# WSN Nodes Placement Optimization Based on a Weighted Centroid Artificial Fish Swarm Algorithm
## 1 Introduction  
Blade is one of the key components of the wind turbine. It is responsible for converting wind energy into rotational mechanical energy of wind turbine rotors. The wind turbine blade is extremely easy to suffer varying degrees of damage under the influence of environment of wind, sand, ice, thunder and lightning, as well as a variety of stress concentrations, leading to the shutdown of the wind turbine, and serious economic losses. The volume and mass of wind turbine blade are huge, so it is difficult to maintain manually, with high maintenance costs. Therefore, in order to decrease the risk of operation, while reducing maintenance costs, it is of great significance to carry out health monitoring in the wind turbine blade structure, especially identify the damage state quickly and accurately [1; 2].  
In this work, modal test is made for the health monitoring of blades. Attach a certain number of sensors on the blades to extract modal parameters. Due to the relative motion between the blades and cabin, it is inconvenient to transmit data through wired mode. At the same time, owing to the large number of sensors and complex wire arrangement, it is very convenient to use wireless sensors to transmit the real-time information about blade's health state to receiver.  
Wireless sensor networks (WSNs) [3] are usually deployed in complex monitoring areas where the environment is harsh and is inaccessible to people. Deployment of WSNs can enlarge the transmission distance and make the system not easily disturbed by the environment. The wireless sensor network adopts an ad hoc network and multi-hop technology, which greatly reduces the cost of the monitoring system, and makes the installation and maintenance of the sensor network very convenient and fast. By changing the topology of the network in WSNs dynamically, energy consumption is reduced, network capacity and life are increased and collisions between nodes are avoided [4; 5]. The topology control of WSNs is divided into the network topology establishment stage and the network topologymaintenance stage. To ensure that the complex topology can operate, as well as the simple topology built initially, a reasonable topology control algorithm is needed [6]. WSN technology has attracted more and more attention in recent years [7; 8].  
By means of WSNs, we can collect, process, and analyze the data reflecting the blade's health state. However, there are few studies on the optimal placement of WSNs for wind turbine blades monitoring. The artificial fish swarm algorithm (AFSA) is a new kind of swarm intelligence optimization algorithm based on the behavior of fish [9; 10]. The basic idea of AFSA is to imitate the fish behaviors such as prey, swarm, and follow. AFSA is simple in principle and has some good features such as robustness, and tolerance of parameter setting. This algorithm has some disadvantages yet. Non-uniform distribution deduced by randomly generating initial fish population decreases the global search ability of the algorithm. The search step of each fish is fixed. At the early iteration, the algorithm is easy to fall into local minimum.  
To solve the above problems, a weighted centroid algorithm (WCA) [11] is introduced to the artificial fish swarm algorithm (AFSA) to initialize the fish swarm in this work. By measuring the distance between nodes, localization compensation error and adaptive search step, the convergence speed and optimization precision are improved.