Wireless sensor networks have inherent characteristics that differ from other wireless networks. Therefore, topology configuration and routing methods in WSNs must address these characteristics. In this paper, we propose an energy efficient clustering model. This model was inspired by the behaviors and capabilities of the six-spotted fishing spider, Dolomedes triton. The suggested model performs cluster-heads selection and clustering in self-organized ways. In order to determine the cluster-heads and the cluster-members, each sensor node uses the local information and simple rules that have been inspired by the Dolomedes triton. We compared our model with a well-known cluster-based routing protocol that uses random fairness for the selection of sensor node cluster-heads. In our computational experiments, we have showed that the energy efficiency and lifetimes of our bio-inspired model exceeds those of the comparison protocol by only using simple bio-inspired mechanism. We also demonstrate our model’s good performance in terms of scalability, which is one of the important indicators of performance for self-organized wireless sensor networks.
Special issue on machine learning applications for self-organized wireless sensor networks
