An Optimal Anchor Placement Method for Localization in Large-Scale Wireless Sensor Networks

ÇAVDAR T., GÜNAY F. B., Ebrahimpour N., Kakiz M. T.

INTELLIGENT AUTOMATION AND SOFT COMPUTING, vol.31, no.2, pp.1197-1222, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 31 Issue: 2
  • Publication Date: 2022
  • Doi Number: 10.32604/iasc.2022.020127
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, Computer & Applied Sciences
  • Page Numbers: pp.1197-1222
  • Keywords: Wireless sensor networks, localization, anchor node placement, grey wolf optimization, particle swarm optimization, ALGORITHM, DEPLOYMENT, SELECTION
  • Karadeniz Technical University Affiliated: Yes


Localization is an essential task in Wireless Sensor Networks (WSN) for various use cases such as target tracking and object monitoring. 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, the optimal placement of anchor nodes in a WSN is of particular interest for reducing the energy consumption while yielding better accuracy at finding locations of the nodes. In this paper, we propose a novel approach for finding the optimal number of anchor nodes and an optimal placement strategy of them in a large-scale WSN, based on the output of Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) methods. As an initial step in this approach, the virtual localization process is executed over a virtual coordinate system in order to optimize the efficiency of the localization process. GWO and PSO methods are compared with a coverage-based analytical method and machine learning approaches such as Support Vector Machine (SVM) regression and Multiple Regression. The simulations we run with different numbers of nodes in a WSN and different communication ranges of nodes demonstrate that the proposed approaches are superior for minimizing the localization errors while reducing the number of anchor nodes.