Abstract
In this work concepts of division of labor in social insects and emergent self-organization are used to design a very efficient heuristic for clustering wireless sensor networks. Differently from previous approaches, we aim at creating clusters with a minimum amount of resources and good intra-cluster connectivity. Our heuristic has two steps. First, we elect the most suitable clusterheads that have the extra responsibility of leading and representing the cluster. Afterwards, the heuristic selects the respective members of the clusters. These processes are guided by a response function that determines the suitability of each node to a given task (role). For example, nodes with good connectivity and high energy level are good candidates for being clusterheads. In addition to the division of labor, we are using a positive/negative feedback mechanism to control the stimulus for attracting new members. Until having enough resources, the positive feedback acts in order to recruit new members. After gathering enough resources, the negative feedback starts to play a major role. Simulations showed that for 80% of cases the proposed heuristic could find results which are below 2.3 times the theoretical optimal solution, define as the sum of the intracommunication cost of the clusters.
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Heimfarth, T., Orfanus, D., Wagner, F.R. (2008). Resource-Aware Clustering of Wireless Sensor Networks Based on Division of Labor in Social Insects. In: Hinchey, M., Pagnoni, A., Rammig, F.J., Schmeck, H. (eds) Biologically-Inspired Collaborative Computing. BICC 2008. IFIP – The International Federation for Information Processing, vol 268. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09655-1_5
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DOI: https://doi.org/10.1007/978-0-387-09655-1_5
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