Low Energy Adaptive Connectionist Clustering for Wireless Sensor Networks

Lahcène Dehni (1) , Younès Bennani (1) , Francine Krief (2)

1 LIPN - Laboratoire d'Informatique de Paris-Nord
2 LaBRI - Laboratoire Bordelais de Recherche en Informatique

The use of the wireless sensor networks (WSNs) should be increasing in different fields (scientist, logistic, military and health, etc.). However, the sensor's size is an important limitation in term of energetic autonomy, and thus of life span because battery must be very small. This is the reason why, today, research mainly carries on the energy management in the WSNs, taking into account communications, essentially. In this context, we propose an adaptive routing algorithm based on the clustering that we named LEA2C. This algorithm relies on connectionist learning techniques and more exactly on the topological selforganizing maps (SOMs). New rules for the choice of the clusterheads have also been added. By comparing the results obtained by our protocol with those of other clustering methods used in the WSNs, such as LEACH and LEACH-C, we obtain important gains in term of energy and thus of network lifetime.

Mots clés: self-organizing maps of Kohonen; Wireless sensor networks; clustering; energy consumption; routing; numerical learning; self-organizing maps of Kohonen.

Domaines: Apprentissage [cs.LG]

Lahcène Dehni, Younès Bennani, Francine Krief. Low Energy Adaptive Connectionist Clustering for Wireless Sensor Networks. IEEE/IFIP MATA 2005 : 2nd International Workshop on Mobility Aware Technologies and Applications, 2005, Montréal, Canada. pp.405-415. ⟨hal-00084645⟩