@inproceedings{8b06b4e72e5941d6830b133219810ef7,
title = "Adaptive data collection protocol using reinforcement learning for VANETs",
abstract = "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.",
keywords = "Collection ratio, Data collection, Number of hops, Qlearning, Reinforcement learning, Vehicular Ad Hoc Networks (VANETs)",
author = "Ahmed Soua and Hossam Afifi",
year = "2013",
month = sep,
day = "16",
doi = "10.1109/IWCMC.2013.6583700",
language = "English",
isbn = "9781467324793",
series = "2013 9th International Wireless Communications and Mobile Computing Conference, IWCMC 2013",
pages = "1040--1045",
booktitle = "2013 9th International Wireless Communications and Mobile Computing Conference, IWCMC 2013",
note = "2013 9th International Wireless Communications and Mobile Computing Conference, IWCMC 2013 ; Conference date: 01-07-2013 Through 05-07-2013",
}