Auditory stimuli strongly shape emotional and physiological states, making them central to affective computing and mental health technologies. We present the study of three auditory conditions, spiritual meditation (SM), music (M), and natural silence (NS), using acoustic time-series signals. To support this, we introduce the Spiritual, Music, Silence Acoustic Time Series (SMSAT) dataset, a benchmark of controlled acoustic recordings with demographic diversity. We develop a contrastive learning-based SMSAT encoder that learns discriminative embeddings from ATS data, achieving 99% accuracy. In addition, we propose the Calmness Analysis Model (CAM), integrating multi-domain features for affective state classification, achieving a 99% accuracy in the three-stimulus classification task. Inter & intra-class feature space separability, calmness evaluation using Temporal Segmented Response Profiling (TSRP) confirm significant physiological differences across auditory conditions, with SM showing stronger effects on cardiac response characteristics (CRC).WaveGAN is used to generate additional dataset. Under subject-wise evaluation, CAM reached$98.4\%$accuracy, and the SMSAT Encoder achieved$96.5\%$accuracy. This work provides a validated dataset and scalable deep learning framework for stress monitoring, well-being, and therapeutic audio interventions.

The rapid adoption of cloud computing has introduced critical security challenges in the cloud, with evolving cyberattacks exposing vulnerabilities in conventional intrusion detection systems (IDS). Existing approaches often struggle with high false-positive rates, poor handling of imbalanced traffic, and computational overhead in dynamic cloud environments. To address these issues, we propose SLCAE-BiLSTM, a deep learning-based IDS which enhances feature extraction and sequential learning. The Single-Layer Contractive Autoencoder (SLCAE) ensures efficient data representation by minimizing redundancy while preserving critical attack patterns. Meanwhile, the Bidirectional Long Short-Term Memory (BiLSTM) captures temporal dependencies in network traffic, improving the detection of rare attacks. Experimental evaluations on two benchmark datasets demonstrate SLCAE-BiLSTM's superiority, achieving 99.91% and 99.87% accuracy in binary classification and 97.73% and 91.22% in multi-class classification, surpassing state-of-theart models such as SCAE-SVM, SAE-SVM, and SDAE-SVM. These high accuracy rates indicate a significant reduction in misclassification and improved detection of both common and rare cyber threats. Furthermore, its reduced computational overhead and faster inference time makes it an efficient solution for enhancing cloud security against emerging threats.

Reconfigurable intelligent surfaces (RISs) are emerging as a transformative technology for sixth-generation (6G) wireless networks. They enable dynamic manipulation of the propagation environment to enhance signal coverage, mitigate interference, and improve spectral and energy efficiencies. However, this flexibility introduces significant security vulnerabilities when RISs are maliciously controlled. This study explores the threats posed by such RISs, focusing on their potential to compromise the security and integrity of 6G networks. From an adversarial perspective, we analyze key attack vectors, including sophisticated jamming attacks that disrupt communication, eavesdropping attacks that intercept communications, and pilot contamination attacks that impair channel estimation accuracy, all contributing to severe performance degradation. For each attack, we detail the underlying mechanisms and adversarial optimization strategies designed to maximize impact. A case study quantifies the practical effects of these malicious RIS-based attacks in a simulated 6G network scenario. This research emphasizes the critical need for robust defense mechanisms and proposes essential research directions to address the evolving threats from malicious RISs, ensuring the security of 6G networks.

Unmanned-aerial-vehicles (UAVs) have been gaining much attention in the next-generation wireless networks due to their ability to enhance coverage and provide advanced services, particularly for first responders. UAVs equipped with mobile-edge computing (MEC) capabilities can migrate computational resources to airborne platforms. However, it is crucial to manage resources efficiently to optimize overall network performance. Moreover, in public safety scenarios, UAVs can help charge low-power Internet of Things (IoT) devices to sustain system operations. A holistic approach to managing communication, computation, caching, and energy resources is necessary to leverage UAV-assisted MEC networks fully. We formulated an optimization problem to minimize latency and reduce resource costs associated with communication, computation, caching, and energy harvesting while maximizing the number of IoT devices served by UAVs. Therefore, we integrated digital twin technology to analyze the latency. The optimization problem is challenging as it involves a mixed-integer nonlinear programming problem. To address this complexity, we propose a multistage offloading algorithm named the penalty function method heuristic algorithm that combines a learning algorithm with an interior-point method, ultimately delivering a practical solution. Our simulation results validate the performance of the proposed algorithm, which yields superior results compared to the simple relaxation heuristic algorithm.

Multihop communication has gained prominence within the realm of the Internet of Underwater Things (IoUT) owing to its exceptional reliability amidst the challenges posed by the underwater acoustic environment. Despite this, the persistence of limitations caused by propagation delay, high collision rate, and limited energy in underwater communication remains, representing the most formidable hurdles in ensuring the successful transmission of data gathered by sensor nodes. To address these challenges, we employ a machine learning (ML)-based optimal cooperating node selection for each hop, considering the Shortest propagation delay, minimal residual Energy, and a low Collision rate (referred to as SEC). For this purpose, we initially assemble the sensor nodes to create a list of cooperative nodes, considering the aspect of SEC. Then, using an assembled list of cooperating sensor nodes, we employ ML-based algorithms, such as reinforcement learning (RL-SEC), deep Q-networks (DQN-SEC), and deep deterministic policy gradient (DDPG-SEC), to predict the optimal cooperating node for each hop. The simulation results of the DDPG-SEC demonstrate a significant improvement of approximately 56% when compared with RL-SEC, DQN-SEC, and other state-of-the-art techniques.

Sixth-generation (6G) networks pose substantial security risks because confidential information is transmitted over wireless channels with a broadcast nature, and various attack vectors emerge. Physical layer security (PLS) exploits the dynamic characteristics of wireless environments to provide secure communications, while reconfigurable intelligent surfaces (RISs) can facilitate PLS by controlling wireless transmissions. With RIS-aided PLS, a lightweight security solution can be designed for low-end Internet of Things (IoT) devices, depending on the design scenario and communication objective. This article discusses RIS-aided PLS designs for 6G-IoT networks against eavesdropping and jamming attacks. The theoretical background and literature review of RIS-aided PLS are discussed, and design solutions related to resource allocation, beamforming, artificial noise, and cooperative communication are presented. We provide simulation results to show the effectiveness of RIS in terms of PLS. In addition, we examine the research issues and possible solutions for RIS modeling, channel modeling and estimation, optimization, and machine learning. Finally, we discuss recent advances, including simultaneous transmitting and reflecting-RIS and malicious RIS.

The usage of drones has tremendously increased in different sectors spanning from military to industrial applications. Despite all the benefits they offer, their misuse can lead to mishaps, and tackling them becomes more challenging particularly at night due to their small size and low visibility conditions. To overcome those limitations and improve the detection accuracy at night, we propose an object detector called Ghost Auto Anchor Network (GAANet) for infrared (IR) images. The detector uses a YOLOv5 core to address challenges in object detection for IR images, such as poor accuracy and a high false alarm rate caused by extended altitudes, poor lighting, and low image resolution. To improve performance, we implemented auto anchor calculation, modified the conventional convolution block to ghost-convolution, adjusted the input channel size, and used the AdamW optimizer. To enhance the precision of multiscale tiny object recognition, we also introduced an additional extra-small object feature extractor and detector. Experimental results in a custom IR dataset with multiple classes (birds, drones, planes, and helicopters) demonstrate that GAANet shows improvement compared to state-of-the-art detectors. In comparison to GhostNet-YOLOv5, GAANet has higher overall mean average precision (mAP@50), recall, and precision around 2.5%, 2.3%, and 1.4%, respectively. The dataset and code for this paper are available as open source at https://github.com/ZeeshanKaleem/GhostAutoAnchorNet.

Unmanned aerial vehicle (UAV) is a key enabler for communication systems beyond the fifth generation due to its applications in almost every field, including mobile communications and vertical industries. However, there exist many challenges in 3-D UAV placement, such as resource and power allocation, trajectory optimization, and user association. This problem becomes even more complex as UAV changes its height, which in turn varies the channel conditions and reduces the coverage on account of high co-channel interference. To maximize the user coverage in uplink transmission, we propose to jointly optimize the 3-D UAV placement and path-loss compensation factor. Moreover, we also optimize the latter for various UAV deployment heights in the suburban environment. Simulation results have demonstrated that the joint optimization of the UAV height and path-loss compensation factor results in better coverage and throughput performance as compared to the baseline scheme.

A device‐to‐device (D2D) discovery scheme is key enabler for proximity‐based services in third generation partnership project long‐term evolution‐advanced systems for public safety (PS) and general LTE scenarios. The deployment of D2D networks results in severe co‐channel interference between conventional cellular users and D2D users, and faces proximity interference management challenges because of the co‐existence of multiple D2D users. We propose a time and energy‐efficient contention‐resolving device discovery resource allocation (TEECR‐DDRA) scheme that has the capability to enhance the success ratio for discovery of D2D users by reducing collisions among users. Moreover, the proposed TEECR‐DDRA scheme has the ability to prioritise PS users to meet their QoS and latency requirements. Furthermore, multi‐channel slotted ALOHA with energy sensing can be used to increase the probability of successful discovery of non‐PS users. This ability helps to reduce the discovery time of PS users under disaster scenarios, and also reduces the energy consumption of non‐PS users by minimising the number of beacon retransmissions. System‐level simulations show that the proposed TEECR‐DDRA scheme performs remarkably well under D2D network. Compared with the conventional random access scheme, the proposed scheme almost doubles the discovery range and significantly improves the success ratio for discovery of D2D users.