Goodput Enhancement of VANETs in Noisy CSMA/CA Channels

The growing interest in vehicular ad hoc networks (VANETs) enables decentralized traveler information systems to become more feasible and effective in Intelligent Transportation Systems (ITS). Major challenges in such network environments include varying path characteristics and vulnerable channel quality resulting from dynamic traffic conditions and the design of the road. This paper demonstrates a feasible methodology that can enhance inter-vehicle information dissemination using dynamic optimal fragmentation with rate adaptation algorithm (DORA). DORA achieves maximum goodput in wireless mobile networks by computing a fragmentation threshold and transmitting optimal sized packets with maximum transfer rates. To estimate the SNR in the model, an adaptive on-demand UDP estimator is designed to reduce estimation overhead. Several test-beds were developed to evaluate the performance of DORA in channel estimation accuracy, ad hoc network goodput, and vehicle-to-vehicle network goodput along I-85 in Atlanta, Georgia. The proposed algorithm is an energy-efficient, generic CSMA/CA MAC protocol for wireless mobile computing applications, and enhances system goodput in ad hoc networks and vehicle-to-vehicle networks without modification of the base protocols.