Enhancing healthcare IoT systems for diabetic patient monitoring: Integration of Harris Hawks and grasshopper optimization algorithms

Hosseinzadeh, Mehdi; Arabi, Zohre; Ali, Saqib; Min, Hong; Malik, Mazhar Hussain

Abstract

The integration of the Internet of Things (IoT) in healthcare, especially for people with diabetes, allows for constant health monitoring. This means that doctors can watch over patients' health more closely, making sure they catch any issues early on. With this technology, healthcare workers can be more accurate and effective when keeping an eye on how patients are doing. This not only helps in keeping track of patients' health in real-time but also makes the whole process more reliable and efficient.By implementing appropriate routing techniques, the transmission of diabetic patients' data to medical centers will facilitate real-time and timely responses from healthcare professionals. The grasshopper optimization algorithm is employed in the proposed approach to cluster network nodes, resulting in the formation of a network tree that facilitates the establishment of connections between the cluster head and the base station. After identifying the cluster head and establishing the clusters, the second stage of routing is implemented by employing the Harris Hawks optimization algorithm. This algorithm ensures that the data pertaining to diabetic patients is transmitted to the treatment centers and hospitals with minimal delay. For node routing, the optimal next step is selected based on the parameters such as the residual energy of the node, the ratio of delivered data packages, and the number of the neighbors of the node. To continue, first, the MATLAB software is utilized to simulate the proposed method, and then, it is compared with other similar methods. This comparison is conducted based on various parameters, including delay, energy consumption, network throughput, and network lifespan. Compared to other methods, the proposed method demonstrates a significant 33% improvement in the average point-to-point delay parameter in the subsequent iterations or rounds.

• Journal Article Type: Article
• Acceptance Date: Mar 15, 2024
• Online Publication Date: May 29, 2024
• Publication Date: May 29, 2024
• Deposit Date: Jun 11, 2024
• Publicly Available Date: Jun 11, 2024
• Journal: PloS one
• Electronic ISSN: 1932-6203
• Publisher: Public Library of Science
• Peer Reviewed: Peer Reviewed
• Volume: 19
• Issue: 5
• Article Number: e0301521
• DOI: https://doi.org/10.1371/journal.pone.0301521
• Keywords: Algorithms, Internet of Things, Monitoring, Physiologic - methods, Humans, Diabetes Mellitus - therapy
• Public URL: https://uwe-repository.worktribe.com/output/12040643
• Publisher URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0301521