Adaptive data collection protocol using reinforcement learning for VANETs

Data Collection is considered as an inherent challenging problem to Vehicular Ad-Hoc networks. Here, an Adaptive Data cOllection Protocol using rEinforcement Learning (ADOPEL) is proposed for VANETs. It is based on a distributed Qlearning technique making the collecting operation more reactive to nodes mobility and topology changes. A reward function is provided and defined to take into account the delay and the number of aggregatable packets. Simulations results confirm the efficiency of our technique compared to a non-learning version and demonstrate the trade-off achieved between delay and collection ratio.