Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks

Vehicular Ad-Hoc Networks (VANETs) are a fast growing technology that many governments and automobile manufacturers are investing in to provide not only safer and more secure roads, but also informational and entertainment-based applications for drivers. The applications developed for VANETs can be classified into multiple categories (safety, informational, entertainment). Most VANET applications, regardless of their category, depend on having certain vehicular data (vehicular speed, X position and Y position) available. Although these applications appear to use the same vehicular data, the characteristics of this data (i.e., amount, accuracy, and update rate) will vary based on the application category. For example, safety applications need an accurate version of the vehicular data with high frequency, but over short distances. Informational applications relax the data frequency constraint as they need the vehicular data to be reasonably accurate with less frequency, but over longer distances. If each of these applications shares the vehicular data with only its peers using its own mechanism, this behavior will not only introduce redundant functionalities (sending, receiving, processing, etc.) for handling the same data, but also wastefully consume the bandwidth by broadcasting the same data multiple times. Despite the differences in the data characteristics needed by each application, this data can be still shared. Vehicular networks introduce the potential for many co-existing applications. If we do not address the problem of data redundancy early, it may hinder the deployment and usefulness of many of these applications. Therefore, we developed a framework, cluster-based accurate syntactic compression of aggregated data in VANETs (CASCADE), for efficiently aggregating and disseminating commonly-used vehicular data. CASCADE is architccted as a layer that provides applications with a customized version of the vehicular data, based on parameters that each application registers with CASCADE. Additionally, the framework performs the common data handling functionalities (sending, receiving, aggregating, etc.) needed by the applications. This dissertation makes the following contributions: (1) a lossless data compression technique based on differential coding that is tailored for the characteristics of vehicular data; (2) a syntactic data aggregation mechanism that can represent the vehicular data in a 1.5 km area in one IEEE 802.11 frame; (3) a light-weight position verification technique that quickly detects false data with very low false positives; (4) a probabilistic data dissemination technique that alleviates the spatial broadcast storm problem and effectively uses the bandwidth to disseminate data to distant areas in a short amount of time in addition to having less redundancy and more coverage than other techniques. (5) a mechanism for recovering from the communication discontinuity problem in short time based on the traffic density in the opposite direction; (6) an investigation of the possible data structures for representing the vehicular data in a searchable format; (7) a parametric mechanism for matching the vehicular data and providing a customized version of the data that satisfies certain characteristics based on the parameter value. CASCADE through its four major components, local view, extended view, data security and data dissemination, provides an efficient solution for the problem of scalability for VANET applications.