Modeling and improving the throughput of vehicular networks using cache enabled RSUs

Newly emerged applications in vehicular networks demand high throughput to transfer large amount of data through both Vehicle-to-Vehicle and Vehicle-to-Infrastructure links. One solution which recently draws researchers attention to itself for improving the throughput in this type of network is to deploy some Road-Side-Units (RSUs) in the streets with storage capability and store the data closer to the end users. Consequently, vehicles are able to download their inquired contents from these local RSUs instead of the base station. This will decrease the network traffic of the base station and also the average delay each vehicle has to wait to receive his requested files. The main issue to implement this distributed approach in this type of environment compared to other types of networks is that the fast moving vehicles make the topology of the network highly dynamic. Also due to limited storage capacity of the caches in the RSUs, we should decide on how to distribute the contents in the RSUs to maximize the number of locally satisfied vehicles. In this paper, we address the cache content placement problem in vehicular networks and model it using a game theoretic approach. We show that the proposed game model is a special case of generalized covering games. Considering the hit ratio of the caches as the performance metric in our model, we propose a method to distributively optimize this metric using the RSU's local information. In addition, we propose a combinatorial approach to find efficient file placements in the RSUs using Markov approximation. Empirical evaluations on realistic trace-based simulations show an improvement of 7.5% in the average hit ratio of the proposed method compared to other well-known cache content placement approaches. Newly emerged applications in vehicular networks demand high throughput to transfer large amount of data through both Vehicle-to-Vehicle and Vehicle-to-Infrastructure links. To improve the network throughput, we deploy some Road-Side-Units (RSUs) in the streets with storage capability and store the data closer to the end users. Consequently, vehicles are able to download their inquired contents from these local RSUs instead of the base station. The main issue to implement this distributed approach is that the fast moving vehicles make the topology of the network highly dynamic. Also due to limited storage capacity of the caches in the RSUs, we should decide on how to distribute the contents in the RSUs to maximize the number of locally satisfied vehicles. In this paper, we address the cache content placement problem in vehicular networks and model it using game theoretic approach and Combinatorial approach. We show that the proposed game model is a special case of generalized covering games. Considering the hit ratio of the caches as the performance metric in our model, we propose a method to distributively optimize this metric using the RSU's local information. In addition, we propose a Combinatorial approach to find efficient file placements in the RSUs using Markov approximation. Empirical evaluations on realistic trace-based simulations show an improvement of 7.5% in the average hit ratio of the proposed method compared to other well-known cache content placement approaches.