Data Collection, Dissemination, and Security in Vehicular Ad Hoc Network
With fast-decreasing cost of electronic devices and the increasing use of mobile phones, vehicular ad hoc networks (VANETs) are emerging as a popular form of mobile ad hoc networks. VANETs are useful for supporting many applications that improve road safety and enhance driving convenience. Moreover, they also provide real-time data for traffic and travel management.
A VANET is composed of fast-moving mobile nodes (vehicles) that have intermittent and short contacts, fixed road-side units (RSUs) that overhear and broadcast to vehicles, and a central server. Vehicles move along roads, collect data and process them, and disseminate the data to other vehicles and RSUs. The central server aggregates data collected by vehicles, overviews traffic and road status, and generates keys and certificates when necessary. RSUs overhear the data sent from vehicles, broadcast road-side information to vehicles, and communicate to the central server via backhaul network.
With smartphones equipped on vehicles, many interesting research topics emerge, such as traffic-congestion detection and road-bump detection. After data are collected and processed, they are disseminated, such that other vehicles can collaboratively sense the road and traffic status. This motivates the need for data dissemination algorithms in a VANET. Due to the limited bandwidth and insufficient coverage of 3G/4G networks, direct peer-to-peer communication between nodes is important.
Other major concerns in a VANET are security and privacy, since a malicious user can track vehicles, report false alarms, create undesirable traffic congestion, and illegally track vehicles. It is important to ensure the authenticity of messages propagated within VANETs, while protecting the identity and location privacy of vehicles that send messages. This thesis addresses data collection, data processing, dissemination, and security.
First, we estimate the location of vehicles in the scenario of weak/faded GPS signals by using the built-in sensors on smartphones. This method combines landmark recognition and Markov-chain predictions to localize a vehicle. Compared to the coarse-grained localization embedded in an Android smartphone using cellular and wifi signals, this method significantly improves accuracy.
For data dissemination, we observe habitual mobility as well as deviations from habits, characterize their impact on latency, and exploit them through the Diverse Routing (DR) algorithm.
Comparing to existing protocols, we show that DR leads to the least packet delay, especially when users deviate from expected behavior.
An important challenge for secure information dissemination in a VANET lies in Sybil attacks, where a single vehicle fakes multiple identities. We propose the Privacy-perserving Detection of Sybil Attack Protocl (P<super>2</super>DAP) scheme to detect such Sybil attacks. The P<super>2</super>DAP method does not require any vehicle in the network to disclose its identity, hence privacy is preserved at all times. Our results also quantify the inherent trade-off between security, i.e., the detection of Sybil attacks and detection latency, and the privacy provided to the vehicles in the network.
Due to the dependency of P<super>2</super>DAP on RSUs, and the fact that RSUs are usually semi-trusted in VANETs, we need an additional protection mechanism for RSUs. This observation motivates the Predistribution and Local-Collaboration-Based Authentication (PLCA) scheme, which combines Shamir secret sharing with neighborhood collaboration. In PLCA, the cost of changing the infrastructure is relatively low, and any compromise of RSUs can be quickly detected.
In summary, this thesis addresses problems that are relevant to various stages of the life-cycle of information in a VANET. The proposed solution handle data collection, data processing and information extraction, data dissemination, and security/privacy issues. Together, these adverses contribute to a secure and efficient environment for VANET, such that better driving experience and safety can be achieved.
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
Rights for Collection: Duke Dissertations