An edge AI-enabled IoT healthcare monitoring system for smart cities

Healthcare systems have significantly benefited from Artificial Intelligence (AI) and the Internet of Things (IoT). The vital signs of patients can be continuously monitored using the technologies mentioned above, and timely treatment can be provided. To this end, this paper proposes a scalable, responsive, and reliable AI-enabled IoT and edge computing-based healthcare solution with low latency when serving patients. The system comprises the collection of health-related data, data processing and analysis at edge nodes, and permanent storage and sharing at edge data centers. The edge nodes and edge controller schedule patients and provide resources in real time. Simulations were conducted to test system performance. The results for end-to-end time, computing, optimization, and transmission latency prove to be very promising. To determine system performance in a real-world scenario, a neural network was used to model transmission latency. The system is extremely useful for those who are disabled or elderly, as well as in pandemic situations.

Automated summary

Healthcare systems have greatly benefited from AI and IoT, allowing continuous monitoring of patients and timely treatment. The proposed solution is scalable, responsive, and reliable, using AI and edge computing with low latency. The system involves data collection, processing at edge nodes, and storage/sharing at edge data centers, with real-time patient scheduling and resource allocation.