Resilient in-network aggregation for vehicular networks

Applications for vehicular ad hoc networks (VANETs) are an active field of research with the potential to significantly contribute to driver safety, traffic efficiency, and comfort. Messages are typically exchanged and forwarded between vehicles using wireless communication, thereby creating a wireless ad hoc network. Especially traffic efficiency applications require the dissemination of information over long distances. For instance, vehicles need to be informed about traffic jams early enough to consider alternative navigation decisions. Each vehicle acts as creator and as forwarder of information to implement the required multihop information dissemination. Two of the most prevalent challenges in designing suitable ad hoc communication protocols are dealing with the limited wireless channel capacity, as well as ensuring the resilience of communication protocols against potential attackers. The focus of this thesis is on the resilience of in-network information aggregation mechanisms for VANETs. In aggregation mechanisms, vehicles collaboratively exchange information and summarize this information as it is disseminated within the network. In contrast to traditional protocols, which often aggregate information at a centralized entity, the aggregation close to the information sources saves bandwidth and provides scalability. Yet, malicious users may be able to inject false information or even alter information summaries to disturb normal system operation. Both types of attacks are hard to detect, because original observations are usually discarded after aggregation and are not available to verify the correctness of claimed aggregated information. By addressing resilient in-network aggregation, this thesis provides solutions that contribute to both channel capacity conservation and protocol resilience. The main contributions of this thesis are (a) a model of the in-network aggregation dissemination process; (b) a detailed security analysis of in-network aggregation mechanisms including the introduction of a taxonomy for security paradigms; (c) the design of four novel security mechanisms for in-network aggregation and (d) their detailed analysis and evaluation using network simulations; and (e) a framework that combines and adapts secure aggregation mechanisms based on situational context, as well as on attack likelihood derived from information exchange. The model for in-network aggregation is comprised of an architecture model and an information flow model. It provides the foundation for understanding which components are essential in the design of aggregation mechanisms and for understanding how information spreads and evolves within the network. The taxonomy of security paradigms, which is based on the modeling results, identifies use of cryptographic tools, interaction between vehicles to facilitate collaborative agreement, and data-consistency checks as most suitable security paradigms to provide resilience for in-network aggregation mechanisms.