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A Survey on the Role of Complex Networks in IoT and Brain Communication

by Vijey Thayananthan1, Aiiad Albeshri2, Hassan A. Alamri3, Muhammad Bilal Qureshi4, Muhammad Shuaib Qureshi5,*

1 Cybersecurity Department, Faculty of Computing, Engineering and Science, University of South Wales, Wales, UK
2 Department of Computer Science, Faculty of Computing and Information Technology (FCIT), King Abdulaziz University, Jeddah, 21589, Saudi Arabia
3 Computer Science Department, Umm Al-Qura University, Makkah, 21955, Saudi Arabia
4 Department of Computer Science & IT, University of Lakki Marwat, Lakki Marwat, 28420, KPK, Pakistan
5 Department of Computer Engineering, Chungnam National University, Daejeon, 34134, Republic of Korea

* Corresponding Author: Muhammad Shuaib Qureshi. Email: email

(This article belongs to the Special Issue: Applications of Digital Twins in Intelligent Healthcare Systems)

Computers, Materials & Continua 2023, 76(3), 2573-2595. https://doi.org/10.32604/cmc.2023.040184

Abstract

Complex networks on the Internet of Things (IoT) and brain communication are the main focus of this paper. The benefits of complex networks may be applicable in the future research directions of 6G, photonic, IoT, brain, etc., communication technologies. Heavy data traffic, huge capacity, minimal level of dynamic latency, etc. are some of the future requirements in 5G+ and 6G communication systems. In emerging communication, technologies such as 5G+/6G-based photonic sensor communication and complex networks play an important role in improving future requirements of IoT and brain communication. In this paper, the state of the complex system considered as a complex network (the connection between the brain cells, neurons, etc.) needs measurement for analyzing the functions of the neurons during brain communication. Here, we measure the state of the complex system through observability. Using 5G+/6G-based photonic sensor nodes, finding observability influenced by the concept of contraction provides the stability of neurons. When IoT or any sensors fail to measure the state of the connectivity in the 5G+ or 6G communication due to external noise and attacks, some information about the sensor nodes during the communication will be lost. Similarly, neurons considered sing the complex networks concept neuron sensors in the brain lose communication and connections. Therefore, affected sensor nodes in a contraction are equivalent to compensate for maintaining stability conditions. In this compensation, loss of observability depends on the contraction size which is a key factor for employing a complex network. To analyze the observability recovery, we can use a contraction detection algorithm with complex network properties. Our survey paper shows that contraction size will allow us to improve the performance of brain communication, stability of neurons, etc., through the clustering coefficient considered in the contraction detection algorithm. In addition, we discuss the scalability of IoT communication using 5G+/6G-based photonic technology.

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Cite This Article

APA Style
Thayananthan, V., Albeshri, A., Alamri, H.A., Qureshi, M.B., Qureshi, M.S. (2023). A survey on the role of complex networks in iot and brain communication. Computers, Materials & Continua, 76(3), 2573-2595. https://doi.org/10.32604/cmc.2023.040184
Vancouver Style
Thayananthan V, Albeshri A, Alamri HA, Qureshi MB, Qureshi MS. A survey on the role of complex networks in iot and brain communication. Comput Mater Contin. 2023;76(3):2573-2595 https://doi.org/10.32604/cmc.2023.040184
IEEE Style
V. Thayananthan, A. Albeshri, H. A. Alamri, M. B. Qureshi, and M. S. Qureshi, “A Survey on the Role of Complex Networks in IoT and Brain Communication,” Comput. Mater. Contin., vol. 76, no. 3, pp. 2573-2595, 2023. https://doi.org/10.32604/cmc.2023.040184



cc Copyright © 2023 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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