Wireless communication networks have been witnessing unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Although there are many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is still room and need for further capacity enhancement in wireless communication networks, especially for the cases of unusual people gatherings, such as sport competitions, musical concerts, etc. Unmanned aerial vehicles (UAVs) have been identified as one of the promising options to enhance capacity due to their easy implementation, pop-up fashion operation, and cost-effective nature. The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity where it is needed. However, UAVs mostly have limited energy storage, hence, their energy consumption must be optimized to increase flight time. In this survey, we investigate different energy optimization techniques with a top-level classification in terms of the optimization algorithm employed—conventional and machine learning (ML). Such classification helps understand the state-of-the-art and the current trend in terms of methodology. In this regard, various optimization techniques are identified from the related literature, and they are presented under the above-mentioned classes of employed optimization methods. In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs. Moreover, novel concepts, such as reflective intelligent surfaces and landing spot optimization, are also covered to capture the latest trends in the literature.

This study explores the transformative role of digitalization in enhancing social sustainability within supply chains. Social sustainability, a critical dimension alongside economic and environmental sustainability, aims to balance environmental health and financial stability by improving the quality of life within supply chain networks. Digital technologies introduced by Industry 4.0 and further developed by Industry 5.0 have significantly impacted various aspects of social sustainability. We investigate the interactions and significance of social sustainability enablers influenced by these technologies. Key enablers include worker well-being support, skills development, and equitable educational opportunities. To perform this analysis, we employ the ISM-Fuzzy MICMAC (Matrice d’Impacts Croisés Multiplication Appliquée à un Classement) and DEMATEL (Decision Making Trial and Evaluation Laboratory) methods, which are widely recognized in the literature for their effectiveness. Our findings provide a strategic roadmap for supply chain stakeholders by highlighting the complex relationships between digitalization and social sustainability enablers. We identify key enablers such as digital inclusion, privacy, digital skills, visibility, innovative business design, decision-making, education, and capacity building to achieve a more human-centered and sustainable supply chain. The results clearly reveal the relationships among these enablers, forming a hierarchical structure that underscores their interactions. Additionally, this study contributes to the existing literature by addressing the often-overlooked social dimension of sustainability, offering practical insights for policymakers and business leaders on leveraging digital technologies for sustainable development.

Even though dynamic cell switching is a prominent approach for energy optimization in heterogeneous wireless communication networks, it results in spectrum under-utilization as the spectrum originally occupied by the base stations that are turned off remain dormant. In order to make the businesses of primary network (PN) operators, who hold the spectrum license, more profitable and sustainable as well as to avoid spectrum under-utilization, this dormant spectrum can be leased to the secondary network (SN) operators who cannot afford to purchase the spectrum license. In this study, first, the cell switching problem, which solely focuses on the amount of energy saved, is translated to a problem of revenue maximization by including the spectrum leasing concept and converting the energy saving to monetary saving from reduced electricity bills. In this regard, two spectrum demand scenarios are considered for the SN operator: delay tolerant (DT), for non-real time applications, and non-delay tolerant (NDT), for real time applications. Then, a cell switching and spectrum leasing framework based on simulated annealing algorithm is developed to maximize the revenue of the PN while respecting the quality-of-service constraints. The simulation results reveal that the DT spectrum demand is more beneficial to both PN and SN operators as it results in greater revenue for the former while the latter is able to access more spectrum to meet higher service demands. This finding suggests that if the application can tolerate delays, then it makes more sense for both PN and SN to adopt the DT scenario. In addition, it is observed that the performance of the proposed framework is very close to that of the optimal solution with a significant reduction in the computation complexity.