Research Article
Designing Smart Adaptive Flooding in MANET Using Evolutionary Algorithm
@INPROCEEDINGS{10.1007/978-3-642-30607-5_7, author={Wahabou Abdou and Christelle Bloch and Damien Charlet and Dominique Dhoutaut and Fran\`{e}ois Spies}, title={Designing Smart Adaptive Flooding in MANET Using Evolutionary Algorithm}, proceedings={Mobile Wireless Middleware, Operating Systems, and Applications. 4th International ICST Conference, Mobilware 2011, London, UK, June 22-24, 2011, Revised Selected Papers}, proceedings_a={MOBILWARE}, year={2012}, month={5}, keywords={MANET VANET Flooding Broadcast Storm Problem Evolutionary Algorithm}, doi={10.1007/978-3-642-30607-5_7} }- Wahabou Abdou
Christelle Bloch
Damien Charlet
Dominique Dhoutaut
François Spies
Year: 2012
Designing Smart Adaptive Flooding in MANET Using Evolutionary Algorithm
MOBILWARE
Springer
DOI: 10.1007/978-3-642-30607-5_7
Abstract
This paper deals with broadcasting warning / emergency messages in mobile ad hoc networks. Traditional broadcasting schemes tend to focus on usually high and homogeneous neighborhood densities environments. This paper presents a broadcasting protocol that locally and dynamically adapts its strategy to the neighborhood densities. The behavior of the protocol is tuned using various internal parameters. Multiple combinations of those parameters have been pre-computed as optimal solutions for a range of neighborhood densities, and the most relevant one is dynamically chosen depending on the locally perceived environment. The combinations were determined by coupling an evolutionary algorithm and a network simulator, using a statistically realistic radio-propagation model (Shadowing Pattern). This approach is compared with other probabilistic methods while broadcasting an emergency message in vehicular ad hoc networks with variable and heterogeneous vehicle densities. In such a context, it is expected from the network to enable each node to receive the warning message. The results show that our protocol covers the whole network, whereas other methods only have a probability of 0.57 to 0.9 to cover the entire network.