Self-Organizing Microservice Composition for IoT-Enabled Neonatal Monitoring

The fast-paced growth of the Internet of Things (IoT) has revolutionized several industries, including the delivery of healthcare applications, due to its ability to support realtime and automated applications. However, several key challenges are associated with the dynamic and complex nature of IoT environments, such as scalability, increased system complexity, and network instability. This paper presents a self-organizing microservice Composition Model created especially for healthcare systems using IoT to address these issues. The Model employs decentralized, local interactions inspired by bio-inspired mechanisms. These mechanisms enable the system to autonomously form complex service composites with minimal human intervention through emergent behaviour. The model leverages an ant agent to optimize bio-inspired mechanisms and a deep learning agent for autonomous service choreography, enhancing the system’s flexibility, adaptability, and overall performance. A continuous neonatal monitoring case study illustrates how the model works in composing services in a self-organizing manner. This research contributes to the advancement of smart healthcare infrastructures by proposing an innovative framework that adapts to the evolving needs of IoT-driven healthcare applications.