The fifth-generation mobile network (5G), as the fundamental enabler of Industry 4.0, has facilitated digital transformation and smart manufacturing through AI and cloud computing (CC). However, B5G is viewed as a turning point that will fundamentally transform existing global trends in wireless communication practices as well as in the lives of masses. B5G foresees a world where physical–digital confluence takes place. This study intends to see the world beyond 5G with the transition to 6G assuming the lead as future wireless communication technology. However, despite several developments, the dream of an era without latency, unprecedented speed internet, and extraterrestrial communication has yet to become a reality. This article explores main impediments and challenges that the 5G–6G transition may face in achieving these greater ideals. This article furnishes the vision for 6G, facilitating technology infrastructures, challenges, and research leads towards the ultimate achievement of “technology for humanity” objective and better service to underprivileged people.
In this paper, we present an auto-encoder-based machine learning framework for self organizing networks (SON). Traditional machine learning approaches, for example, K Nearest Neighbor, lack the ability to be precisely predictive. Therefore, they can not be extended for sequential data in the true sense because they require a batch of data to be trained on. In this work, we explore artificial neural network-based approaches like the autoencoders (AE) and propose a framework. The proposed framework provides an advantage over traditional machine learning approaches in terms of accuracy and the capability to be extended with other methods. The paper provides an assessment of the application of autoencoders (AE) for cell outage detection. First, we briefly introduce deep learning (DL) and also shed light on why it is a promising technique to make self organizing networks intelligent, cognitive, and intuitive so that they behave as fully self-configured, self-optimized, and self-healed cellular networks. The concept of SON is then explained with applications of intrusion detection and mobility load balancing. Our empirical study presents a framework for cell outage detection based on an autoencoder using simulated data obtained from a SON simulator. Finally, we provide a comparative analysis of the proposed framework with the existing frameworks.
The number of elderly people increases quickly in many countries, under the global population aging situation. It is an upsetting fact that many elderly people are suffering from the dementia, which seriously obstructs their independent living and travel. It is a pervasive problem that the demented elderly individuals are easy to get lost or go into danger during alone travel in daily life. Therefore we propose a novel mobile system named "Canderoid" to monitor independent outdoor travel of the elderly individuals remotely, with aid from the caretaker. The system is composed mainly of an android terminal (Wanderoid), an MQTT broker, and a platform on caretaker side. In the system, an android terminal named "Wanderoid" is implemented on a smartphone to capture the travelling status, using built-in smartphone sensors (i.e. camera with an adhesive fish-eye lens, compass and GPS). The terminal device is a normal smartphone, with a fish-eye lens attached on the camera. The sensor data are transferred to the platform of caretaker after capturing. The data transmission work relies on a message pushing architecture, which deals with mobile IP address changing and enables remote manipulation of the smartphone terminal, by introducing the MQTT broker. Then the caretaker platform can interpret sensor data and real-timely present the travelling status using snapshot taken by the fish-eye camera, street view and map. A reliability test, energy dissipation test and usability test are carried out on the prototype to verify that the system is effective, easy-to-use, reliable and energy-saving, from the viewpoints of both technology and human factors.
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