Geographical angular zone-based optimal resource allocation and efficient routing protocols for vehicular ad hoc networks

Vehicular ad hoc network (VANET) is an emerging trend where vehicles communicate with each other and possibly with a roadside unit. Collaboration among vehicles is significant in VANET. Resource constraint is one of the great challenges of VANETs. Owing to the absence of centralised management, there is pitfall in optimal resource allocation that leads ineffective routing. Effective reliable routing is quite essential to achieve intelligent transportation. Stochastic dynamic programming (SDP) is currently employed as a tool to analyse and solve network resource constraint and allocation issues of resources in VANET. The authors have considered this work as a geographic angular zone-based two-phase dynamic resource allocation problem with homogeneous and heterogeneous resource class. This work uses relaxed approximation-based SDP algorithm to generate optimal resource allocation strategies over time in response to past task completion status history. The second-phase resource allocation uses the observed outcome of the first-phase task completion to provide optimal viability decisions. They have also suggested an alternative solution called model predictive control algorithm (MPCA) that used approximation as a part to allocate resource over time in response to information on data transmission completion status. Simulation results show that the proposed schemes works significantly well for homogeneous resources.