Fifth-generation (5G) cellular networks have led to the implementation of beyond 5G (B5G) networks, which are capable of incorporating autonomous services to swarm of unmanned aerial vehicles (UAVs). They provide capacity expansion strategies to address massive connectivity issues and guarantee ultra-high throughput and low latency, especially in extreme or emergency situations where network density, bandwidth, and traffic patterns fluctuate. On the one hand, 6G technology integrates AI/ML, IoT, and blockchain to establish ultra-reliable, intelligent, secure, and ubiquitous UAV networks. 6G networks, on the other hand, rely on new enabling technologies such as air interface and transmission technologies, as well as a unique network design, posing new challenges for the swarm of UAVs.Keeping these challenges in mind, this article focuses on the security and privacy, intelligence, and energy-efficiency issues faced by swarms of UAVs operating in 6G mobile network. In this state-of-the-art review, we integrated blockchain and AI/ML with UAV networks utilizing the 6G ecosystem. The key findings are then presented, and potential research challenges are identified. We conclude the review by shedding light on future research in this emerging field of research.

The groundbreaking Unmanned Aerial Vehicles (UAVs) technology has gained significant attention from both academia and industrial experts due to several applications, such as military missions, power lines inspection, precision agriculture, remote sensing, delivery services, traffic monitoring and many more. UAVs are expected to become a mainstream delivery element by 2040 to address the ever-increasing demand for delivery services. Similarly, UAV-assisted monitoring approaches will automate the inspection process, lowering mission costs, increasing access to remote locations and saving time and energy. Despite the fact that unmanned aerial vehicles (UAVs) are gaining popularity in both military and civilian applications, they have a number of limitations and critical problems that must be addressed in order for missions to be effective. One of the most difficult and time-consuming tasks is charging UAVs. UAVs’ mission length and travel distance are constrained by their low battery endurance. There is a need to study multi-UAV charging systems to overcome battery capacity limitations, allowing UAVs to be used for a variety of services while saving time and human resources. Wired and Wireless Power Transfer (WPT) systems have emerged as viable options to successfully solve this difficulty. In the past, several research surveys have focused on crucial aspects of wireless UAV charging. In this review, we have also examined the most emerging charging techniques for UAVs such as laser power transfer (LPT), distributed laser charging (DLC), simultaneous wireless information and power transfer (SWIPT) and simultaneous light wave information and power transfer (SLIPT). The classification and types of UAVs, as well as various battery charging methods, are all discussed in this paper. We’ve also addressed a number of difficulties and solutions for safe operation. In the final section, we have briefly discussed future research directions.

The development of tourist destinations has been determined by sustainable energy management and the advancement of traditional methods. The rate of development must adapt to technological innovations while also considering future generations. The present study aims to determine to what extent tourism personnel are aware of the skills, knowledge, and methods for sustainable energy management, and also to analyze the Internet of Things as a technological solution for sustainable energy management at tourism destinations in India with the help of the Servqual method. It is important to implement modern technologies such as internal things and develop a sustainable attitude toward tourism. Findings suggested that over each attribute of Servqual model, reliability, assurance, tangibles, empathy and responsiveness of IoT as sustainable energy management solutions at tourism destinations in India, tourism stakeholders have higher level of expectations (23.41, 19.86, 18.45, 23.60 and 24.73) and perceptions (18.34, 16.50, 14.97, 18.17 and 19.20) followed by tourists expectations (22.10, 17.36, 16.01, 22.62 and 21.87) and perceptions (19.32, 11.75, 09.46, 15.06 and 17.43) and local residents expectations (20.17, 14.61, 14.87, 19.46 and 18.81) and perceptions (13.48, 08.85, 07.73, 13.54 and 12.94), respectively. Results also showed that older generations and traditional tourism destinations are unable to cope with the modern advanced terminology, tools, and management strategies, which makes the present study the most significant about changing the traditional way of energy management and developing tourism destinations as sustainable and responsible.

The survival of an organization in the contemporary competitive and dynamic global business environment is associated with its creative workforce. This study explores the impact of transformational leadership (TL) and employee adaptiveness on workforce creativity. The mediating role of employee adaptiveness and the moderating role of knowledge sharing has also been tested. A simple random sampling technique was used to collect data from the 561 employees working in three star and five star hotels. Statistical techniques such as correlation and hierarchical regressions were used for testing hypotheses. To test the mediation effect, we used the statistical procedure suggested by Preacher and Hayes. The results revealed that employee creativity is initiated through TL in hotels and TL positively affects employee adaptiveness. The results of the mediating analysis confirmed the mediating role of employee adaptiveness between TL and creativity link in the context of hotel industry operating in developing nations. Moreover, the findings also revealed that knowledge sharing plays key role for TL and employee adaptiveness links. In this study, quantitative research design was utilized, and the cross-sectional method was used for data collection. Future research designs can be based on both quantitative and current qualitative analyses. The current model can be enriched using factors such as knowledge management systems, organizational structures, and digital platforms in the future.

This study work is among the few attempts to understand the significance of AI and its implementation barriers in the healthcare systems in developing countries. Moreover, it examines the breadth of applications of AI in healthcare and medicine. AI is a promising solution for the healthcare industry, but due to a lack of research, the understanding and potential of this technology is unexplored. This study aims to determine the crucial AI implementation barriers in public healthcare from the viewpoint of the society, the economy, and the infrastructure. The study used MCDM techniques to structure the multiple-level analysis of the AI implementation. The research outcomes contribute to the understanding of the various implementation barriers and provide insights for the decision makers for their future actions. The results show that there are a few critical implementation barriers at the tactical, operational, and strategic levels. The findings contribute to the understanding of the various implementation issues related to the governance, scalability, and privacy of AI and provide insights for decision makers for their future actions. These AI implementation barriers are encountered due to the wider range of system-oriented, legal, technical, and operational implementations and the scale of the usage of AI for public healthcare.