Background: Continuous surveillance helps people with diabetes live better lives. A wide range of technologies, including the Internet of Things (IoT), modern communications, and artificial intelligence (AI), can assist in lowering the expense of health services. Due to numerous communication systems, it is now possible to provide customized and distant healthcare. Main problem: Healthcare data grows daily, making storage and processing challenging. We provide intelligent healthcare structures for smart e-health apps to solve the aforesaid problem. The 5G network must offer advanced healthcare services to meet important requirements like large bandwidth and excellent energy efficacy. Methodology: This research suggested an intelligent system for diabetic patient tracking based on machine learning (ML). The architectural components comprised smartphones, sensors, and smart devices, to gather body dimensions. Then, the preprocessed data is normalized using the normalization procedure. To extract features, we use linear discriminant analysis (LDA). To establish a diagnosis, the intelligent system conducted data classification utilizing the suggested advanced-spatial-vector-based Random Forest (ASV-RF) in conjunction with particle swarm optimization (PSO). Results: Compared to other techniques, the simulation’s outcomes demonstrate that the suggested approach offers greater accuracy.
SummaryIn wireless sensor networks (WSNs), the data must reach a processing node with less energy consumption and delay rate. However, with the specific consideration of the unique properties of sensor networks such as limited power, stringent bandwidth, dynamic topology due to node failures, adding/removing nodes, or even physical mobility, high network density and large‐scale deployments have posed many challenges in the design and management of sensor networks. These challenges have demanded energy awareness and robust protocol design. Efficient utilization of the sensor's energy resources and maximizing the network lifetime were the primary design considerations for the proposed work. As a result, the new interior gateway routing with particle swarm optimization (IGRPSO) is presented with minimal latency and a higher packet delivery ratio in WSN; optimum link weights are calculated in a fraction of the original data to the new protocol. Network entropy maximization is a conceptual framework that guides the development of the protocol and the computational techniques required to determine which traffic distributions are not only best but also realizable through link‐state routing. Therefore, a routing algorithm provides the optimal and economically optimal route. We used the MATLAB simulation environment to compare and contrast the performance of the dynamic source routing (DSR) protocol and enhanced interior gateway routing protocol, DSR protocol with particle swarm optimization with that of the IGRPSO protocol, which has the maximum packet delivery ratio of 75.74% and less packet drop of 25.37%, less energy consumption of 0.3674 J.
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