A Self-Adaptive AP Selection Algorithm Based on Multiobjective Optimization for Indoor WiFi Positioning

Zhang, Wei; Yu, Kegen; Wang, Weixi; Li, Xiaoming

doi:10.1109/jiot.2020.3011402

Cited by 49 publications

(18 citation statements)

References 49 publications

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“…Due to the ability to use real hardware and viewing past case studies, we have adopted experimental methods and propagation measurements rather than simulation study or analytical modeling to gather precise information. Many key network researchers have used propagation measurement techniques to produce accurate Wi-Fi throughput performances and data collection [11,13,21,33].…”

Section: Methodsmentioning

confidence: 99%

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Impact of People’s Movement on Wi-Fi Link Throughput in Indoor Propagation Environments: An Empirical Study

2021

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There has been tremendous growth in the deployment of Wi-Fi 802.11-based networks in recent years. Many researchers have been investigating the performance of the Wi-Fi 802.11-based networks by exploring factors such as signal interference, radio propagation environments, and wireless protocols. However, exploring the effect of people's movement on the Wi-Fi link throughout the performance is still a potential area yet to be explored. This paper investigates the impact of people's movement on Wi-Fi link throughput. This is achieved by setting up experimental scenarios by using a pair of wireless laptops to file share where there is human movement between the two nodes. Wi-Fi link throughput is measured in an obstructed office block, laboratory, library, and suburban residential home environments. The collected data from the experimental study show that the performance difference between fixed and random human movement had an overall average of 2.21 ± 0.07 Mbps. Empirical results show that the impact of people's movement (fixed and random people movements) on Wi-Fi link throughput is insignificant. The findings reported in this paper provide some insights into the effect of human movement on Wi-Fi throughputs that can help network planners for the deployment of next generation Wi-Fi systems.

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Section: Methodsmentioning

confidence: 99%

“…Zhang el al. [21] have proposed another interesting solution for the optimization of Access Point (AP) position for indoor environments. A multi-objective based optimization is conducted considering both connection quality and the strength of the signal.…”

Section: Adaptive Positioning Systemsmentioning

confidence: 99%

Impact of People’s Movement on Wi-Fi Link Throughput in Indoor Propagation Environments: An Empirical Study

2021

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“…However, due to the high instability of the signal strength received by the AP, it is a challenging problem to use Wi-Fi positioning technology to achieve high accuracy. Zhang et al [7] proposed an AP selection algorithm by multiobjective optimization to improve indoor Wi-Fi positioning accuracy. The adaptive AP selection algorithm can be easily applied to various real scenarios.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Perception and Positioning Technology of Internet of Things by $K$ -Nearest Neighbor Matching Algorithm

Zou

Xingrui

Zeren

2022

Wireless Communications and Mobile Computing

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To study the intelligent sensing and positioning technology of the Internet of Things (IoT) combined with the K -nearest neighbor algorithm, the K -nearest neighbor matching algorithm and optimization algorithm are introduced using the indoor Wi-Fi positioning technology. The study proposes weighting K -nearest neighbor (WKNN) by weighted Euclidean distance, adaptive weighted Euclidean distance K-nearest neighbor Wi-Fi localization algorithm, and optimal K -value Wi-Fi fingerprint localization algorithm. The experimental error is verified. The experimental results show that the lowest error of continuous acquisition of 3 s signal values in experimental environment A is 1.8815 m, which is 10.13% lower than the error of only acquiring 1 s for the same K -value. The lowest error of environment B scheme two can reach 1.8862, which is 7.06% lower than the error of the same K -value. The optimal K -value Wi-Fi fingerprint positioning algorithm by distance constraint has better positioning accuracy than other KNN positioning algorithms, and the positioning fluctuation is smaller. The average positioning error of the optimal K in environment A is 1.2987 m, which is 0.2797 m less than the average of the traditional positioning algorithm. In environment B, the average positioning error of the optimal K is 1.5353 m, which is 0.3253 m less than the average of the traditional positioning algorithm. Therefore, the optimal K -value Wi-Fi positioning algorithm proposed has better performance.

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“…One of the key features of merit of an IPS is its positioning accuracy, but it must also be robust, giving accurate positions when the users walk in various manners through their environment. Pedestrian IPS can be divided into three categories: (1) radio signal-based IPSs, e.g., using wireless technologies such as WiFi [1][2][3][4][5], ultra-wide band (UWB), [6][7][8] and Bluetooth low energy (BLE) [9,10]; (2) optical and acoustic sensor-based IPS, e.g., camera [11][12][13], parameters of the step counting and step length estimation methods. (3) After setting the parameters, the trajectory can be constructed.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Human Activity-Aided Hand-Held Smartphone-Based Pedestrian Dead Reckoning Positioning System

Poslad

et al. 2021

Remote Sensing

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Pedestrian dead reckoning (PDR), enabled by smartphones’ embedded inertial sensors, is widely applied as a type of indoor positioning system (IPS). However, traditional PDR faces two challenges to improve its accuracy: lack of robustness for different PDR-related human activities and positioning error accumulation over elapsed time. To cope with these issues, we propose a novel adaptive human activity-aided PDR (HAA-PDR) IPS that consists of two main parts, human activity recognition (HAR) and PDR optimization. (1) For HAR, eight different locomotion-related activities are divided into two classes: steady-heading activities (ascending/descending stairs, stationary, normal walking, stationary stepping, and lateral walking) and non-steady-heading activities (door opening and turning). A hierarchical combination of a support vector machine (SVM) and decision tree (DT) is used to recognize steady-heading activities. An autoencoder-based deep neural network (DNN) and a heading range-based method to recognize door opening and turning, respectively. The overall HAR accuracy is over 98.44%. (2) For optimization methods, a process automatically sets the parameters of the PDR differently for different activities to enhance step counting and step length estimation. Furthermore, a method of trajectory optimization mitigates PDR error accumulation utilizing the non-steady-heading activities. We divided the trajectory into small segments and reconstructed it after targeted optimization of each segment. Our method does not use any a priori knowledge of the building layout, plan, or map. Finally, the mean positioning error of our HAA-PDR in a multilevel building is 1.79 m, which is a significant improvement in accuracy compared with a baseline state-of-the-art PDR system.

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A Self-Adaptive AP Selection Algorithm Based on Multiobjective Optimization for Indoor WiFi Positioning

Cited by 49 publications

References 49 publications

Impact of People’s Movement on Wi-Fi Link Throughput in Indoor Propagation Environments: An Empirical Study

Impact of People’s Movement on Wi-Fi Link Throughput in Indoor Propagation Environments: An Empirical Study

Intelligent Perception and Positioning Technology of Internet of Things by $K$ -Nearest Neighbor Matching Algorithm

An Adaptive Human Activity-Aided Hand-Held Smartphone-Based Pedestrian Dead Reckoning Positioning System

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A Self-Adaptive AP Selection Algorithm Based on Multiobjective Optimization for Indoor WiFi Positioning

Cited by 49 publications

References 49 publications

Impact of People’s Movement on Wi-Fi Link Throughput in Indoor Propagation Environments: An Empirical Study

Impact of People’s Movement on Wi-Fi Link Throughput in Indoor Propagation Environments: An Empirical Study

Intelligent Perception and Positioning Technology of Internet of Things by K -Nearest Neighbor Matching Algorithm

An Adaptive Human Activity-Aided Hand-Held Smartphone-Based Pedestrian Dead Reckoning Positioning System

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Intelligent Perception and Positioning Technology of Internet of Things by $K$ -Nearest Neighbor Matching Algorithm