An IOT-based framework for providing preventive and intelligent healthcare in ICUs

Document Type: Research Paper


1 Faculty of industrial and systems engineering, Tarbiat Modares University, Tehran 1411713116, Iran

2 Faculty of Industrial and Systems Engineering, Tarbiat Modares University


Background and Objectives: Effective and continuous monitoring is one of the essential requirements of ICUs. In recent years, the advent of new technologies has led to the development of smart ICUs that automatically gather, store and analyze healthcare information. In this paper, we aim to design the information view of an IOT-based framework for smart ICUs to provide preventive and intelligent healthcare. In previous studies conducted to develop architectures for smart ICUs, data processing is done in a large centralized fashion by cloud computers. This approach may take a long time when dealing with a large amount of data. In this paper, we used the fog technique to bring some computation and storage resources to the edge of the network instead of relying on the cloud for everything.
Methods:  A reference architecture model and new technologies such as IoT, cloud computing, fog computing and smart sensors were used to design the information view of a smart ICU architecture. This view consists of five layers of data acquisition, transfer, storage, process and presentation layer. The proposed framework gathers patients’ health-related data continuously and provides real-time analysis.
In this paper, training the models, that took a long time, was performed in the cloud. Instead, classifying the new records, which took much less time, was performed in the fog. This greatly increased the speed of operations (2 ms vs 13590 ms). In addition, conducting calculations in the fog intensely reduced the transmission delays (8 ms vs 108 ms for only SPO2 variable).
Conclusion: New technologies were used to provide the information view of a smart ICU framework. Instead of relying solely on cloud, this paper uses fog technology to bring some computation and storage resources to the edge of the network. This greatly reduced the transmission latency and provided real-time analysis.