An indoor Wi-Fi access points localization algorithm based on improved path loss model parameter calculation method and recursive partition

The localization accuracy of the existing methods for indoor Wi-Fi access points ranging-based localization depends on the accuracy of the received signal strength measured. Because the existing ranging-based methods are interfered by various indoor environmental factors, it is difficult to accurately measure the received signal strength, which leads to the problem of low localization accuracy of the indoor Wi-Fi access points. An indoor Wi-Fi access points localization algorithm based on improved path loss model parameter calculation method and recursive partition is proposed in this article. The algorithm recursively partitions the region where the target Wi-Fi access points are located according to the idea of quadtree partition, and partitions it into same sub-grids, which is sequentially performed until the sub-grids are smaller than the set threshold. The detection device is used at the detection location of the grids to measure the received signal strength, which is from the detection points to the target access point, the grid center point is used as the location of the candidate target access point, the parameters in the path loss model are calculated by using the signal strength differences between the detection points, and then the distances between the detection points and the target access point are calculated by using the signal strength values from the detection points to the target access point. Finally, the location of the target access point is estimated by executing a localization algorithm, and the location of the grid center point closest to the target access point is taken as the location of the target access point. The experimental results show that under the premise that the target access point can be found, the proposed algorithm reduces the use of the device and improves the localization accuracy compared with the typical localization method.

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Received Signal Strength-Based Robust Cooperative Localization With Dynamic Path Loss Model

IEEE Sensors Journal ◽

10.1109/jsen.2015.2500270 ◽

2016 ◽

Vol 16 (5) ◽

pp. 1265-1270 ◽

Cited By ~ 14

Author(s):

Chen Liang ◽

Fuxi Wen

Keyword(s):

Path Loss ◽

Signal Strength ◽

Received Signal Strength ◽

Cooperative Localization ◽

Loss Model ◽

Path Loss Model ◽

Strength Based ◽

Dynamic Path

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Step Length Measurements Using the Received Signal Strength Indicator

Sensors ◽

10.3390/s21020382 ◽

2021 ◽

Vol 21 (2) ◽

pp. 382

Author(s):

Zanru Yang ◽

Le Chung Tran ◽

Farzad Safaei

Keyword(s):

Path Loss ◽

Signal Strength ◽

Step Length ◽

Received Signal Strength ◽

Body Parts ◽

Received Signal Strength Indicator ◽

Loss Model ◽

Path Loss Model ◽

Outdoor Environments ◽

Indoor And Outdoor Environments

In this paper, portable transceivers with micro-controllers and radio frequency modules are developed to measure the received signal strength, path loss, and thus the distance between the human ankles for both indoor and outdoor environments. By comparing the experimental results and the theoretical model, a path loss model between transceivers attached to the subject’s ankles is derived. With the developed experimental path loss model, the step length can be measured relatively accurately, despite the imperfections of hardware devices, with the distance errors of a centimeter level. This paper, therefore, helps address the need for a distance measurement method that has fewer health concerns, is accurate, and is less affected by occlusions and confined spaces. Our findings possibly lay a foundation for some important applications, such as the measurement of gait speed and localization of the human body parts, in wireless body area networks.

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Localization accuracy of farmland wireless sensor network localization algorithm based on received signal strength indicator

Ingénierie des systèmes d information ◽

10.3166/isi.23.5.69-80 ◽

2018 ◽

Vol 23 (5) ◽

pp. 69-80 ◽

Cited By ~ 1

Author(s):

Yisheng MIAO ◽

Huarui WU ◽

Huaji ZHU ◽

Yuling SONG

Keyword(s):

Wireless Sensor Network ◽

Sensor Network ◽

Signal Strength ◽

Received Signal Strength ◽

Wireless Sensor ◽

Localization Algorithm ◽

Received Signal Strength Indicator ◽

Sensor Network Localization ◽

Localization Accuracy ◽

Network Localization

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Determination of an Outdoor Path Loss Model and Signal Penetration Level in Some Selected Modern Residential and Office Apartments in Ogbomosho, Oyo State, Nigeria

Journal of Engineering Research and Reports ◽

10.9734/jerr/2018/v1i29804 ◽

2018 ◽

pp. 1-25

Author(s):

V. O. A. Akpaida ◽

F. I. Anyasi ◽

S. I. Uzairue ◽

A. I. Idim

Keyword(s):

Building Materials ◽

Path Loss ◽

Signal Strength ◽

Base Stations ◽

Propagation Path ◽

Loss Model ◽

Path Loss Model ◽

Path Loss Exponent ◽

Received Power

This article involves the site specific determination of an outdoor path loss model and Signal penetration level in some selected modern residential and office apartments in Ogbomosho, Oyo State. Measurements of signal strength and its associated location parameters referenced globally were carried out. Propagation path loss characteristics of Ogbomosho were investigated using three different locations with distinctively different yet modern building materials. Consequently, received signal strength (RSS) was measured at a distance d in meters, from appropriate base stations for various environments investigated. The data were analyzed to determine the propagation path loss exponent, signal penetration level and path loss characteristics. From calculations, the average building penetration losses were, 5.93dBm, 6.40dBm and 6.1dBm outside the hollow blocks B1, solid blocks B2 and hollow blocks mixed with pre cast asbestos B3, buildings respectively with a corresponding path loss exponent values of, 3.77, 3.80 and 3.63. Models were developed and validated, and used to predict the received power inside specific buildings. Moreover, the propagation models developed for the different building types can be used to predict the respective signal level within the building types, once the transmitter – receiver distance is known. The readings obtained from the developed models were compared with both the measured values and values computed using some existing models with satisfactory results obtained.

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SP-Loc: A crowdsourcing fingerprint based shop-level indoor localization algorithm integrating shop popularity without the indoor map

International Journal of Distributed Sensor Networks ◽

10.1177/1550147718815637 ◽

2018 ◽

Vol 14 (11) ◽

pp. 155014771881563 ◽

Cited By ~ 2

Author(s):

Jie Wei ◽

Fang Zhao ◽

Haiyong Luo

Keyword(s):

Indoor Localization ◽

Signal Strength ◽

Received Signal Strength ◽

Location Based Services ◽

Shopping Mall ◽

Time Stamp ◽

Localization Algorithm ◽

Variation Pattern ◽

Localization Accuracy ◽

User Location

With the development of indoor localization technology, the location-based services such as product advertising recommendation in the shopping mall attract widespread attention, as precise user location significantly improves the efficiency of advertising push and brings broader profits. However, most of the Wi-Fi-based indoor localization approaches requiring professionals to deploy expensive beacon devices and intensively collect fingerprints in each location grid, which severely limits its extensive promotion. We introduce a zero-cost indoor localization algorithm utilizing crowdsourcing fingerprints to obtain the shop recognition where the user is located. Naturally utilizing the Wi-Fi, GPS, and time-stamp fingerprints collected from the smartphone when user paid as the crowdsourcing fingerprint, we avoid the requirement for indoor map and get rid of both devices cost and manual signal collecting process. Moreover, a shop-level hierarchical indoor localization framework is proposed, and high robustness features based on Wi-Fi sequences variation pattern in the same shop analysis are designed to avoid the received signal strength fluctuations. Besides, we also pay more attention to mine the popularity properties of shops and explore GPS features to improve localization accuracy in the Wi-Fi absence situation effectively. Massive experiments indicate that SP-Loc achieves more than 93% localization accuracy.

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Indoor localization system based on virtual access points with filtering schemes

International Journal of Distributed Sensor Networks ◽

10.1177/1550147719866135 ◽

2019 ◽

Vol 15 (7) ◽

pp. 155014771986613 ◽

Cited By ~ 2

Author(s):

Dong Myung Lee ◽

Boney Labinghisa

Keyword(s):

Kalman Filter ◽

Indoor Environment ◽

Performance Testing ◽

Signal Strength ◽

Access Point ◽

Received Signal Strength ◽

Indoor Environments ◽

Received Signal Strength Indicator ◽

Access Points ◽

Error Distance

In indoor positioning techniques, Wi-Fi is one of the most used technology because of its availability and cost-effectiveness. Access points are usually the main source of Wi-Fi signals in an indoor environment. If access points are optimized to cover the indoor area, this could improve Wi-Fi signal distribution. This article proposed an alternative to optimizing access point placement and distribution by introducing virtual access points that can be virtually placed in any part of the indoor environment without installation of actual access points. Virtual access points will be created heuristically by correlating received signal strength indicator of already existing access points and through linear regression. After introducing virtual access points in the indoor environment, next will be the addition of filters to improve signal fluctuation and reduce noise interference. Kalman filter has been previously used together with virtual access point and showed improvement by decreasing error distance of Wi-Fi fingerprinting results. This article also aims to include particle filter in the system to further improve localization and test its effectiveness when paired with Kalman filter. The performance testing of the algorithm in different indoor environments resulted in 3.18 and 3.59 m error distances. An improvement was added on the system by using relative distances instead of received signal strength indicator values in distance estimation and gave an error distance average of 1.85 m.

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An Automated Indoor Localization System for Online Bluetooth Signal Strength Modeling Using Visual-Inertial SLAM

Sensors ◽

10.3390/s21082857 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2857

Author(s):

Simon Tomažič ◽

Igor Škrjanc

Keyword(s):

Data Acquisition ◽

Indoor Localization ◽

Low Cost ◽

Path Loss ◽

Signal Strength ◽

Localization Algorithm ◽

Localization Accuracy ◽

Localization System ◽

Good Trade ◽

Strength Modeling

Indoor localization is becoming increasingly important but is not yet widespread because installing the necessary infrastructure is often time-consuming and labor-intensive, which drives up the price. This paper presents an automated indoor localization system that combines all the necessary components to realize low-cost Bluetooth localization with the least data acquisition and network configuration overhead. The proposed system incorporates a sophisticated visual-inertial localization algorithm for a fully automated collection of Bluetooth signal strength data. A suitable collection of measurements can be quickly and easily performed, clearly defining which part of the space is not yet well covered by measurements. The obtained measurements, which can also be collected via the crowdsourcing approach, are used within a constrained nonlinear optimization algorithm. The latter is implemented on a smartphone and allows the online determination of the beacons’ locations and the construction of path loss models, which are validated in real-time using the particle swarm localization algorithm. The proposed system represents an advanced innovation as the application user can quickly find out when there are enough data collected for the expected radiolocation accuracy. In this way, radiolocation becomes much less time-consuming and labor-intensive as the configuration time is reduced by more than half. The experiment results show that the proposed system achieves a good trade-off in terms of network setup complexity and localization accuracy. The developed system for automated data acquisition and online modeling on a smartphone has proved to be very useful, as it can significantly simplify and speed up the installation of the Bluetooth network, especially in wide-area facilities.

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Survey of Radio Propagation Models for Acoustic Applications in Underwater Wireless Sensor Network

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327909666191127093403 ◽

2019 ◽

Vol 09 ◽

Author(s):

Preeti Saini ◽

Rishi Pal Singh ◽

Adwitiya Sinha

Keyword(s):

Acoustic Waves ◽

Path Loss ◽

Signal Strength ◽

Propagation Model ◽

Propagation Loss ◽

Radio Propagation ◽

Propagation Models ◽

Loss Model ◽

Path Loss Model ◽

Radio Propagation Model

Background: Acoustic waves have a large range of applications in UWSNs from underwater monitoring to disaster management, military surveillance to assisted navigation. Acoustic waves are primarily used for wireless communication in water. But radio waves are more suitable than acoustic waves for many underwater applications (e.g. real-time applications, shallow water applications). Objectives: A propagation model is required to effectively design a radio wave based UWSN. Propagation model predicts the average received signal strength at a given distance from the transmitter and the variability of the signal strength in close spatial proximity to a particular location. Various radio propagation models are developed for air. Methods: The performance of RF-EM waves underwater is not the same as that in the air. Many parameters which have real-value in the air becomes complex valued in seawater. Thus, propagation models for air cannot be directly used to calculate propagation loss underwater. Various radio propagation models are developed for water by Al-Shamaa’a et al., Uribe and Grote, Jiang et al., Elrashidi et al., Hattab et al. Each model has some merits and demerits. Path loss model developed by Al-Shamma’a et al. is a simple model based on attenuation only. Results: Uribe and Grote have introduced distance-dependent attenuation coefficient in path loss calculation. Path loss model by Jiang et al. calculates path loss for freshwater. Model by Hattab et al. is specifically designed for UWSN. According to the authors, it is the first path loss model developed for UWSN. Elrashidi et al. have calculated path loss for freshwater and seawater at 2.4 GHz. The model includes the effect of the reflected signals on the received signal by the receiver node. Conclusion: The paper presents a comparative analysis of these various radio propagation models developed for underwater. Among these models, the radio propagation model by Hattab et al. is more realistic and covers both propagation loss and interface loss. According to the authors, it is the first radio propagation model developed for UWSNs.

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