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21 pages, 4513 KiB

Open AccessArticle

An Enhanced ZigBee-Based Indoor Localization Method Using Multi-Stage RSSI Filtering and LQI-Aware MLE

by Jianming Li, Shuyan Yu, Zhe Wei and Zhanpeng Zhou

Sensors 2025, 25(9), 2947; https://doi.org/10.3390/s25092947 - 7 May 2025

Cited by 1 | Viewed by 641

Accurate indoor localization in wireless sensor networks remains a non-trivial challenge, particularly in complex environments characterized by signal variability and multipath propagation. This study presents a ZigBee-based localization approach that integrates multi-stage preprocessing of received signal strength indicator (RSSI) data with a reliability-aware extension of the maximum likelihood estimation (MLE) algorithm. To improve measurement stability, a hybrid filtering framework combining Kalman filtering, Dixon’s Q test, Gaussian smoothing, and mean averaging is applied to reduce the influence of noise and outliers. Building on the filtered data, the proposed method introduces a noise and link quality indicator (LQI)-based dynamic weighting mechanism that adjusts the contribution of each distance estimate during localization. The approach was evaluated under simulated and semi-physical non-line-of-sight (NLOS) indoor conditions designed to reflect practical deployment scenarios. While based on a limited set of representative test points, the method yielded improved positioning consistency and achieved an average accuracy gain of 11.7% over conventional MLE in the tested environments. These results suggest that the proposed method may offer a feasible solution for resource-constrained localization applications requiring robustness to signal degradation. Full article

(This article belongs to the Special Issue Sensors and Techniques for Indoor Positioning and Localization: 2nd Edition)

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16 pages, 5520 KiB

Open AccessArticle

A Combined Filtering Method for ZigBee Indoor Distance Measurement

by Zhe Wei and Zhanpeng Zhou

Sensors 2024, 24(10), 3164; https://doi.org/10.3390/s24103164 - 16 May 2024

Cited by 5 | Viewed by 1554

Abstract

Indoor distance measurement technology utilizing Zigbee’s Received Signal Strength Indication (RSSI) offers cost-effective and energy-efficient advantages, making it widely adopted for indoor distance measurement applications. However, challenges such as multipath effects, signal attenuation, and signal blockage often degrade the accuracy of distance measurements. Addressing these issues, this study proposes a combined filtering approach integrating Kalman filtering, Dixon’s Q-test, Gaussian filtering, and mean filtering. Initially, the method evaluates Zigbee’s transmission power, channel, and other parameters, analyzing their impact on RSSI values. Subsequently, it fits a signal propagation loss model based on actual measured data to understand the filtering algorithm’s effect on distance measurement error. Experimental results demonstrate that the proposed method effectively improves the conversion relationship between RSSI and distance. The average distance measurement error, approximately 0.46 m, substantially outperforms errors derived from raw RSSI data. Consequently, this method offers enhanced distance measurement accuracy, making it particularly suitable for indoor positioning applications. Full article

(This article belongs to the Special Issue Indoor Positioning Technologies for Internet-of-Things)

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12 pages, 424 KiB

Open AccessArticle

IoT-Based Small Scale Anomaly Detection Using Dixon’s Q Test for e-Health Data

by Partha Pratim Ray and Dinesh Dash

Appl. Syst. Innov. 2021, 4(4), 100; https://doi.org/10.3390/asi4040100 - 16 Dec 2021

Cited by 1 | Viewed by 2635

Abstract

Anomaly detection in the smart application domain can significantly improve the quality of data processing, especially when the size of a dataset is too small. Internet of Things (IoT) enables the development of numerous applications where sensor-data-aware anomalies can affect the decision making of the underlying system. In this paper, we propose a scheme: IoTDixon, which works on the Dixon’s Q test to identify point anomalies from a simulated normally distributed dataset. The proposed technique involves Q statistics, Kolmogorov–Smirnov test, and partitioning of a given dataset into a specific data packet. The proposed techniques use Q-test to detect point anomalies. We find that value 76.37 is statistically significant where

P = 0.012 < α = 0.05

, thus rejecting the null hypothesis for a test data packet. In other data packets, no such significance is observed; thus, no outlier is statistically detected. The proposed approach of IoTDixon can help to improve small-scale point anomaly detection for a small-size dataset as shown in the conducted experiments. Full article

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17 pages, 3203 KiB

Open AccessArticle

An Efficient Document Skew Detection Method Using Probability Model and Q Test

by Kai Huang, Zixuan Chen, Min Yu, Xiaolang Yan and Aiguo Yin

Electronics 2020, 9(1), 55; https://doi.org/10.3390/electronics9010055 - 30 Dec 2019

Cited by 10 | Viewed by 5716

Abstract

Document skew detection is one of the key technologies in most of the document analysis systems. However, existing skew detection methods either have low accuracy or require a large amount of computation. To achieve a good tradeoff between efficiency and performance, we propose a novel skew detection approach based on bounding boxes, probability model, and Dixon’s Q test. Firstly, bounding boxes are used to pick out the eligible connected components (ECC). Then, we calculate the slopes of the skew document with the probability model. Finally, we find the optimal result with Dixon’s Q test and projection profile method. Moreover, the proposed method can detect the skew angle in a wider range. The experimental results show that our skew detection algorithm can achieve high speed and accuracy simultaneously compared with existing algorithms. Full article

(This article belongs to the Section Computer Science & Engineering)

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