Learning Automata based Cache Update Policy in Fog-enabled Vehicular Adhoc Networks

Abstract

Caching in Vehicular Adhoc Networks (VANETs) is a very important technique to reduce the transmission overhead and latency to improve the overall performance of the network. Increasing cache hit ratio is very important for delay sensitive applications. In this paper, average cache hit ratio maximization problem is identified and formulated while taking into account the time-varying topology of network, vehicular (user) mobility, varying requests and preferences of multiple users and the limited cache capacity of the Road Side Units (RSUs). A Learning Automata based cache update policy has been designed in order to determine the appropriate content to be cached in RSUs. The performance of the proposed learning automata based vehicular caching mechanism has been evaluated using simulations and analyzed in comparison with three other existing caching policies. Simulation results show that the efficacy of the proposed learning automata based caching approach can significantly improve the average cache hit ratio and reduce the latency in the vehicular ad-hoc network.