An opportunistic data dissemination for autonomous vehicles communication

Abstract

Autonomous transportation is an inevitable means of day-to-day activity that supports a country’s economic growth. Effective communication systems design is an incentive for autonomous vehicles’ protection and comfort to be further extended. Inter-vehicle communication and effective ways for the user to communicate with the autonomous vehicle and the communication systems’ elements for autonomous vehicles need to be considered. Vehicular ad hoc network (VANET) is a prominent roadside communication-assisting technology designed to serve the purpose. Mitigating communication errors in transportation prevent service interruptions and ease information access. External and internal communication lags need to be addressed to sustain uninterrupted communication. To improve communication reliability, an opportunistic data dissemination model is introduced in this paper. This dissemination model observes storage and processing communication demands and time dependency to assist roadside communication. The observations are processed by a neural-network-based learning scheme to respond precisely to users’ requests by pre-estimating vehicular and communication storage and time requirements. The communicating autonomous transport system is notified of the time and storage dependency of the requests to improve data dissemination. The communication status is updated with the infrastructure-assisted connecting technologies to ensure better VANET performance. The experimental results achieve a high resource utilization ratio of 93.4%, throughput 8.3 Mbps, less response time of 288.53\xa0ms, request loss of 1.9%, and average delay of 60\xa0ms compared to other existing methods.