Kevin Bylykbashi

Opportunistic networks are characterized by intermittent connectivity and dynamic topologies, which pose significant challenges for efficient message delivery, resource management, and routing decision-making. This paper introduces the Fuzzy Control Routing Protocol, a novel approach designed to address these challenges by leveraging fuzzy logic to enhance routing decisions and improve overall network performance. The protocol considers buffer occupancy, angle to destination, and the number of unique connections of the target nodes to make context-aware routing decisions. It was implemented and evaluated using the FuzzyC framework for simulations and the opportunistic network environment simulator for realistic network scenarios. Simulation results demonstrate that the Fuzzy Control Routing Protocol achieves competitive delivery probability, efficient resource utilization, and low overhead compared to the Epidemic and MaxProp protocols. Notably, it consistently outperformed the Epidemic protocol across all metrics and exhibited comparable delivery probability to MaxProp while maintaining significantly lower overhead, particularly in low-density scenarios. The results demonstrate the protocol’s ability to adapt to varying network conditions, effectively balance forwarding and resource management, and maintain robust performance in dynamic vehicular environments.

Vehicular Ad Hoc Networks (VANETs) have gained significant attention due to their potential to enhance road safety, traffic efficiency, and passenger comfort through vehicle-to-vehicle and vehicle-to-infrastructure communication. However, VANETs face resource management challenges due to the dynamic and resource constrained nature of vehicular environments. Integrating cloud-fog-edge computing and Software-Defined Networking (SDN) with VANETs can harness the computational capabilities and resources available at different tiers to efficiently process and manage vehicular data. In this work, we used this paradigm and proposed an intelligent approach based on Fuzzy Logic (FL) to evaluate the processing and storage capability of vehicles for helping other vehicles in need of additional resources. The effectiveness of the proposed system is evaluated through extensive simulations and a testbed. Performance analysis between the simulation results and the testbed offers a comprehensive understanding of the proposed system and its performance and feasibility.

Drivers are held responsible for the vast majority of traffic accidents. While most of the errors that cause these accidents are involuntary, a significant number of them are caused by irresponsible driving behaviors, which must be utterly preventable. Irresponsible driving, in addition, is often associated with the stress drivers experience while driving. We have previously implemented an intelligent system based on fuzzy logic for determining driver’s stress in Vehicular Ad hoc Networks (VANETs), called Fuzzy-based System for Determining the Stress Feeling Level (FSDSFL), considering the driver’s impatience, the behavior of other drivers, and the traffic condition as input parameters. In this work, we present an Improved FSDSFL (IFSDSFL) system, which considers the driving experience and history as an additional input. We show through simulations the effect that driving experience and history and the other parameters have on the determination of the stress feeling level and demonstrate some actions that can be performed when the stress exceeds certain levels.