Clustering by Swarm Intelligence in the Ad-Hoc Networks

Clustering by Swarm Intelligence in the Ad-Hoc Networks

Bakhta Meroufel (Department of Computer Science, Unviersity of Oran, Oran, Algeria) and Ghalem Belalem (Department of Computer Science, Unviersity of Oran, Oran, Algeria)
Copyright: © 2014 |Pages: 13
DOI: 10.4018/ijaec.2014070101
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An Ad-hoc wireless is composed mainly of mobile hosts that communicate with each other without fixed infrastructure and no central administration. The main problems associated with these networks are the unpredictable mobility of hosts and a modest flow of communication. In this context, a major problem is partitioning the network in groups called clusters, giving a hierarchical organization. This work presents a deterministic self-stabilizing clustering algorithm for Ad-Hoc networks based on PSO (Particle Swarm Optimization). The proposed approach creates clusters and controls the nodes mobility to ensure more stability for the system. To increase the network life and reduce the answer time of user queries, it proposed also a replication strategy based on the non-similarity degree. The simulations show that the cooperative approaches (clustering, mobility and replication) minimize the energy consumption and increase the QoS of the system.
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Figure 1 shows a classification of clustering algorithms in Ad-Hoc networks. We interest rightly to the algorithms (Schema) based on PSO (Particle Swarm Optimization), since it constitutes an integral part of our algorithm (for the other clustering techniques, more details can be found in (Bentaleb et al. 2013)). The PSO based clustering is classified in three categories: PSO-C (Heinzelman & Chandrakasan, 2002), MST-PSO (Co et al. 2008) and PSO-clustering (Guru et al. 2005). We will explain each strategy in the next paragraphs.

Figure 1.

The algorithms of clustering in an ad-hoc networks (Kumar et al. 2011; Singh et al. 2013)


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