The exponential growth in wireless communication demands intelligent and adaptive spectrum-sharing solutions, especially within dynamic and densely populated 6G Cognitive Radio Networks (CRNs). This paper introduces a novel hybrid framework combing the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with Proximal Policy Optimisation (PPO) for multi-objective optimisation in spectrum management. The proposed model balances spectrum efficiency, interference mitigation, energy conservation, collision rate reduction, and QoS maintenance. Evaluation on synthetic and ns-3 datasets shows that the NSGA-II and PPO hybrid consistently outperforms the random, greedy, and stand-alone PPO strategies, achieving higher cumulative reward, perfect fairness (Jain’s Fairness Index = 1.0), robust hypervolume convergence (65.1%), up to 12% reduction in PU collision rate, 20% lower interference, and approximately 40% improvement in energy efficiency. These findings validate the framework’s effectiveness in promoting fairness, reliability, and efficiency in 6G wireless communication systems.

The exponential growth in wireless communication demands intelligent and adaptive spectrum-sharing solutions, especially within dynamic and densely populated 6G cognitive radio networks (CRNs). This paper introduces a novel hybrid framework combing the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with Proximal Policy Optimisation (PPO) for multi-objective optimisation in spectrum management. The proposed model balances spectrum efficiency, interference mitigation, energy conservation, collision rate reduction, and QoS maintenance. Evaluation on synthetic and ns-3 datasets shows that the NSGA-II and PPO hybrid consistently outperforms Random, Greedy, and standalone PPO strategies, achieving higher cumulative reward, perfect fairness (Jain’s Index = 1.0), robust hypervolume convergence (65.1%), up to 12% reduction in PU collision rate, 20% lower interference, and approximately 40% improvement in energy efficiency. These findings validate the framework’s effectiveness in promoting fairness, reliability, and efficiency in 6G wireless communication systems.

This research project aims to empower artisanal miners by developing a wearable technology: a smart helmet designed to improve their safety and overall mining experience. In a quantitative study approach involving 400 artisan miners, a survey questionnaire was conducted to gather data on their experiences and perceptions regarding current mining practices and the potential utility of smart helmets. The survey responses offered valuable insights into these miners' specific needs and challenges. The collected data was analysed using the IBM Statistical Package for the Social Sciences (SPSS) to provide descriptive and inferential statistics. By applying the principles of design science methodology, the survey findings informed the design and development of a smart helmet tailored to the artisanal mining context. The resulting smart helmet integrates features such as real-time location tracking, environmental monitoring, health tracking and communication capabilities. This study showcases the potential of combining user-centric design with advanced technology to address the unique requirements of artisanal miners, ultimately leading to safer and more efficient mining operations. The proposed smart helmet represents a significant advancement in leveraging technology to enhance the well-being and productivity of artisanal miners.