Parallel multi-objective metaheuristics for smart communications in vehicular networks

VANETs improve the safety and efficiency of the road traffic through powerful cooperative applications that gather and broadcast real-time road traffic information. Routing in VANETs is a critical issue in today's research due to the high speed of the nodes, rate of topology variability, and real-time restrictions of their applications. Hence, the research community is very active with hot topics, creating new VANET protocols and improving the existent ones (Lee et al. 2009). The Ad hoc On Demand Vector (AODV) routing proto-col (Perkins et al. 2003), which is optimized in this study, has been previously analyzed for use in vehicular environments. Some authors have proposed changes to its parameter configuration to gain huge improvements over its quality-of-service (QoS) in VANETs (Said and Nakamura 2014). The configuration parameters of AODV have a strongly non-linear relationship with each other and a complex influence on its final performance. In fact, they represent a mix of discrete plus continuous variables which makes it a hard challenge to find the "best" configuration in a real-world scenario. Thus, exact and enumerative methods are not applicable for solving the underlying optimization problem of finding the "best" AODV configuration, because they require critically long execution times to perform the search, and because we are far from having a traditional analytical equation. In this context, soft computing methods are a promising approach to find accurate QoS-efficient AODV configurations in rea-sonable times.