Smart scheduling can be used to reduce infrastructure-to-vehicle energy costs in delay tolerant vehicular networks (Hammad et al., 2010).. In this thesis we show that by combining this with vehicle-to-vehicle (V2V) forwarding, energy efficiency can be increased beyond that possible in the single hop case. This is accomplished by having the roadside infrastructure forward packets through vehicles which are in energy favourable locations. We first derive offline bounds on the downlink energy usage for a given input sample function when V2V forwarding is used. Separate bounds are given for the off-channel and in-channel forwarding cases. These bounds are used for comparisons with a variety of proposed online scheduling algorithms. The paper then introduces online algorithms for both fixed bit rate and variable bit rate air interface options. The first algorithm is based on a greedy local optimization (GLOA). A version of this algorithm which uses a minimum cost flow graph scheduler is also introduced. A more sophisticated algorithm is then proposed which is based on a finite window group optimization (FWGO). Versions of these algorithms are also proposed which use in-channel vehicle-to-vehicle scheduling. The proposed algorithms are also adapted to the variable bit rate air interface case. Results from a variety of experiments show that the proposed scheduling algorithms can significantly improve the downlink energy requirements of the roadside unit compared to the case where vehicle-to-vehicle packet forwarding is not used. The performance improvements are especially strong under heavy loading conditions and when the variation in vehicle communication requirements or vehicle speed is high.

Master of Applied Science (MASc)