In this paper, we propose an autonomous blockchain-based federated learning (BFL)
design for privacy-aware and efficient vehicular communication networking, where local on …

Since the traffic conditions change over time, machine learning models that predict traffic
flows must be updated continuously and efficiently in smart public transportation. Federated …

The rapid increase in the volume of data generated from connected devices in industrial
Internet of Things paradigm, opens up new possibilities for enhancing the quality of service …

The drastically increasing volume and the growing trend on the types of data have brought
in the possibility of realizing advanced applications such as enhanced driving safety, and …

In disaster areas, a large amount of data (eg, rescue commands, road damage, and rescue
experience) should be delivered among ground rescuing vehicles for safe driving and …

The Internet of Things (IoT) revitalizes the world with tremendous capabilities and potential
to be utilized in vehicular networks. The Smart Transport Infrastructure (STI) era depends …

The emerging advances in personal devices and privacy concerns have given the rise to the
concept of Federated Learning. Federated Learning proves its effectiveness and privacy …

Although vehicular global positioning system (GPS) has been widely applied in many traffic
scenarios, it is far from achieving lane-level positioning due to its low accuracy. Existing …

We propose an autonomous blockchain-based federated learning (BFL) design for privacy-
aware and efficient vehicular communication networking, where local on-vehicle machine …

As accurate and timely traffic flow information is extremely important for traffic management,
traffic flow prediction has become a vital component of intelligent transportation systems …