arXiv subjects

Basem Almadani

Publications and source records attributed to Basem Almadani.

2 recordsLinked to original sources

arXiv2026

Real-Time Patient Monitoring with Heterogeneous Systems Using DDS-Based Communication

Real-time patient monitoring requires communication systems that maintain low latency and high reliability while scaling across heterogeneous hospital deployments. This paper presents a middleware-based monitoring system that uses the Data Distribution Service (DDS) to coordinate data exchange among distributed medical components. The system architecture consists of modular DDS domain participants deployed across patient rooms and ward-level applications, connected through a layered data bus structure. Quality of Service (QoS) policies, including Reliable and Best Effort, are configured and evaluated to examine trade-offs between delivery guarantees and communication overhead. A prototype implementation is developed to emulate clinical monitoring workflows, and experiments are conducted in comparison with socket-based messaging. The evaluation indicates that DDS with Reliable QoS avoids packet loss in the tested scenarios and provides more dependable delivery than sockets under network load. These results support the use of DDS as a practical middleware option for real-time clinical communication where consistent data delivery is required.

cs.NI↗

arXiv2024

A Real-Time DDS-Based Chest X-Ray Decision Support System for Resource-Constrained Clinics

Internet of Things (IoT)-based healthcare systems offer significant potential for improving healthcare delivery in humanitarian and resource-constrained environments, providing essential services to underserved populations in remote areas. However, limited network infrastructure in such regions makes reliable communication challenging for traditional IoT systems. This paper presents a real-time chest X-ray decision support system designed for hospitals in remote locations. The proposed system integrates a fine-tuned ResNet50 deep learning model for disease classification with Fast DDS real-time middleware to ensure reliable and low-latency communication between healthcare practitioners and the inference system. Experimental results show that the model achieves an accuracy of 88.61%, precision of 88.76%, and recall of 88.49%. The system attains an average throughput of 3.2 KB/s and an average latency of 65 ms, demonstrating its suitability for deployment in bandwidth-constrained environments. These results highlight the effectiveness of DDS-based middleware in enabling real-time medical decision support for remote healthcare applications.

eess.IV↗