically designed for diabetic patients. The core hardware consists of an Alaris 8100 insulin infusion pump interfaced with a Keil LPC‑1768 development board that houses a Cortex‑M3 microcontroller. The board controls the pump, collects patient data, and communicates with an IoT‑cloud platform via an Ethernet port. Patient records are stored in the cloud and can be accessed by authorized users (patients, caregivers, hospitals) through web or mobile applications, enabling remote monitoring and control of insulin delivery.

Security is addressed by employing the SHA‑256 hash algorithm to guarantee data integrity and by using the Secure Shell (SSH) protocol to encrypt all communications between the board and the cloud. The authors deliberately avoid HMAC, citing vulnerability to length‑extension attacks, and rely on SHA‑256 alone for integrity verification. While the paper mentions the use of SSH keys for authentication, detailed key‑management procedures and certificate handling are not described, leaving a gap that would need to be filled for a production‑grade deployment.

Reliability is evaluated across five dimensions: availability, confidentiality, integrity, authentication, and authorization. To quantify availability, the authors construct a Markov model that captures component failures and recoveries within the system. The model predicts a 99.3 % probability that the system remains in a normal operating state, which the authors claim validates high availability. However, the transition probabilities, state definitions, and validation methodology of the Markov model are not fully disclosed, limiting reproducibility. Confidentiality and integrity are demonstrated through successful SHA‑256 hash verification and encrypted SSH sessions in test scenarios. Authentication and authorization are enforced by a simple privilege‑based access control layer that restricts system actions to pre‑registered users.

The literature review situates the work among prior IoT health‑care solutions, noting that most existing studies focus on sensor data collection rather than direct actuation of therapeutic devices. By integrating a commercial insulin pump with an embedded controller, the proposed system achieves “on‑time medication” and remote dose adjustment, which represents a notable advancement over purely monitoring‑centric approaches. The authors also highlight the affordability and simplicity of using the LPC‑1768 board, suggesting that the solution can be deployed at relatively low cost.

Nevertheless, several technical shortcomings are evident. The paper lacks a detailed description of the hardware interface between the pump and the LPC‑1768 board (e.g., voltage level shifting, communication protocol, error detection), which is critical for meeting medical device regulatory standards (FDA, CE). Data at rest in the cloud appears to be stored without encryption, exposing a potential privacy risk despite the use of encrypted transmission. Performance metrics such as latency, throughput, and error rates are not quantified, making it difficult to assess real‑time suitability for insulin delivery, where timing errors can have serious clinical consequences.

In summary, the study contributes a proof‑of‑concept IoT‑based insulin delivery system that combines hardware control, cloud storage, and basic cryptographic safeguards. It demonstrates high theoretical availability via a Markov analysis and validates core security functions experimentally. To transition from prototype to clinical use, future work must address comprehensive key management, end‑to‑end data encryption, rigorous hardware validation, detailed performance benchmarking, and extensive clinical testing to verify safety and efficacy.