Data aggregation in VANETs a generalized framework for channel load adaptive schemes

Abstract

One of the main communication challenges in vehicle-to-x communication is scalability. With increasing number of communication nodes the wireless channel must not get congested especially if a large amount of sensor data has to be forwarded over multiple nodes to a data processing application. This challenge can be solved by reducing the data load through data aggregation. This work introduces a framework for data aggregation as a decentralized congestion control mechanism on the application layer. This framework can be used to flexibly design aggregation schemes that adaptively adjust the generated data load depending on the overall channel load. Three basic aggregation schemes with different complexity and resulting data precision were developed within this framework and they are discussed in this paper. Performance evaluations show that the aggregation schemes are able to adapt to given channel load thresholds within seconds and deliver optimal data quality even in traffic jam situations.