Vehicular ad hoc networks have highly dynamic topologies, making data exchange among mobile nodes challenging. Named data networking (NDN) has been applied to vehicular networks recently, and the results are promising, as NDN supports highly dynamic topologies better than traditional IP architectures. However, as of yet, vehicular named data networks (VNDNs) have no support for pushing critical data into the network, and the existing consumer pull mechanism introduces undesirable delay. Therefore, we propose a proactive data dissemination scheme for pushing critical content to one-hop neighbours in VNDN. Producers of critical content first send a beacon message containing metadata about tentative new chunks. Upon receiving the beacon message, neighbors make temporary pending interest table entries, allowing them to subsequently store new chunks once received. We evaluate the proposed scheme using ndnSIM and find it more efficient than pure consumer pull VNDN.

Distributed Denial of Service (DDoS) attacks, advanced persistent threats, and malware actively compromise the availability and security of Internet services. Thus, this paper proposes an intelligent agent system for detecting DDoS attacks using automatic feature extraction and selection. We used dataset CICDDoS2019, a custom-generated dataset, in our experiment, and the system achieved a 99.7% improvement over state-of-the-art machine learning-based DDoS attack detection techniques. We also designed an agent-based mechanism that combines machine learning techniques and sequential feature selection in this system. The system learning phase selected the best features and reconstructed the DDoS detector agent when the system dynamically detected DDoS attack traffic. By utilizing the most recent CICDDoS2019 custom-generated dataset and automatic feature extraction and selection, our proposed method meets the current, most advanced detection accuracy while delivering faster processing than the current standard.

Disasters are the uncertain calamities which within no time can change the situation quite drastically. They not only affect the system’s infrastructure but can also put an adverse effect on human life. A large chunk of the IP-based Internet of Things (IoT) schemes tackle disasters such as fire, earthquake, and flood. Moreover, recently proposed Named Data Networking (NDN) architecture exhibited promising results for IoT as compare to IP-based approaches. Therefore to tackle disaster management system (DMS), it is needed to explore it through NDN architecture and this is the main motivation behind this work. In this research, a NDN based IoT-DMS (fire disaster) architecture is proposed, named as NDN-DISCA. In NDN-DISCA, NDN producer pushes emergency content towards nearby consumers. To provide push support, Beacon Alert Message (BAM) is created using fixed sequence number. NDN-DISCA is simulated in ndnSIM considering the disaster scenario of IoT-based smart campus (SC). From results, it is found that NDN-DISCA exhibits minimal delay and improved throughput when compared to the legacy NDN and existing PUSH schemes.

Microgrids (MG), considered the future of power systems, has gained significant attention in the present time. MG’s inherent capability of combining both AC and DC operations in a single frame. Its function is crucial in achieving a sustainable electricity supply and advances a decentralized power system approach. MG not only combines the benefits of both AC and DC but also incorporates distributed generations (DGs) and distributed renewable energy sources (DREs). However, the advancement of DGs and DREs with power electronics interfaces (PEIs), and smart loads (SLs) poses the dynamic nature of DGs and DREs such as bi-directional power flow and varying output, which has introduced certain protection challenges in MG maneuver. Therefore, this paper reviews the protection challenges in MG and critically addresses the assessment of existing protection schemes developed so far. It also categorizes the MG protection issues including its components and faults separately for both AC and DC MGs and analyzes the performance of measurement and protection techniques for MG protection strategy on the basis of operational modes AC and DC MGs.

Prostate cancer (PCa) is a major global concern, particularly for men, emphasizing the urgency of early detection to reduce mortality. As the second leading cause of cancer-related male deaths worldwide, precise and efficient diagnostic methods are crucial. Due to high and multiresolution MRI in PCa, computer-aided diagnostic (CAD) methods have emerged to assist radiologists in identifying anomalies. However, the rapid advancement of medical technology has led to the adoption of deep learning methods. These techniques enhance diagnostic efficiency, reduce observer variability, and consistently outperform traditional approaches. Resource constraints that can distinguish whether a cancer is aggressive or not is a significant problem in PCa treatment. This study aims to identify PCa using MRI images by combining deep learning and transfer learning (TL). Researchers have explored numerous CNN-based Deep Learning methods for classifying MRI images related to PCa. In this study, we have developed an approach for the classification of PCa using transfer learning on a limited number of images to achieve high performance and help radiologists instantly identify PCa. The proposed methodology adopts the EfficientNet architecture, pre-trained on the ImageNet dataset, and incorporates three branches for feature extraction from different MRI sequences. The extracted features are then combined, significantly enhancing the model’s ability to distinguish MRI images accurately. Our model demonstrated remarkable results in classifying prostate cancer, achieving an accuracy rate of 88.89%. Furthermore, comparative results indicate that our approach achieve higher accuracy than both traditional hand-crafted feature techniques and existing deep learning techniques in PCa classification. The proposed methodology can learn more distinctive features in prostate images and correctly identify cancer.