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

A middleware-based monitoring architecture that leverages DDS QoS policies to guarantee reliable, low-latency data exchange across distributed clinical components

Muhammad Dikko Gambo; MD Sakibul Islam; Basem Almadani; Farouq Aliyu; Abdullahi Sani Shuaibu· 2026· DOI 10.48550/arXiv.2608.14597

The core problem

Real-time patient monitoring in modern hospitals demands communication systems that can sustain low latency and high reliability while scaling across heterogeneous deployments. Clinical environments increasingly rely on distributed medical components—bedside sensors, patient-room gateways, and ward-level applications—that must exchange data continuously and consistently. Traditional socket-based messaging, while simple and widely used, often struggles to maintain delivery guarantees under network load, leading to packet loss that can compromise patient safety. This paper addresses the gap by proposing a middleware-based monitoring system built on the Data Distribution Service (DDS). DDS is a publish-subscribe middleware standard that provides fine-grained control over Quality of Service (QoS) policies, making it a strong candidate for clinical communication where consistent data delivery is non-negotiable. The authors—Muhammad Dikko Gambo, MD Sakibul Islam, Basem Almadani, Farouq Aliyu, and Abdullahi Sani Shuaibu—investigate whether DDS can outperform conventional sockets in emulated clinical monitoring workflows. The central research question is: can DDS, with appropriate QoS co

Innovation

The evaluation indicates that DDS with Reliable QoS avoids packet loss in all tested scenarios, providing more dependable delivery than socket-based messaging under network load. In contrast, socket-based messaging exhibited packet loss as network load increased, confirming the limitations of conventional approaches in clinical settings. Key findings include: - **Reliable QoS**: Zero packet loss across all tested scenarios, with acceptable latency for real-time monitoring. - **Best Effort QoS**: Lower communication overhead but occasional packet loss under high load, making it suitable only for non-critical data streams. - **Socket-based messaging**: Demonstrated packet loss under network load, with reliability degrading as concurrent data streams increased. The results are summarized in the following table (qualitative representation): | Communication Method | Packet Loss | Latency | Overhead | |----------------------|-------------|---------|----------| | DDS Reliable QoS | None | Low | Moderate | | DDS Best Effort QoS | Occasional | Very Low| Low | | Socket-based | Yes | Variable| Low | The experiments confirm that DDS Reliable QoS

Real-time patient monitoring in modern hospitals demands communication systems that can sustain low latency and high reliability while scaling across heterogeneous deployments. Clinical environments increasingly rely on distributed medical components—bedside sensors, patient-room gateways, and ward-level applications—that must exchange data continuously and consistently. Traditional socket-based messaging, while simple and widely used, often struggles to maintain delivery guarantees under network load, leading to packet loss that can compromise patient safety.

This paper addresses the gap by proposing a middleware-based monitoring system built on the Data Distribution Service (DDS). DDS is a publish-subscribe middleware standard that provides fine-grained control over Quality of Service (QoS) policies, making it a strong candidate for clinical communication where consistent data delivery is non-negotiable. The authors—Muhammad Dikko Gambo, MD Sakibul Islam, Basem Almadani, Farouq Aliyu, and Abdullahi Sani Shuaibu—investigate whether DDS can outperform conventional sockets in emulated clinical monitoring workflows.

Why it matters

The findings support the use of DDS as a practical middleware option for real-time clinical communication where consistent data delivery is required. The ability to configure QoS policies allows system architects to tailor communication behavior to the criticality of different data streams—for example, using Reliable QoS for vital signs and Best Effort for less critical telemetry. The trade-off between delivery guarantees and communication overhead is a central consideration. Reliable QoS introduces additional overhead through acknowledgments and retransmissions, but this is acceptable in clinical settings where data loss can have severe consequences. Best Effort QoS remains useful for high-frequency, low-criticality data where occasional loss is tolerable. From a cybersecurity perspective, DDS offers built-in security features (not evaluated in this paper) that can be leveraged to protect patient data. The taxonomy candidates—Architecture, Cybersecurity, Network, and Cryptography—highlight the broader context in which this work sits. Future research could integrate DDS Security plugins to address confidentiality and integrity. The layered data bus structure and modular domain participants facilitate integration with existing hospital systems, reducing deployment barriers. However, the study acknowledges limitations: experiments were conducted in an emulated environment, and real-world hospital networks may introduce additional challenges such as interference, mobility, and legacy system integration. Overall, the paper provides compelling evidence that DDS-based communication can enhance the reliability of real-time patient monitoring. The results encourage further exploration of DDS in clinical settings, including large-scale deployments and interoperability with standards such as HL7 and FHIR.

Who should read this

CS practitioners and researchers

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