Using a Fuzzy-Bayesian Approach for Predicting the QoS in VANET

Abstract

There are considerable obstacles in the transport sector of developing countries, including poor road conditions, poor road maintenance and congestion. The dire impacts of these challenges could be extremely damaging to both human lives and the economies of the countries involved. Intelligent Transportation Systems (ITSs) integrate modern technologies into existing transportation systems to monitor traffic. Adopting Vehicular Adhoc Network (VANET) into the road transport system is one of the most ITS developments demonstrating its benefits in reducing incidents, traffic congestion, fuel consumption, waiting times and pollution. However, this type of network is vulnerable to many problems that can affect the availability of services. This article uses a Fuzzy Bayesian approach that combines Bayesian Networks (BN) and Fuzzy Logic (FL) for predicting the risks affecting the quality of service in VANET. The implementation of this model can be used for different types of predictions in the networking field and other research areas.

AI-Based Efficient Wireless Technologies and Infrastructure-Based Networks With VANET For Smart Transportation High Performance

“…Real-time detection was extremely difficult to guarantee in this instance. In [15], a data density-based technique was proposed for this issue. In this method, instead of using a threshold like other methods do, the data density is calculated recursively to find anomalies through the distribution of metrics.…”