An Efficient Indoor Positioning Method with the External Distance Variation for Wireless Networks

Ching-Mu Chen,Yung-Fa Huang,You-Ting Jheng

doi:10.3390/electronics10161949

Ching-Mu Chen, Yung-Fa Huang + Show 1 more

Open Access

https://doi.org/10.3390/electronics10161949

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Abstract

This study strengthens the external distance variation for the indoor positioning performance. With the received signal strength (RSS) of the unknown node, a localization is performed to positioning its coordinates. The mean square error (MSE) of localization is deteriorated by the shadowing effect and the MSE depends on the location of reference nodes. Moreover, the minimum mean square error (MMSE) algorithm is also used with the RSS. The amount of variation in the distance between the reference point and the positioning node will also affect the accuracy. Therefore, this paper considers the distance between the reference point and the positioning node and also the distance variation between the reference points. MSE is used to estimate positioning performance and Monte Carlo is also used to simulate the average error of different shadowing and decay environments. When reference nodes have known distances, the distance is obtained separately and the estimated distances are identified by the MMSE method. In order to reduce the number of reference nodes and calculation cost, this paper uses adaptive reference node selection to improve the accuracy of positioning. Simulation results show that the external distance variation mechanism strengthens the indoor positioning performance. Moreover, this paper investigates the performance of several reference nodes (three, four, five, and six reference nodes) through 3D graphs to estimate the small range area. The differences are more clearly observed with fewer reference nodes and lower MSE. Finally, simulation results show that the MSE value of fixed three reference nodes is almost 100% better with external distance variation method compared to the random selected three reference nodes.

Highlights

In recent years, wireless networks are developed quickly and have been applied to many places such as hot spots, personal digital devices, and so forth [1,2]
Since the external variation is selected, unsuitable nodes may be selected to increase the probability of mean square error (MSE)
This paper proposes the signal strength to estimate the distance and the minimum mean square error (MMSE) algorithm to explore the effectiveness of indoor positioning

Summary

Introduction

Wireless networks are developed quickly and have been applied to many places such as hot spots, personal digital devices, and so forth [1,2]. The influencing factor of the large-scale propagation model is the change caused by the long distance or time of the signal in the transmission process. Wireless telecommunication signals change very quickly in mobile communication, a very important factor affecting communication quality This phenomenon is unavoidable and the radio wave part is still on the bottleneck of communication performance in the overall mobile communication system. The third category is the fast fading referred to a situation where the signal presents a large change in a short period of time It may be affected by the movement of the receiving point or surrounding objects. Many papers have proposed quite a lot of channel models, which can be roughly divided into the following three parts: propagation path loss model, large scale propagation model, and small scale propagation model [22]

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