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The present master thesis is focused on the design and evaluation of a cluster-based aggregation protocol (CBAP), which defines a set of rules and procedures for data aggregation based on a cluster structure. The proposed protocol is regarded as a complex mechanism consisting of two component sub-protocols: a clustering algorithm, grouping vehicles into cluster entities, and an aggregation scheme, deploying in-network and hierarchical data aggregation atop the prebuilt clusters. A cluster-based aggregation, as a concept, introduces a novel approach to the VANET technology, since it is more specific to other research areas, such as Wireless Sensor Networks (WSNs), where clustering mechanisms have been successfully applied to solve both scalability and data quality issues. In this respect, clustering helps scalability, because nodes exchange and aggregate information within the cluster, and disseminate further only the merged results, thus saving bandwidth resources. Last but not least, we introduce in this work a novel and very interesting approach to hierarchical aggregation, which copes efficiently with data duplicates and multiple transmissions of similar data structures. This was particularly possible due to the application of Bloom filters, that have not been used for such purposes before. The introduction of a Time-To-Live parameter improves further the proposed aggregation scheme.