Artificial intelligence techniques in wireless sensor networks for accurate localization of user in floor, building and indoor area

Chadha, Jigyasa; Jain, Aarti; Kumar, Yogesh

doi:10.1007/s11042-022-12979-w

Cited by 7 publications

(1 citation statement)

References 32 publications

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“…Various localization methods have been proposed for use in WSN and IoT. Some of these methods include distance vector-hop (DV-Hop) [7], angle of arrival (AOA) [8], time-difference-of-arrival (TDOA) [9], artificial neural networks (ANN) [10], and deep learning [11].…”

Section: Introductionmentioning

confidence: 99%

Localization in a wireless sensor network with a geometric approach of Trilateration and mathematical modeling of sine and cosine functions

Ghadami

2024

Preprint

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Wireless sensor network (WSN) has several applications in agriculture, military, rescue, and environmental applications. In a WSN, a large number of sensor nodes are scattered in an operating environment and gather environmental information. The data collected by the WSN is sent to an application in the cloud computing layer or base station for analysis. Gathering information without having their localization and position is not of high value, and accurate localization and position determination of sensor nodes are required in various applications. One method for localization is to use a Global Positioning System (GPS) and install it on all sensor nodes. localization with GPS costs a lot, and on the other hand, this method consumes a lot of energy for localization. DV-Hop technique is a practical method for positioning by reducing the positioning cost. The challenge of the DV-Hop approach for localization is the significant error in locating sensor nodes without a GPS. In this article, a practical localization method is presented by combining the DV-Hop method and the geometric localization method of three references. In this study, the sensor nodes first estimate their distance from the three guide nodes with the step measurement algorithm and then locate them using the Trilateration method. In the proposed approach, the sine and cosine optimization algorithm (SCA) are applied to minimize the positioning error. The evaluations illustrate that the localization error of the suggested approach is lower than the DV-Hop approach. The proposed positioning algorithm has less positioning error than the whale optimization algorithm (WOA), the Harris Hawk Optimization (HHO) algorithm, and the Jellyfish Search optimizer (JSO). The evaluations show that the proposed method of advanced localization methods based on steps such as GSDV-Hop and MGDV-Hop has fewer errors in localization.

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