Unmanned aerial vehicles (UAVs) play a crucial role in enhancing search and rescue (SAR) operations by accessing inaccessible areas, accomplishing challenging tasks, and providing real-time monitoring and modeling in situations where human presence is unsafe. Multi-UAVs can collaborate more efficiently and cost-effectively than a single large UAV for performing SAR operations. In multi-UAV systems, task allocation (TA) is a critical and complex process involving cooperative decision making and control to minimize the time and energy consumption of UAVs for task completion. This paper offers an exhaustive review of both static and dynamic TA algorithms, confidently assessing their strengths, weaknesses, and limitations. It provides valuable insights into addressing research questions related to specific UAV operations in SAR. The paper rigorously discusses outstanding issues and challenges and confidently presents potential directions for the future development of task assignment algorithms. Finally, it confidently highlights the challenges of multi-UAV dynamic TA methods for SAR. This work is crucial for gaining a comprehensive understanding of multi-UAV dynamic TA algorithms and confidently emphasizes critical open issues and research gaps for future SAR research and development, ensuring that readers feel informed and knowledgeable.

Cognitive Radio (CR) based standards are apt to cater the diverse communication requirements for the humongous volume of data generated by Smart Grid applications. Although CR technology is one of the most promising technique to increase the spectral efficiency of any wireless communication network, efficient spectrum allocation among Secondary User (SUs) in different application scenarios remains an intriguing area for researchers. In this research article, we propose a clustering-based approach to deal with Channel Allocation (CA) problem among SUs considering practical constraints in SG environment. We first present a simple CR based SG communication network architecture by dividing the service area into groups of SUs called Neighborhood Area Network (NAN) clusters, depending upon the distance of Smart Meters from Data Concentrator Unit (DCU). Then we formulate a multiple constraint NP-hard CA problem using interference avoidance strategy by considering two practical scenarios: fairness-based allocation and priority-based allocation. We then propose our CA algorithm based on Cat Swarm Optimization (CSO) to eliminate the severe integer constraints of the problem under consideration. We simulate the two above-mentioned practical scenarios to measure a number of allocations per SU, Jain’s Fairness Index and per user average rewards. Moreover, conventional average user rewards are compared for varying number of channels, SUs, and rounds to evaluate the performance of proposed CA scheme. The results indicate that our proposed CA algorithm works well, for both fairness-based and priority-based cases.

Unmanned air vehicle communication (UAV) systems have recently emerged as a quick, low-cost, and adaptable solution to numerous challenges in the next-generation wireless network. In particular, UAV systems have shown to be very useful in wireless communication applications with sudden traffic demands, network recovery, aerial relays, and edge computing. Meanwhile, non-orthogonal multiple access (NOMA) has been able to maximize the number of served users with the highest traffic capacity for future aerial systems in the literature. However, the study of joint optimization of UAV altitude, user pairing, and power allocation for the problem of capacity maximization requires further investigation. Thus, a capacity optimization problem for the NOMA aerial system is evaluated in this paper, considering the combination of convex and heuristic optimization techniques. The proposed algorithm is evaluated by using multiple heuristic techniques and deployment scenarios. The results prove the efficiency of the proposed NOMA scheme in comparison to the benchmark technique of orthogonal multiple access (OMA). Moreover, a comparative analysis of heuristic techniques for capacity optimization is also presented.