A Comparative Analysis of MQTT and CoAP to Test Transmission Performance in Medical Digital Twin Applications

In the context of technological advancement, the convergence of Digital Twin, Internet of Things (IoT), Artificial Intelligence (AI), and the emergence of sixth-generation (6 G) cellular networks is poised to revolutionize industries worldwide. These concepts are closely interconnected, with the digital twin being particularly reliant on the others due to its requirement for the exploitation of highly state-of-the-art technologies. In the field of IoT communications, two widely utilized protocols are Message Queuing Telemetry Transport (MQTT) and Constrained Application Protocol (CoAP). Our work is relevant to this context because, by employing the Unreal Engine 5 tool, we have developed a human digital twin that is directly fed with health data gathered from Internet of Medical Things (IoMT) sensors. Specifically, we have constructed a testbed for the purpose of collecting data on a physical patient and evaluating the performance of data transmission towards the digital twin via two distinct protocols, namely MQTT and CoAP. The two protocols have been implemented with different settings, thus allowing for a comprehensive quantification and assessment of performance in terms of latency, throughput, and energy consumption. Furthermore, this paper discusses the potential of AI in enhancing medical digital twin applications, thus establishing a foundation for future works on this topic.