Researchers from University of Brescia suggested an IoT-based communication architecture for connecting prototype instruments to the cloud
A cyber-physical system (CPS) is a mechanism that is regulated or monitored by computer-based algorithms that are combined with the Internet and its users. The system contains several different intelligent entities capable of dynamic interactions with the environment. These systems must adhere to stringent requirements in terms of dependability, security, safety, and efficiency in real time. CPs also need to fulfill privacy constraints. Now, a team of researchers from University of Brescia suggested a novel IoT-based architecture that meets the requirements of prototype medical instruments.
The architecture is based on well-accepted, secure, open, and interoperable message oriented solutions and is economical. The architecture can also include other information generated from ancillary sensors. The real-time solution can be used for early diagnosis of dementia based on Transcranial Magnetic Stimulation. The architecture can also be used for different diagnostic solutions at the prototype stage. The interoperable message oriented solution enables to easily interconnect several prototype instruments and medical personnel on both local and geographical scale in real-time.
The team found that the end-to-end delay of the communication framework is well-suited for real-time requirements of medical control applications with a human-in-the-loop. The team preferred Advanced Message Queuing Protocol (AMQP) solutions as it has the same performance of Message Queuing Telemetry Transport (MQTT) and offers more features. To assess time-related metrics on both a local and geographical scale, the team implemented a purposely-designed experimental setup. The team found that the overall end-to-end delay from the transcranial magnetic stimulation (TMS) instrument to the remote client has an average value of about 140 milliseconds and surges to 160 milliseconds for the opposite direction. This in turn proves the viability of the proposed approach. The research was published in the journal MDPI Sensors on March 31, 2019.