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.

Ship collision avoidance decision-making research in coastal waters considering uncertainty of target ships

“…Zhen et al [8] improved the Hausdorff distance and used the k-medoids algorithm to cluster ship trajectories, taking into account the heading information of trajectory points. Bai et al [9] proposed a ship route design method based on the K-means and ant colony algorithm, which uses the K-means algorithm to cluster routing in underwater.…”

Evaluation and Analysis Model of Economic Development Level for Latin American Countries

“…Now we will analyze the spatial distribution characteristics of high-quality development levels in Latin American countries, that is, the relationship between the relative position of each country and the level of development. In this paper, the horizontal position coordinates, vertical position coordinates, and comprehensive index of each country are used as the three characteristics of the cluster analysis datasets for K-Means cluster analysis [17][18][19][20][21][22][23]. At the same time, the K value needs to be selected.…”

EP-ADTA: Edge Prediction-Based Adaptive Data Transfer Algorithm for Underwater Wireless Sensor Networks (UWSNs)

“…They have adopted the methods of channel awareness [ 5 , 6 ], energy consumption awareness [ 7 , 8 , 9 , 10 ], and delay awareness [ 5 , 11 ] to better select transmission channels. Layered [ 9 , 12 ] or clustering [ 12 , 13 , 14 , 15 ] methods have been adopted to divide the underwater network, balance the energy consumption, and extend the network life. Previous studies have endowed the transmission routing with intelligence, so that it can adapt to the changes in the transmission environment; optimize the transmission quality; bypass network holes [ 16 , 17 ]; and realize efficient, reliable, and stable data transmission.…”

“…Through cooperation and communication, intelligent agents can effectively improve the performance of underwater wireless network communication [ 31 , 32 ]. Mobile agents have been used to enhance the network scalability [ 12 ]. Multiple agents have been used to optimize routing selection [ 28 , 29 ] and to improve the rationality of resource allocation [ 30 , 31 ].…”