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The current paper presents an alternative and innovative technique to predict the severity of pollution of high voltage insulator using a higher harmonics component with up to the 7 th component of leakage current. The leakage current was measured using a current transformer and a shunt resistor. Next, laboratory tests were conducted on glass and porcelain insulators with artificial pollution under salt-fog pollution state which is further represented by three levels, namely light, medium, and high contamination. In this case, the formulation of a new severity of harmonic index refers to a ratio of the sum of 5 th and 7 th to the 3 rd harmonic component. More importantly, the new index managed to provide more accurate results when used as a diagnostic tool for the levels of pollution, compared to the ratio of the total harmonic distortion (THD) to the number of odd harmonics components (n) as the boundaries. In this case, the insulators were found to be in a clean and normal condition when the K (5+7)/3 value was greater than 3%. Contrastingly, the insulators were in an extreme condition when the K (5+7)/3 was lower than 3%. Nevertheless, there is a high probability of a flashover in glass and porcelain insulators if the K (5+7)/3 value is less than 2%. The present study shows the possibility of utilizing the value of strange harmonics up to the 7 th component of leakage current as the parameter for the monitoring of leakage current in overhead insulators in the presence of contamination. Overall, it can be concluded that the 3 rd , 5 th , and 7 th harmonics details extracted from the leakage current act as a good indicator for the level of contamination. INDEX TERMS Polluted insulators, leakage current, harmonic components, total harmonic distortion, fast fourier transform, salt fog.

The sixth generation (6G) wireless communication network presents itself as a promising technique that can be utilized to provide a fully data-driven network evaluating and optimizing the end-toend behavior and big volumes of a real-time network within a data rate of Tb/s. In addition, 6G adopts an average of 1000+ massive number of connections per person in one decade (2030 virtually instantaneously). The data-driven network is a novel service paradigm that offers a new application for the future of 6G wireless communication and network architecture. It enables ultra-reliable and low latency communication (URLLC) enhancing information transmission up to around 1 Tb/s data rate while achieving a 0.1 millisecond transmission latency. The main limitation of this technique is the computational power available for distributing with big data and greatly designed artificial neural networks. The work carried out in this paper aims to highlight improvements to the multi-level architecture by enabling artificial intelligence (AI) in URLLC providing a new technique in designing wireless networks. This is done through the application of learning, predicting, and decision-making to manage the stream of individuals trained by big data. The secondary aim of this research paper is to improve a multi-level architecture. This enables user level for device intelligence, cell level for edge intelligence, and cloud intelligence for URLLC. The improvement mainly depends on using the training process in unsupervised learning by developing data-driven resource management. In addition, improving a multi-level architecture for URLLC through deep learning (DL) would facilitate the creation of a data-driven AI system, 6G networks for intelligent devices, and technologies based on an effective learning capability. These investigational problems are essential in addressing the requirements in the creation of future smart networks. Moreover, this work provides further ideas on several research gaps between DL and 6G that are up-to-date unknown.INDEX TERMS Artificial neural networks, artificial intelligence, Internet of Things, sixth-generation wireless communication and network architecture, URLLC.

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