Our aim with this paper is to introduce a new protocol for self-organization and data-collection in heterogeneous wireless sensor networks (WSNs) called SDCP (Self-organization and Data-collection Protocol). This protocol is supported by two types of special mobile nodes (SƿN) that are rich in resources (those that can move by flying (such as Unmanned Aerial Vehicle - UAVs) and those that can move on the ground (Actors)). Through our SDCP protocol, we plan to ensure optimal performance measures, such as energy efficiency, low latency, high success rate, a moderate number of hops, and suitably interactions between static sensors and SpNs while benefiting from their mobility (in a hierarchical heterogeneous network with unstable architecture due to mobility). The SƿN moves conforming to a particular mobility model, namely random waypoint mobility model, which allows pause time. Each SƿN, while it is suspended, forms a transitory cluster that heads for collecting and processing sensed data (sensed by static sensor nodes in that cluster). During his trip, SƿN piggybacks the collected data and returns it to a base station (BS). Once the data transferred to the BS, SƿN continues its movement again for a new data collection. The simulations carried out, delighted us with valuable performance outcomes.
Research into vision-based obstacle detection systems plays a fundamental role in developing autonomous vehicles and intelligent transportation systems. In fact, an intelligent vehicle should be ready to discover vehicles and possible obstacles along its route. This paper presents a comparative study of existing state-of-the-art methods for obstacle detection. However, there have been a large number of studies that thoroughly explored various types of state-of-the-art methods for obstacle detection. Here, this paper compares three methods in obstacle detection, namely the “Robust obstacle detection for ADAS using distortions of IPM of a monocular camera”, “Robust Obstacle Detection and Recognition for DAS”, and “Real-time Obstacle Detection Over Rails Using Deep CNN” and analyzes the obtained results.
Smart meters have become the essential elements of smart city applications, where smart grid (SG) environments could be enabled with the real-time monitoring and collection of power usage data through trusted wireless networks. Key agreement schemes are fundamental to ensuring secure and trustworthy power usage assessment in SG systems, while also protecting data integrity and optimizing computational costs. This paper proposes an Authentic Key Agreement (AKA) scheme based on Blockchain designed to enhance the security, integrity, and efficiency of SG systems. The proposed scheme utilizes the Advanced Encryption Standard (AES) cryptographic algorithm integrated with Blockchain technology, achieving better security and computational efficiency compared to existing state-of-the-art schemes. Simulation results obtained using the NS3 simulator demonstrate a significant reduction in network delay. Specifically, when the number of nodes in a network cluster is doubled, the proposed framework achieves a 25% reduction in delay. Thus, this proposed framework offers increased efficiency, improved security, lower costs, and better transparency of power usage in SGs.
This paper presents a novel metaheuristic binary crow search algorithm (CSA) designed for positive-unlabeled (PU) learning, a paradigm where only positive and unlabeled data are available, with applications in many diversified fields, such as medical diagnosis and fraud detection. The algorithm represent a useful adaptation of CSA, itself inspired by the food-hiding behavior of crows. The proposed BiCSA-PUL (binary crow search algorithm for positive-unlabeled learning) selects reliable negative samples from unlabeled data using binary vectors, and updates positions employing Hamming distance, guided by a modified F1-score, as fitness function. The algorithm was tested on 30 samples from 10 diverse datasets, outperforming seven state-of-the-art PU learning methods. The results reveal that BiCSA-PUL provides a robust and efficient approach for PU learning, significantly improving fitness and reliability. This work opens new avenues for applying metaheuristic optimization methods to challenging classification problems with limited labeled data. The main limitations are the potentially time-intensive process of parameters tuning and reliance on initial sampling.
The hospital environment is characterised by stressful situations and constant pressures. Resilience is essential for healthcare professionals to cope with these challenges and maintain their well-being and effectiveness. This study aims to assess the level of resilience and its relationship with sociodemographic factors among healthcare professionals working in hospitals in the Algerian province of Batna. To this end, a cross-sectional survey based on an anonymous, self-administered questionnaire (Resilience Scale-25) was conducted in healthcare settings of Batna-Algeria. Sociodemographic characteristics and resilience attributes were analyzed using descriptive and inferential statistics. The results indicate, on the one hand, a low level of resilience among healthcare professionals (mean RS-25 score = 115.63) and, on the other, a significant relationship between resilience and sociodemographic factors (p <0.05). Consequently, the hospitals studied need to adopt appropriate strategies to continuously improve healthcare professionals' resilience.
