Abstract: In a mobile ad-hoc network (MANET) nodes are self-organized without any infrastructure support: they move arbitrarily causing the network to experience quick and random topology changes, have to act as routers as well as forwarding nodes, some of them do not communicate directly with each other. Routing and IP address auto-configuration are among the most challenging tasks in the MANET domain. Swarm Intelligence is a property of natural and artificial systems involving minimally skilled individuals that exhibit a collective intelligent behavior derived from the interaction with each other by means of the environment. Colonies of ants and bees are the most prominent examples of swarm intelligence systems. Flexibility, robustness, and self-organization make swarm intelligence a successful design paradigm for difficult combinatorial optimization problems, such as routing and IP address allocation in MANET. This paper proposes AutoBeeConf, a new IP address auto-configuration algorithm based on a bee swarm labor that may be applied to large scale MANET with low complexity, low communication overhead, even address distribution, and low latency. Both the protocol description and the simulation experiments are presented to demonstrate the advantages of AutoBeeConf over two known algorithms, namely Buddy and Antbased protocols. Eventually, future research directions are established, especially toward the principle that swarm intelligence paradigms may be usefully employed in the redefinition or modifications of each layer in the TCP/IP suite in such a way that it can efficiently work even in the infrastructure-less and dynamic nature of MANET environment.

Luca Caputo, Cristiano Davino, Filomena de Santis and Vincenzo Ferri, “AutoBeeConf : A swarm intelligence algorithm for MANET administration” International Journal of Advanced Research in Artificial Intelligence(IJARAI), 2(2), 2013. http://dx.doi.org/10.14569/IJARAI.2013.020209

@article{Caputo2013,
title = {AutoBeeConf : A swarm intelligence algorithm for MANET administration},
journal = {International Journal of Advanced Research in Artificial Intelligence}
doi = {10.14569/IJARAI.2013.020209},
url = {http://dx.doi.org/10.14569/IJARAI.2013.020209},
year = {2013},
publisher = {The Science and Information Organization},
volume = {2},
number = {2},
author = {Luca Caputo and Cristiano Davino and Filomena de Santis and Vincenzo Ferri},
}