بحوث سكوبس — فنر علي جوده السويدي

The wireless sensor network (WSN) is one of the most important achievements of the modern technological revolution and greatly affects human life. These networks suffer from some limitations that affect their performance, such as the limited power of their devices. Clustering technology is one of the effective power conservation techniques that improves the performance of WSNs, so this article focuses on developing aggregation technology by proposing an Energy Saving, Multi-hops, Clustering, and Hierarchy (ESMCH) protocol for homogeneous WSNs. The proposed protocol improves clustering technology in several directions. The first direction is by determining the ideal number of cluster head nodes (CHs) suitable for the network, and the second is through the perfect choice of nodes that will represent CHs. The third trend is the selection of secondary CHs. The last trend is the formation of clusters, which depends on an important parameter (distance to CHs). The proposed system uses the TDMA method to schedule the data transfer process to CHs (inside the cluster) and the CSMA method to organize the exchange of data packets between CHs (outside the clusters) for delivery to the base station. The results of simulation experiments in MATLAB R (2020 a) show that the proposed protocol extends network lifetime by 32% compared to the LEACH algorithm and by 26% compared to the SEP algorithm. The results also display that the ESMCH protocol increased the throughput of the WSN by 16.8% compared to its throughput when using the SEP algorithm and by 30% when using the LEACH algorithm. © 2024 University of Baghdad-College of Science. All rights reserved.

Securing communications in drone networks is an essential aspect of ensuring good network performance. Data transferred over the Internet of Drones (IoD) Communications, which is rapidly growing, holds crucial information for navigation, coordination, data sharing, and control, and enables the creation of smart services in many sectors. Sixth-generation (6G) mobile systems are anticipated to be impacted by the plethora of IoD. The possibility of malevolent drones intercepting or altering data before it reaches its target is a serious worry. Operations on IoD networks may be hampered by this, and safety issues may arise. Utilizing three security levels, the suggested method solves the issue of malicious drones in the IoD network. The suggested system’s first level allocates a trust value to IoD drones based on behaviors including prior drone behavioral histories, packet losses, and processing delays. This can be accomplished by choosing drones as investigators to monitor the actions of neighboring drones and assess the level of trust value. The second level involves communication protection, which is accomplished by historical communication behavior. The purpose of the final security level is to safeguard the reliability of the data used to calculate trust values. The fundamental topical of our proposed system is to propose and explore a novel tactic for detecting malicious UAVs within the internet of drone framework, using theoretical and simulations models. Because that 6G networks are still now in the developmental stage, the results presented are based on predictive analyses and simulations rather than real-world applications. © 2024 River Publishers.

Wireless mesh networks have recently presented a promising environment for many researchers to develop large-scale wireless communication. Traffic in WMNs often suffers from congestion due to heavy traffic load’s saturation of certain routes. Therefore, this article proposes an efficient approach for congestion awareness and load balancing in WMNs, based on the Ant Colony Optimization (ACO) approach. The proposed approach aims to raise the performance of the WMN by distributing the traffic load between optimal routes and avoiding severe traffic congestion. The proposed approach relies on three basic mechanisms: detection of severe congestion within the ideal paths used for data transmission, creation of ideal secondary paths with updated pheromone values, and distribution of the traffic load (data packet flow) between the primary and secondary ideal paths. According to the results of the NS2 simulator, the suggested approach increased the WMN throughput by 14.8% when compared to the CACO approach and by 37% when employing the WCETT approach. The results also showed that the proposed approach achieved an average end-to-end delay closing of 0.0562, while WCETT and CACO approaches achieved an average end-to-end delay close to 0.1021 and 0.0976, respectively. The results indicated that the proposed approach achieved a lower percentage of dropped packets by 6.97% and 0.99% compared to the WCETT and CACO approaches. Thus, the proposed approach is effective in improving the performance of WMNs. © 2023 River Publishers.

The focus of this study encompasses the burgeoning field of abnormal behavior detection through computer vision, with a specific emphasis on gait analysis. A foundational gait model has been constructed, deriving from an extensive analysis of various gait types. The research endeavors to establish a model capable of discerning individual abnormal behavior, predicated on their walking patterns. A meticulous evaluation and comparison of three predominant feature extraction methodologies—Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP), and Center Symmetric Local Binary Pattern (CS-LBP)—constitute the core of this study. These techniques have been selected owing to their prevalent application and validated efficacy across numerous computer vision domains. Following feature extraction, the classification stage is initiated, utilizing Convolutional Neural Networks (CNNs), a paradigm of deep learning algorithms. The methodology has undergone rigorous testing and evaluation on a comprehensive dataset, inclusive of both standard and aberrant behavioral instances. A high performance level, signified by a 99% accuracy rate, was achieved through the application of the CS-LBP method for abnormal behavior detection. The empirical results underscore the significance of gait feature extraction methods in augmenting the system’s proficiency in anomaly detection. © 2023 International Information and Engineering Technology Association. All rights reserved.

Video steganography has become a popular option for protecting secret data from hacking attempts and common attacks on the internet. However, when the whole video frame(s) are used to embed secret data, this may lead to visual distortion. This work is an attempt to hide sensitive secret image inside the moving objects in a video based on separating the object from the background of the frame, selecting and arranging them according to object's size for embedding secret image. The XOR technique is used with reverse bits between the secret image bits and the detected moving object bits for embedding. The proposed method provides more security and imperceptibility as the moving objects are used for embedding, so it is difficult to notice the changes in the moving objects instead of using background area for embedding in the video. The experimental results showed the better visual quality of the stego video with PSNR values exceeding 58 dB, this indicates that the proposed method works without causing much distortion in the original video and transmitted secret message. © 2023 University of Baghdad. All rights reserved.