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

Vijey Thayananthan, Aiiad Albeshri, Hassan A. Alamri, Muhammad Bilal Qureshi, Muhammad Shuaib Qureshi
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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.
复杂网络在物联网和脑通信中的作用综述
物联网(IoT)上的复杂网络和大脑通信是本文的主要关注点。复杂网络的优势可能适用于未来6G、光子、物联网、大脑等通信技术的研究方向。大数据流量、大容量、最小动态延迟等是未来5G+和6G通信系统的一些要求。在新兴通信中,基于5G+/ 6g的光子传感器通信和复杂网络等技术在提高未来物联网和大脑通信需求方面发挥着重要作用。本文将复杂系统视为复杂网络(脑细胞、神经元等之间的连接),需要测量其状态,以分析神经元在大脑通信过程中的功能。在这里,我们通过可观测性来测量复杂系统的状态。利用基于5G+/ 6g的光子传感器节点,发现受收缩概念影响的可观察性提供了神经元的稳定性。当物联网或任何传感器在5G+或6G通信中由于外部噪声和攻击而无法测量连接状态时,将会丢失通信过程中传感器节点的一些信息。同样,考虑到复杂网络概念的神经元,大脑中的神经元传感器失去了通信和连接。因此,在收缩中受影响的传感器节点是等效的,以补偿维持稳定的条件。在这种补偿中,可观察性的损失取决于收缩大小,这是采用复杂网络的关键因素。为了分析可观测性恢复,我们可以使用具有复杂网络性质的收缩检测算法。我们的调查论文表明,通过收缩检测算法中考虑的聚类系数,收缩大小将使我们能够提高大脑通信的性能,神经元的稳定性等。此外,我们还讨论了基于5G+/ 6g光子技术的物联网通信的可扩展性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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