Abstract
In recent times, health applications have been gaining rapid popularity in smart cities using the Internet of Medical Things (IoMT). Many real-time solutions are giving benefits to both patients and professionals for remote data accessibility and suitable actions. However, timely medical decisions and efficient management of big data using IoT-based resources are the burning research challenges. Additionally, the distributed nature of data processing in many proposed solutions explicitly increases the threats of information leakages and damages the network integrity. Such solutions impose overhead on medical sensors and decrease the stability of the real-time transmission systems. Therefore, this paper presents a machine-learning model with SDN-enabled security to predict the consumption of network resources and improve the delivery of sensors data. Additionally, it offers centralized-based software define network (SDN) architecture to overcome the network threats among deployed sensors with nominal management cost. Firstly, it offers an unsupervised machine learning technique and decreases the communication overheads for IoT networks. Secondly, it predicts the link status using dynamic metrics and refines its strategies using SDN architecture. In the end, a security algorithm is utilized by the SDN controller that efficiently manages the consumption of the IoT nodes and protects it from unidentified occurrences. The proposed model is verified using simulations and improves system performance in terms of network throughput by 13%, data drop ratio by 39%, data delay by 11%, and faulty packets by 46% compared to HUNA and CMMA schemes.
Highlights
Internet of Things (IoT) is a revolutionary paradigm that is equipped with sensors and physical objects to combine the real and digital worlds [1,2,3]
The Internet of Medical Things (IoMT) represents wearable sensors that cooperate with other medical devices and clinical systems to support health activities
It is observed from various research studies that IoMT offers vast services to patients and the medical team, they have significant security and authentication risks in terms of privacy concerns, especially when health organizations cope with critical medical data [13,14,15]
Summary
Internet of Things (IoT) is a revolutionary paradigm that is equipped with sensors and physical objects to combine the real and digital worlds [1,2,3] It is integrated with different mobile devices and offers smart solutions for the support of societies. Modern health applications substantially increase the volume of medical data that needs to be managed efficiently, demanding the incorporation of big data analytics for examining the data [11,12] It is observed from various research studies that IoMT offers vast services to patients and the medical team, they have significant security and authentication risks in terms of privacy concerns, especially when health organizations cope with critical medical data [13,14,15]
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