A K-Means and Ant Colony Optimization-Based Routing in Underwater Sensor Networks

Abstract

Reliable data transfer seems a quite challenging task in underwater sensor networks (UWSNs) in comparison with terrestrial wireless sensor networks due to the peculiar attributes of UWSN communication. Therefore, K-means and ant colony optimization-based routing (KACO) is proposed in this paper. In KACO, network area under water is divided into layers with regard to the depth level. And nodes of each layer are divided into clusters by the optimized K-means algorithm. The K-means algorithm is used to cluster nodes. Considering the shortcoming of K-means clustering, an improved K-means clustering is used to select the initial cluster center. In the stage of selecting cluster heads, the remaining energy of nodes and the distance from the sink node are used to calculate the competing factors of nodes, and then, the cluster heads are selected according to the competing factors. In the intercluster routing, the ant colony optimization (ACO) was improved by introducing the Gini coefficient, and the intercluster routing based on improved ACO is proposed. The simulation results show that the proposed KACO routing can effectively reduce the energy consumption of nodes and improve the efficiency of packet transmission.