Green wireless local area network received signal strength dimensionality reduction and indoor localization based on fingerprint algorithm

2013 ◽  
Vol 27 (12) ◽  
pp. 4527-4542 ◽  
Author(s):  
Lin Ma ◽  
Caifa Zhou ◽  
Danyang Qin ◽  
Yubin Xu
Keyword(s):  
Dimensionality Reduction ◽  
Wireless Local Area Network ◽  
Indoor Localization ◽  
Local Area Network ◽  
Signal Strength ◽  
Local Area ◽  
Received Signal Strength ◽  
Area Network ◽  
Fingerprint Algorithm

scholarly journals DeepLocBox: Reliable Fingerprinting-Based Indoor Area Localization

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2000
Author(s):  
Marius Laska ◽  
Jörg Blankenbach
Keyword(s):  
Wireless Local Area Network ◽  
Indoor Localization ◽  
Local Area Network ◽  
Success Probability ◽  
Local Area ◽  
Position Estimation ◽  
Location Based Services ◽  
Global Navigation Satellite Systems ◽  
Area Network ◽  
Floor Plan

Location-based services (LBS) have gained increasing importance in our everyday lives and serve as the foundation for many smartphone applications. Whereas Global Navigation Satellite Systems (GNSS) enable reliable position estimation outdoors, there does not exist any comparable gold standard for indoor localization yet. Wireless local area network (WLAN) fingerprinting is still a promising and widely adopted approach to indoor localization, since it does not rely on preinstalled hardware but uses the existing WLAN infrastructure typically present in buildings. The accuracy of the method is, however, limited due to unstable fingerprints, etc. Deep learning has recently gained attention in the field of indoor localization and is also utilized to increase the performance of fingerprinting-based approaches. Current solutions can be grouped into models that either estimate the exact position of the user (regression) or classify the area (pre-segmented floor plan) or a reference location. We propose a model, DeepLocBox (DLB), that offers reliable area localization in multi-building/multi-floor environments without the prerequisite of a pre-segmented floor plan. Instead, the model predicts a bounding box that contains the user’s position while minimizing the required prediction space (size of the box). We compare the performance of DLB with the standard approach of neural network-based position estimation and demonstrate that DLB achieves a gain in success probability by 9.48% on a self-collected dataset at RWTH Aachen University, Germany; by 5.48% for a dataset provided by Tampere University of Technology (TUT), Finland; and by 3.71% for the UJIIndoorLoc dataset collected at Jaume I University (UJI) campus, Spain.

scholarly journals Indoor Localization within Multi-Story Buildings Using MAC and RSSI Fingerprint Vectors

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2433 ◽  
Author(s):  
Litao Han ◽  
Li Jiang ◽  
Qiaoli Kong ◽  
Ji Wang ◽  
Aiguo Zhang ◽  
...  
Keyword(s):  
Wireless Local Area Network ◽  
Indoor Localization ◽  
Local Area Network ◽  
Local Area ◽  
Indoor Positioning ◽  
Media Access Control ◽  
Area Network ◽  
Media Access ◽  
Novel Approach ◽  
The Difference

For existing wireless network devices and smart phones to achieve available positioning accuracy easily, fingerprint localization is widely used in indoor positioning, which depends on the differences of the Received Signal Strength Indicator (RSSI) from the Wireless Local Area Network (WLAN) in different places. Currently, most researchers pay more attention to the improvement of online positioning algorithms using RSSI values, while few focus on the MAC (media access control) addresses received from the WLAN. Accordingly, we attempt to integrate MAC addresses and RSSI values simultaneously in order to realize indoor localization within multi-story buildings. A novel approach to indoor positioning within multi-story buildings is presented in this article, which includes two steps: firstly, to identify the floor using the difference of received MAC addresses in different floors; secondly, to implement further localization on the same floor. Meanwhile, clustering operation using MAC addresses as the clustering index is introduced in the online positioning phase to improve the efficiency and accuracy of indoor positioning. Experimental results show that the proposed approach can achieve not only the precise location with the horizontal accuracy of 1.8 meters, but also the floor where the receiver is located within multi-story buildings.

scholarly journals Indoor Radio Map Construction Based on Position Adjustment and Equipment Calibration

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2818
Author(s):  
Ruolin Guo ◽  
Danyang Qin ◽  
Min Zhao ◽  
Xinxin Wang
Keyword(s):  
Particle Filtering ◽  
Wireless Local Area Network ◽  
Indoor Localization ◽  
Local Area Network ◽  
Dead Reckoning ◽  
Local Area ◽  
Area Network ◽  
Map Construction ◽  
Indoor Radio ◽  
Radio Map

The crowdsourcing-based wireless local area network (WLAN) indoor localization system has been widely promoted for the effective reduction of the workload from the offline phase data collection while constructing radio maps. Aiming at the problem of the diverse terminal devices and the inaccurate location annotation of the crowdsourced samples, which will result in the construction of the wrong radio map, an effective indoor radio map construction scheme (RMPAEC) is proposed based on position adjustment and equipment calibration. The RMPAEC consists of three main modules: terminal equipment calibration, pedestrian dead reckoning (PDR) estimated position adjustment, and fingerprint amendment. A position adjustment algorithm based on selective particle filtering is used by RMPAEC to reduce the cumulative error in PDR tracking. Moreover, an inter-device calibration algorithm is put forward based on receiver pattern analysis to obtain a device-independent grid fingerprint. The experimental results demonstrate that the proposed solution achieves higher localization accuracy than the peer schemes, and it possesses good effectiveness at the same time.

scholarly journals Pedestrian Motion Learning Based Indoor WLAN Localization via Spatial Clustering

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaolong Yang ◽  
Yanmeng Wang ◽  
Mu Zhou ◽  
Yiyao Liu
Keyword(s):  
Wireless Local Area Network ◽  
Indoor Localization ◽  
Local Area Network ◽  
Spatial Clustering ◽  
Local Area ◽  
Labor Cost ◽  
Location Based Services ◽  
Area Network ◽  
Motion Sensors ◽  
Motion Learning

Applications on Location Based Services (LBSs) have driven the increasing demand for indoor localization technology. The conventional location fingerprinting based localization involves heavy time and labor cost for database construction, while the well-known Simultaneous Localization and Mapping (SLAM) technique requires assistant motion sensors as well as complicated data fusion algorithms. To solve the above problems, a new pedestrian motion learning based indoor Wireless Local Area Network (WLAN) localization approach is proposed in this paper to achieve satisfactory LBS without the demand for location calibration or motion sensors. First of all, the concept of pedestrian motion learning is adopted to construct users’ motion paths in the target environment. Second, based on the timestamp relation of the collected Received Signal Strength (RSS) sequences, the RSS segments are constructed to obtain the signal clusters with the newly defined high-dimensional linear distance. Third, the PageRank algorithm is performed to establish the hotspot mapping relations between the physical and signal spaces which are then used to localize the target. Finally, the experimental results show that the proposed approach can effectively estimate the target’s locations and analyze users’ motion preference in indoor environment.

scholarly journals Penerapan Wireless Distribution System (WDS) Mesh Untuk Optimasi Cakupan Area Wi-Fi di UM Jember

2021 ◽  
Vol 1 (2) ◽  
pp. 61-69
Author(s):  
Achmad Riyadus Sholikhin ◽  
Taufiq Timur Warisaji ◽  
Triawan Adi Cahyanto
Keyword(s):  
Distribution System ◽  
Wireless Local Area Network ◽  
Local Area Network ◽  
Signal To Noise Ratio ◽  
Rapid Change ◽  
Signal Strength ◽  
Access Point ◽  
Local Area ◽  
Area Network ◽  
One Step

One very rapid change in computer networks is the use of Wireless Local Area Network (WLAN) to access systems both locally and the internet. One way to add an Access Point (AP) so as not to change the configuration of the running software is use the Wireless Distribution System Mesh (WDS) network. The use of WDS mesh at Universitas Muhammmadiyah Jember (UM Jember) enables a slightly different wireless configuration to reduce the connection loss in the user due to the wireless network area coverage. One step is use AP devices into one unit to handle the case of a connection break by utilizing a network service that is WDS Mesh. WDS Mesh can cover Wi-Fi areas at UM Jember, and the results of testing and analysis of client devices for APs that have been configured WDS Mesh with parameters Signal Strength, CCQ, Signal to Noise Ratio Ratio, Throughput is Excellent.

On Localization Using Location Fingerprints and Energy Efficient Operation of WLANs in Indoor Areas

2011 ◽  
Vol 204-210 ◽  
pp. 1221-1224
Author(s):  
Mu Zhou ◽  
Yu Bin Xu ◽  
Prashant Krishnamurthy ◽  
Lin Ma
Keyword(s):  
Energy Consumption ◽  
Wireless Local Area Network ◽  
Local Area Network ◽  
Local Area ◽  
Area Network ◽  
Efficient Operation ◽  
Power Efficient ◽  
University Campuses ◽  
Special Location ◽  
Fingerprint Algorithm

This study focuses on the power efficient localization by fingerprinted KNN algorithm in the indoor wireless local area network (WLAN) environment. To the best of our knowledge, although the fingerprint algorithm has been utilized to supply some special location based service (LBS) with high precision and accuracy, these associated location systems have resulted in significant energy consumption. Therefore, serious attention should be paid on the energy consumption because of the hundreds to thousands of access points (APs) with high-density deployed in our university campuses and corporate offices. In response to this compelling problem, we discuss the relationship between energy costs and radio map-based location performance for the Gaussian radio signal strength (RSS) distribution with different neighboring points in KNN. Furthermore, using our results, the guidelines on the power efficient fingerprint location algorithm is also provided.

scholarly journals Indoor Localization Based on Optimized KNN

2020 ◽  
Vol 5 (2) ◽  
pp. 34
Author(s):  
Xuanyu Zhu
Keyword(s):  
Wireless Local Area Network ◽  
Indoor Localization ◽  
Local Area Network ◽  
Local Area ◽  
Indoor Positioning ◽  
Area Network ◽  
Online Matching ◽  
Fingerprint Database ◽  
The Cost ◽  
Real Time Information

In recent years, with the continuous development of the economic situation, the price of low-end smart phones continues to reduce, the coverage of wireless local area network (WLAN) continues to improve, and individual users pay more and more attention to the real-time information around them, so indoor positioning technology has become a research hotspot. Among them, the indoor positioning based on the location fingerprint method quickly becomes the “Navigator” of indoor positioning direction by virtue of the simplicity of layout, the cost reduction of hardware facilities and the accuracy of positioning effect. However, the traditional indoor positioning methods usually rely on WiFi signal and KNN algorithm. When the KNN algorithm is implemented, there will be a lot of calculation and heavy workload to establish the location fingerprint database offline, and the efficiency and accuracy of online matching positioning points are low. This paper proposes an OKNN algorithm based on the improved KNN algorithm. By improving the efficiency of matching algorithm, the algorithm indirectly improves the positioning accuracy and optimizes the indoor positioning effect.

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