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Self-organization in ambient networks through molecular assembly

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An ambient network provides a homogeneous environment to the mobile node amidst the heterogeneity, which arises from the connections to various clusters over its lifespan. When a mobile node in an ambient network changes its location or communication pattern, these changes force the mobile node to join a new cluster. Therefore, we have extended the molecular self-assembly for the ambient network to search for the best set of clusters to seize all the nodes. An internal-view of a molecular system depicts all its molecules and their relationships as holding together due to the equilibrium between the attraction and repulsion forces among its molecules. Here we have analogized the nodes within the ambient network as molecules where these nodes are also governed by special-forces (relations) to configure a connected topology. In this paper, we have defined three forces, which are the physical distance, incoming traffic and outgoing traffic with respect to the pair-wise relations between the node-to-node (at micro-level of a cluster in an ambient network), to act as attraction and repulsion among nodes and forming clusters in a self-organized manner. The ambient network topology problem is formulated as an optimization problem to find suitable clusters of nodes with an objective to reduce the backbone traffic where a cluster assembles the strongly attracted nodes together with respect to all three forces. The simulation results show that our proposed molecular assembly (MA) algorithm embedded on each node coordinates the clustering and our algorithm leads in reducing the backbone traffic up to 20% under the influence of an individual force and up to 10% with the forces applied together when compared to our previous network redesign algorithm with genetic algorithm (GA), which offered reduction in backbone traffic up to 3% as an optimization tool. The robustness of the proposed algorithm is tested by varying the network sizes with 25 and 50 nodes and the convergence rate of MA, which is faster in comparison with GA.

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This work was supported by Kuwait University, Research Grant No. EO02/06.

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Correspondence to Sami. J. Habib.

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Habib, S.J., Marimuthu, P.N. Self-organization in ambient networks through molecular assembly. J Ambient Intell Human Comput 2, 165–173 (2011).

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