Search

Vehicular ad-hoc networks (VANETs) that enable communication among vehicles and between vehicles and unmanned aerial vehicles (UAVs) and cellular base stations have recently attracted significant interest from the research community, due to the wide range of practical applications they can facilitate (e.g., road safety, traffic management and rescue missions). Despite this increased research activity, the high vehicle mobility in a VANET raises concerns regarding the robustness and adaptiveness of such networks to support time-critical applications and internetworking. In this thesis, as a first step toward the design of efficient MAC protocol to support time-critical applications and internetworking, we show that it is indeed possible to follow the dynamics of a network and consequently adapt the transmission probability of the Aloha protocol to reduce the interference and maximise the single-hop throughput between adjacent nodes. Extensive simulation validates the proposed analytical model, which thus can serve as a promising tool to improve VANETs performance. By exploiting the parallel between the CSMA/CA and Aloha performance models, the optimal transmission probability for the Aloha protocol as a function of estimated vehicular density is derived. This probability is then used to obtain the optimal maximum CW that can be integrated in an amended CSMA/CA protocol to maximise the single-hop throughput among adjacent vehicles. We show by means of simulation that the beneficial impact the proposed protocol is increased channel throughput and reduced transmission delay when compared with the standardised protocol CSMA/CA in IEEE 802.11p. These results reveal the applicability of the new, optimised protocol to safety applications and clustering techniques with stringent performance requirements. Lastly, we propose a Stable Clustering Algorithm for vehicular ad-hoc networks (SCalE) internetworking. The exchange of the necessary status information to support the efficient clusters formation can firmly relay on the support of our optimised CSMA/CA protocol. The SCalE algorithm makes use of the knowledge of the vehicles behaviour (explained in Chapter 5) for efficient selection of CHs, and selects a backup CH on top of the CH to maintain the stability of cluster structures. The increased stability and improved performance of the SCalE algorithm is studied and compared with existing clustering algorithms.