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.

This paper presents a novel hybrid Extreme Learning Machine (ELM) with Cuckoo Search Algorithm (CSA) for the classification purposes of the digitally modulated signals such as Phase Shift Keying (PSK), Frequency Shift Keying (FSK), and Quadrature Amplitude Modulation (QAM). Nine modulation schemes having different orders have been considered for this research work. First, the Gabor filter is used to extract the key features from the received signal which are then optimized by CSA. Finally, ELM is used to classify the modulation schemes using these optimized features. Our proposed CSA-ELM approach is not only fast convergent and robust but also manifests improved percentage classification accuracy at low SNRs and lower sample size for both AWGN and Rayleigh fading channels.

With the spread of Internet technologies, the use of social media has increased exponentially. Although social media has many benefits, it has become the primary source of disinformation or fake news. The spread of fake news is creating many societal and economic issues. It has become very critical to develop an effective method to detect fake news so that it can be stopped, removed or flagged before spreading. To address the challenge of accurately detecting fake news, this paper proposes a solution called Statistical Word Embedding over Linguistic Features via Deep Learning (SWELDL Fake), which utilizes deep learning techniques to improve accuracy. The proposed model implements a statistical method called “principal component analysis” (PCA) on fake news textual representations to identify significant features that can help identify fake news. In addition, word embedding is employed to comprehend linguistic features and Bidirectional Long Short-Term Memory (Bi-LSTM) is utilized to classify news as true or fake. We used a benchmark dataset called SWELDL Fake to validate our proposed model, which has about 72,000 news articles collected from different benchmark datasets. Our model achieved a classification accuracy of 98.52% on fake news, surpassing the performance of state-of-the-art deep learning and machine learning models.

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.