Urban intersections pose significant challenges for reliable information dissemination among connected and autonomous vehicles (CAVs) due to dynamic topologies, occlusion effects, and fluctuating vehicle densities. Existing models, such as epidemic or graph-based approaches, fail to capture phase transition behavior critical for scalable communication. This study adapts percolation theory to model CAV networks, aiming to identify critical density thresholds (ρc) and optimize communication parameters for robust connectivity. A hybrid simulation framework integrates traffic flow dynamics (SUMO) and network modeling (NS-3), incorporating occlusion effects and dynamic clustering to derive ρc. Results reveal a phase transition at ρc ≈ 0.4 veh/m2 under moderate occlusion, with latency reductions of 60% (450 ms to 180 ms) above this threshold. Optimization identifies Pareto-optimal transmission range (r = 200 m) and packet rate (λ =5 Hz), achieving 92% packet delivery at 210 ms latency. The percolation model outperforms epidemic approaches, requiring 40% lower density for 95% coverage. This work establishes percolation theory as a physics-inspired tool for CAV network design, offering actionable insights for infrastructure planning and parameter tuning. Future research should integrate vehicle-to-infrastructure (V2I) elements and validate findings in real-world deployments.
Ontologies offer a powerful means of structuring medical knowledge for AI-driven diagnosis. This paper investigates the conceptual design of a framework that integrates ontologies and logical reasoning to automate the diagnosis of nutrition-related medical conditions. There is a significant gap in the literature on direct diagnosis using ontologies and logical reasoning for such conditions. By proposing a theoretical framework, this work aims to enhance diagnostic accuracy, efficiency, and reliability while providing explainable diagnoses. It is important to note that this study is part of an ongoing Ph.D. thesis and that the implementation of the proposed framework is planned in future work. This approach has the potential to improve patient outcomes and reduce healthcare costs significantly.
this paper demonstrates a brute-force attack on password based ModbusTCP implementations in PLCs and HMIs using a swarm of 20 microcontrollers. To counter this attack, a misleading LADDER-based algorithm that misleads attackers with false register values and randomized passwords is proposed. To evaluate the attack and the proposed protocol, two attack scenarios and two password lengths have been leveraged on a PLC in a LAN environment. Results show that the misleading protocol causes each attacker to receive different passwords and data, disrupting brute-force attempts while enabling attacker identification. When tested with one thread and 20 Arduino kits, each kit found a different password and received different data when reading PLC registers. This approach not only prevents unauthorized access but also allows administrators to track attackers' IP addresses, highlighting the effectiveness of deception-based defenses in industrial security.
The Internet of Things (IoT) has found applications across a wide range of sectors, including smart cities, agriculture, waste management, weather monitoring, and energy grids. Despite its growing impact, there is still room for enhancement—particularly in terms of energy efficiency. This study focuses on extending the lifespan of IoT networks by minimizing the energy consumption of sensor nodes. To achieve this, it proposes an optimal selection of Cluster Heads (CHs) that can effectively balance energy usage across the network. A hybrid meta-heuristic algorithm is introduced, combining the strengths of Simulated Annealing (SA) and the Whale Optimization Algorithm (WOA). Key performance indicators such as node density, energy distribution, ambient temperature, remaining battery power, and a defined cost function are employed to identify the most efficient CHs. Experimental results show that the proposed hybrid method outperforms existing algorithms, including WOA, Adaptive Gravitational Search Algorithm (AGSA), Genetic Algorithm (GA), and Artificial Bee Colony (ABC).
Software-Defined Networking (SDN) offers significant advantages in network management and scalability, but is vulnerable to security threats, particularly distributed denial-of-service (DDoS) attacks. These attacks can manipulate SDN flow tables and disrupt the network operations. This paper presents a comprehensive survey of supervised, unsupervised, and ensemble machine-learning techniques for DDoS detection in SDN. To the best of our knowledge, no previous study has provided such a detailed analysis within a single work. This study evaluates the effectiveness of various machine-learning models, focusing on their applications, benefits, and limitations in securing SDN environments. It reviews state-of-the-art methods and discusses performance metrics such as accuracy and computational complexity. The paper also identifies key research challenges and future directions for DDoS detection in SDN. The structure includes a review of related studies, a taxonomy of machine learning techniques, and a conclusion summarizing the findings and implications.
