Clustering, medium access control, and data aggregation in vehicular ad hoc networks

Résumé

Traffic congestion and traffic accidents are some of the serious problems faced by humans in their day to day life. These social problems causes a lot of human inconvenience, traffic congestion for instance reduces the productivity of humans by wasting a lot of time being on the road. To reduce the traffic congestion and road accidents, several traffic efficiency and safety applications can be implemented using the vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication. The network consisting of vehicles and road side units (RSUs) are known as vehicular ad hoc networks (VANETs). V2V and V2I communications exchange two different types of messages, periodic and event-driven messages. Periodic messages of a vehicle updates the nearby vehicles about its current position, speed and direction and event-driven messages are transmitted during a hard brake or detection of dangerous road condition. Most of the traffic safety and efficiency applications are based on the broadcasting of safety or update messages by vehicles or RSUs. This type of transmission is designed to help reduce road accidents and traffic congestions. However, during traffic accidents, there can be a huge number of vehicles in a certain region that can lead to congestion in the channel access. Furthermore, congestion in the channel also arises during the busy hours of the day. The reliability of the safety application depends upon the successful delivery of safety message to its neighbouring vehicles and RSUs with a predictable delay, which is one of the main functions of a deterministic medium access control (MAC) protocol proposed for VANETs. Another important challenge in VANETs is to achieve scalability. With the increasing size of the road system, the data load eventually congests the network channel. This challenge can be solved by reducing the data collisions and data load through clustering, MAC and data aggregation. The main aim of this thesis is to design a cluster based MAC protocol that is scalable in high density scenarios, transmit the safety message with high reliability, and deliver the safety message with a predictable delay. Moreover, reduce the channel load during traffic accidents using data aggregation application. This thesis makes the following research contributions: • A cluster head (CH) election, cluster formation, and cluster maintenance algorithm that increases the stability of the cluster structure and minimises overall management overhead for the maintenance of the algorithm. • A hybrid protocol based on contention free and contention based channel access. Time division multiple access (TDMA) slot allocation priority based on different parameters such as future position, and standard score. • A DA-CMAC protocol that allocates slots based on the direction of movement of vehicles. • A cluster based data aggregation scheme that aggregates data based on the density of vehicles. I will show that my CH election protocols, cluster maintenance and direction aware clustering protocols improves the stability of cluster architecture, there by reducing the maintenance overhead caused due to CH re-election, re-clustering, and re-configuration. The CH election protocols presented here leads to an hierarchical efficient network topology. Additionally, I will show that the proposed MAC protocols performs better than the current wireless access for vehicular environment (WAVE) standard in terms of predictability, reliability, and scalability. Moreover, the cluster based data aggregation is proposed to reduce the load in the channel. The protocols are evaluated using computer simulations that are developed in network simulator ns-2, and the network simulator ns-3. Moreover, the performance of the proposed protocols are compared with the WAVE standard, SBCA and the HCA protocol. Furthermore, the ability of the proposed protocols are demonstrated by detailed delivery delay analysis, including percentage of access collisions, cluster and CH lifetime, precision, and level of aggregation in different densities.