Anchor node placement with optimization methods for localization of nodes in large-scale mobile wireless sensor networks

Localization is an important process in Wireless Sensor Networks (WSNs) for various usage areas such as target tracking and object tracking. Anchor nodes play a critical role in this task since they can find their location via GPS signals or manual setup mechanisms and help other nodes in the network determine their locations. Therefore, optimal placement of anchor nodes in a WSN is particularly important to reduce energy consumption while providing more precise accuracy in locating nodes. In this thesis, a new approach is proposed to find the optimal number of anchor nodes and the optimal placement strategy in a large-scale WSN, based on Gray Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) methods. As the first step of this approach, the virtual localization process is provided over a virtual coordinate system to optimize the efficiency of the process. GWO and PSO methods are compared with machine learning approaches such as a coverage-based analytical method, Support Vector Machine (SVM) regression, and Multiple Regression. In addition, it was compared with the DV-HoP method in the localization aspect. The simulations we run in a WSN with different numbers of nodes and different maximum coverage distances show that the proposed approaches are superior in terms of minimizing localization errors while reducing the number of anchor nodes.