Wireless Sensor Network Localization Based on Static Anchor Node

Year : 2023 | Volume :01 | Issue : 01 | Page : 12-20
By

Siti Nur,

  1. Student, Department of Computer Engineering, Universitas Negeri, Surabaya, Indonesia

Abstract

Anchor-based localization is a crucial challenge in wireless sensor networks (WSNs), aiming to determine the positions of all sensors by leveraging a limited number of anchor nodes whose locations are known. In this paper, we propose a novel algorithm for anchor-based localization in a static network, which improves the localization accuracy while reducing the computational complexity compared to existing methods. Our algorithm uses a range-based approach to estimate the distances between the sensors and anchor nodes, which are then used to compute the sensor positions using a least-squares method. To reduce the effects of measurement noise and anchor node density, we also introduce a weighted least-squares approach, which assigns larger weights to the measurements with lower noise levels and higher anchor node densities. We evaluate our algorithm through simulations on various network topologies with different densities of sensors and anchor nodes. The results show that our algorithm achieves significantly better localization accuracy than existing methods while maintaining low computational complexity. Moreover, our algorithm is robust to noise and anchor node density variations, making it suitable for real-world applications.

Keywords: Sensor, Localization, Anchor, RSSI, Mobile network

[This article belongs to International Journal of Wireless Security and Networks (ijwsn)]

How to cite this article:
Siti Nur. Wireless Sensor Network Localization Based on Static Anchor Node. International Journal of Wireless Security and Networks. 2023; 01(01):12-20.
How to cite this URL:
Siti Nur. Wireless Sensor Network Localization Based on Static Anchor Node. International Journal of Wireless Security and Networks. 2023; 01(01):12-20. Available from: https://journals.stmjournals.com/ijwsn/article=2023/view=116431

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Regular Issue Subscription Review Article
Volume 01
Issue 01
Received July 3, 2023
Accepted July 7, 2023
Published August 23, 2023

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