Detection of anomalies in video surveillance plays a key role in ensuring the safety and security of public spaces. The number of surveillance cameras is growing, making it harder to monitor them manually. So, automated systems are needed. This change increases the demand for automated systems that detect abnormal events or anomalies, such as road accidents, fighting, snatching, car fires, and explosions in real-time. These systems improve detection accuracy, minimize human error, and make security operations more efficient. In this study, we proposed the Composite Recurrent Bi-Attention (CRBA) model for detecting anomalies in surveillance videos. The CRBA model combines DenseNet201 for robust spatial feature extraction with BiLSTM networks that capture temporal dependencies across video frames. A multi-attention mechanism was also incorporated to direct the model’s focus to critical spatiotemporal regions. This improves the system’s ability to distinguish between normal and abnormal behaviors. By integrating these methodologies, the CRBA model improves the detection and classification of anomalies in surveillance videos, effectively addressing both spatial and temporal challenges. Experimental assessments demonstrate that the CRBA model achieves high accuracy on both the University of Central Florida (UCF) and the newly developed Road Anomaly Dataset (RAD). This model enhances detection accuracy while also improving resource efficiency and minimizing response times in critical situations. These advantages make it an invaluable tool for public safety and security operations, where rapid and accurate responses are needed for maintaining safety.

Photocatalytic reduction of CO₂ with CH₄ through the dry reforming of methane (DRM) is an attractive approach to recycling greenhouse gases into valuable chemicals and fuels; however, this process is quite challenging. Although there is growing interest in designing efficient photocatalysts, they are less stable, and have lower photoactivity when employed for DRM reactions. Herein, we developed a noble metal-free hierarchical graphitic carbon nitride (HC₃N₄) loaded with cobalt (Co) for highly efficient and stable photocatalytic dry reforming of methane to produce synthesis gases (CO and H₂). The performance of the newly designed Co/HC₃N₄ composite was tested for different reforming systems such as the dry reforming of methane, bi-reforming of methane (BRM) and reforming of CO₂ with methanol–water. The performance of HC₃N₄ was much higher compared to bulk g-C₃N₄, whereas Co/HC₃N₄ was found to be promising for higher charge carrier separation and visible light absorption. The yield of CO and H₂ with HC₃N₄ was 1.85- and 1.81-fold higher than when using g-C₃N₄ due to higher charge carrier separation. The optimized 2% Co/HC₃N₄ produces CO and H₂ at an evolution rate of 555 and 41.2 µmol g⁻¹ h⁻¹, which was 18.28- and 1.74-fold more than using HC₃N₄ during photocatalytic dry reforming of methane (DRM), with a CH₄/CO₂ feed ratio of 1.0. This significantly enhanced photocatalytic CO and H₂ evolution during DRM was due to efficient charge carrier separation in the presence of Co. The CH₄/CO₂ feed ratio was further investigated, and a 2:1 ratio was best for CO production. In contrast, the highest H₂ was produced with a 1:1 feed ratio due to the competitive adsorption of the reactants over the catalyst surface. The performance of the composite was further investigated for bi-reforming methane and methanol. Using photocatalytic CO₂ reduction with CH₄/H₂O, the production of CO and H₂ was reduced, whereas significantly higher CO and H₂ evolved using the BRM process involving methanol. Using methanol with CO₂ and H₂O, 10.77- and 1.39-fold more H₂ and CO efficiency was achieved than when using dry reforming of methane. The composite was also very stable for continuous synthesis gas production during DRM in consecutive cycles. Thus, a co-assisted g-C₃N₄ nanotexture is promising for promoting photocatalytic activity and can be further explored in other solar energy applications.

Surveillance cameras have been widely used for monitoring in both private and public sectors as a security measure. Close Circuits Television (CCTV) Cameras are widely used to surveillance normal and anomalous incidents. Real-world anomaly detection in complex environment situations is a significant challenge due to its diverse nature manually reviewing this vast data is difficult, time-consuming, and prone to errors. Human operators may overlook crucial information due to failure, which reduces the accuracy of manual monitoring. This highlights the need for automated solutions to improve accuracy and efficiency We proposed a Bidirectional Motion Temporal (BiMT) model to address this issue by coupling a pre-trained model of deep neural networks. This system utilizes a Convolutional Neural Network (CNN) to extract the best spatial key features from the video stream, which are converted into time series data and coupled with Recurrent Neural Network (RNN) deep learning model Bidirectional Long Short-Term Memory (BiLSTM) extract temporal key features, and Multi-Head Self-Attention (MHSA) allows for the detection of short-term frame correlations. In addition, created a custom transformer encoder with relative positional embedding that improves the detection and recognizes long-range frame dependencies. Furthermore, categorical focal loss (CFL) is used during training to prioritize essential features and further enhance the model's precision. The more sophisticated ConvNeXtv2 model produced average AUC scores of 98.6%, 86.5%, and 83.5% for the UCF-Crime, UBI-Fight, and RAD datasets respectively.

Network lifetime and energy efficiency are crucial performance metrics used to evaluate wireless sensor networks (WSNs). Decreasing and balancing the energy consumption of nodes can be employed to increase network lifetime. In cluster-based WSNs, one objective of applying clustering is to decrease the energy consumption of the network. In fact, the clustering technique will be considered effective if the energy consumed by sensor nodes decreases after applying clustering, however, this aim will not be achieved if the cluster size is not properly chosen. Therefore, in this paper, the energy consumption of nodes, before clustering, is considered to determine the optimal cluster size. A two-stage Genetic Algorithm (GA) is employed to determine the optimal interval of cluster size and derive the exact value from the interval. Furthermore, the energy hole is an inherent problem which leads to a remarkable decrease in the network’s lifespan. This problem stems from the asynchronous energy depletion of nodes located in different layers of the network. For this reason, we propose Circular Motion of Mobile-Sink with Varied Velocity Algorithm (CM2SV2) to balance the energy consumption ratio of cluster heads (CH). According to the results, these strategies could largely increase the network’s lifetime by decreasing the energy consumption of sensors and balancing the energy consumption among CHs.

In modern years, the Internet of Things (IoT) has gained tremendous growth and development in various sectors because of its scalability, self-configuring, and heterogeneous factors. It performs a vital role in improving multimedia communication and reducing production costs. The multimedia data consist of various types and formats (text, audio, videos, etc.), which are forwarded in the form of blocks of bits in the network layer of TCP/IP. Due to limited resources available to IoT-built devices, most of the Multimedia Internet of Things (MIoT)-based applications are delay constraints, especially for big data content. Similarly, multimedia-based applications are more vulnerable to security burdens and lower the trust of data processing. In this paper, we present a secure and sustainable prediction framework for MIoT data transmission using machine learning, which aims to offer intelligent behavior of the system with information protection. Firstly, the network edges exploit a regression analysis for a real-time multimedia routing scheme and achieve precise delivery towards the media servers. Secondly, an efficient and low-processing asymmetric process is proposed to provide secure data transmission between the IoT devices, edges, and data servers. Extensive experiments are performed over the OMNET++ network simulator, and its significance is achieved by an average for energy consumption by 71%, throughput by 30.5%, latency by 22%, bandwidth by 34.5%, packets overheads by 38.5%, computation time by 12.5%, and packet drop ratio by 35% in the comparison of existing schemes.

The Internet of Things (IoT) provides robust services to connected sensors in a distributed manner, and maintains real-time communication using wireless standards. The smart network has offered many autonomous smart systems to collect information from remote nodes, and share it by exploring the network layer. Researchers have recently offered a variety of ways to increase the effectiveness of emerging applications using trustworthy relaying systems. However, there are still many issues with route reformulation due to frequent disconnections of mobile devices and resource limitations. Furthermore, most of the existing methods for IoT systems are unable to utilize network resources, which lowers the performance of green networks. Thus, providing a foolproof solution for the autonomous system with energy efficiency is a challenging task. Therefore, this paper presents an algorithm for the mobile network using fog computing to reduce network disconnectivity. Furthermore, using security services, the proposed algorithm efficiently explores the characteristics of the device, and avoids malicious traffic to drain the additional energy consumption of the network. The main aspects of the proposed algorithm are as follows: (i) using the adjustable transmission power, the proposed algorithm offers a fault-tolerant solution to transmit the aggregated data over the unpredictable wireless system; (ii) with the support of fog nodes, the data load is reduced among devices with the offering of a secured authentication scheme. Using simulations, the proposed algorithm is tested, and its significance is demonstrated against other related studies.