Adaptive Forwarding Delay Control for VANET Data Aggregation
In-network data aggregation is a useful technique to reduce redundant data and to improve communication efficiency. Traditional data aggregation schemes for wireless sensor networks usually rely on a fixed routing structure to ensure data can be aggregated at certain sensor nodes. However, they cannot be applied in highly mobile vehicular environments. In this paper, we propose an adaptive forwarding delay control scheme, namely Catch-Up, which dynamically changes the forwarding speed of nearby reports so that they have a better chance to meet each other and be aggregated together. The Catch-Up scheme is designed based on a distributed learning algorithm. Each vehicle learns from local observations and chooses a delay based on learning results. The simulation results demonstrate that our scheme can efficiently reduce the number of redundant reports and achieve a good trade-off between delay and communication overhead.
data aggregation distributed learning vehicular networks