A Machine Learning SDN-Enabled Big Data Model for IoMT Systems
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 a...
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doaj-492117204ea846449d8f3513676f5c982021-09-26T00:03:16ZengMDPI AGElectronics2079-92922021-09-01102228222810.3390/electronics10182228A Machine Learning SDN-Enabled Big Data Model for IoMT SystemsKhalid Haseeb0Irshad Ahmad1Israr Iqbal Awan2Jaime Lloret3Ignacio Bosch4Department of Computer Science, Islamia College Peshawar, Peshawar 25120, PakistanDepartment of Computer Science, Islamia College Peshawar, Peshawar 25120, PakistanDepartment of Computer Science, Islamia College Peshawar, Peshawar 25120, PakistanInstituto de Investigacion Para la Gestion Integrada de Zonas Costeras, Universitat Politenica de Valencia, Campus de Gandia, C/Paranimf, 46370 Valencia, SpainInstituto de Telecomunicaciones y Aplicaciones Multimedia (iTEAM), Universitat Politènica de València, Camino Vera sn, 46022 Valencia, SpainIn 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.https://www.mdpi.com/2079-9292/10/18/2228software-defined networkmachine learninginternet of thingsrouting algorithmnetwork resources |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Khalid Haseeb Irshad Ahmad Israr Iqbal Awan Jaime Lloret Ignacio Bosch |
spellingShingle |
Khalid Haseeb Irshad Ahmad Israr Iqbal Awan Jaime Lloret Ignacio Bosch A Machine Learning SDN-Enabled Big Data Model for IoMT Systems Electronics software-defined network machine learning internet of things routing algorithm network resources |
author_facet |
Khalid Haseeb Irshad Ahmad Israr Iqbal Awan Jaime Lloret Ignacio Bosch |
author_sort |
Khalid Haseeb |
title |
A Machine Learning SDN-Enabled Big Data Model for IoMT Systems |
title_short |
A Machine Learning SDN-Enabled Big Data Model for IoMT Systems |
title_full |
A Machine Learning SDN-Enabled Big Data Model for IoMT Systems |
title_fullStr |
A Machine Learning SDN-Enabled Big Data Model for IoMT Systems |
title_full_unstemmed |
A Machine Learning SDN-Enabled Big Data Model for IoMT Systems |
title_sort |
machine learning sdn-enabled big data model for iomt systems |
publisher |
MDPI AG |
series |
Electronics |
issn |
2079-9292 |
publishDate |
2021-09-01 |
description |
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. |
topic |
software-defined network machine learning internet of things routing algorithm network resources |
url |
https://www.mdpi.com/2079-9292/10/18/2228 |
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