Federated hyper LSTM model for storage optimization and collision prediction in an intelligent IoVT

The Internet of Vehicles (IoV) supports the combination of various techno insights to provide safe and comfortable transportation. These vehicles can share information to facilitate the current status of the location that the vehicle is about to travel with. A collision occurs when the abundant information fails to reach the target IoV within a stipulated time limit. The term collision in an IoT environment is always annexed with storage since the sparse storage system could lead to loss of information. Thus, there is a pressing need for collision avoidance annotated with storage optimization for IoV technologies. In this article, we propose an innovative federated hyper-LSTM model that initially handles the storage environment by incorporating federated learners to optimize it. The collision is predicted simultaneously by the proposed hyper-LSTM model. The entire model is equipped with reinforcement learners to keep track of the current status of storage and collision, achieving a benchmark accuracy of 97% for the proposed model.