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As the commercial implementations of 5G networks have been initiated in different regions of the world, the focus of the researchers is bending towards the next generation of wireless communication. This research study intends to investigate the requisites of the fast establishment of the theoretical and practical measures for sixth generation (6G) wireless communication. To this end, this paper first outlined the existing research works that have considered different aspects of 6G, and then based on this existing works, the future vision is established. Then, the 6G vision is based on four types of connectivity and is summarized as “Wherever you think, everything follows your heart.” To fill the gap between the market requirements after one decade and the limited capabilities of 5G, different specifications of 6G that make it an appropriate replacement are discussed. Furthermore, different candidate technologies that can potentially realize the 6G communication are studied, followed by discussion on different challenges in the realization and possible research directions to cope with these challenges. By exploring the vision of future, its specification, and key candidate technologies, this paper attempts to summarize the general 6G framework. In addition, with mentioned challenges in realization of 6G, the aim of this paper is to guide the researcher and attract their interest to consider them.

Dynamic opportunistic channel access with software-defined radio at a network layer in distributed cognitive IoT introduces a concurrent channel selection along with end-to-end route selection for application data transmission. State-of-the-art cognitive IoT big data-based routing protocols are not explored in terms of how the spectrum management is being coordinated with the network layer for concurrent channel route selection during end-to-end channel route discovery for data transmission of IoT and big data applications. In this paper, a reactive big data-based “cognitive dynamic source routing protocol” is proposed for cognitive-based IoT networks to concurrently select the channel route at the network layer from source to destination. Experimental results show that the proposed protocol cognitive DSR with concurrent channel route selection criteria is outperformed. This will happen when it is compared with the existing distributed cognitive DSR with independent channel route application data transmission.

The rapid growth of the Internet of Medical Things (IoMT) has led to the ubiquitous home health diagnostic network. Excessive demand from patients leads to high cost, low latency, and communication overload. However, in the process of parameter updating, the communication cost of the system or network becomes very large due to iteration and many participants. Although edge computing can reduce latency to some extent, there are significant challenges in further reducing system latency. Federated learning is an emerging paradigm that has recently attracted great interest in academia and industry. The basic idea is to train a globally optimal machine learning model among all participating collaborators. In this paper, a gradient reduction algorithm based on federated random variance is proposed to reduce the number of iterations between the participant and the server from the perspective of the system while ensuring the accuracy, and the corresponding convergence analysis is given. Finally, the method is verified by linear regression and logistic regression. Experimental results show that the proposed method can significantly reduce the communication cost compared with the general stochastic gradient descent federated learning.