Date of Award
Doctor of Philosophy (PhD)
College of Engineering and Computing
Michael J. Laszlo
Clustering serves a vital role in the operation of Vehicular Ad hoc Networks (VANETs) by continually grouping highly mobile vehicles into logical hierarchical structures. These moving clusters support Intelligent Transport Systems (ITS) applications and message routing by establishing a more stable global topology. Clustering increases scalability of the VANET by eliminating broadcast storms caused by packet flooding and facilitate multi-channel operation. Clustering techniques are partitioned in research into two categories: active and passive. Active techniques rely on periodic beacon messages from all vehicles containing location, velocity, and direction information. However, in areas of high vehicle density, congestion may occur on the long-range channel used for beacon messages limiting the scale of the VANET. Passive techniques use embedded information in the packet headers of existing traffic to perform clustering. In this method, vehicles not transmitting traffic may cause cluster heads to contain stale and malformed clusters. This dissertation presents a hybrid active/passive clustering technique, where the passive technique is used as a congestion control strategy for areas where congestion is detected in the network. In this case, cluster members halt their periodic beacon messages and utilize embedded position information in the header to update the cluster head of their position. This work demonstrated through simulation that the hybrid technique reduced/eliminated the delays caused by congestion in the modified Distributed Coordination Function (DCF) process, thus increasing the scalability of VANETs in urban environments. Packet loss and delays caused by the hidden terminal problem was limited to distant, non-clustered vehicles. This dissertation report presents a literature review, methodology, results, analysis, and conclusion.
Garrett Lee Moore. 2019. A Hybrid (Active-Passive) VANET Clustering Technique. Doctoral dissertation. Nova Southeastern University. Retrieved from NSUWorks, College of Engineering and Computing. (1077)