scholarly journals A Design of Irregular Grid Map for Large-Scale Wi-Fi LAN Fingerprint Positioning Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Jae-Hoon Kim ◽  
Kyoung Sik Min ◽  
Woon-Young Yeo
Keyword(s):  
Urban Areas ◽  
Large Scale ◽  
Position Estimation ◽  
Location Based Services ◽  
Positioning Systems ◽  
Irregular Grid ◽  
Positioning Method ◽  
Accurate Position ◽  
Radio Fingerprint ◽  
Received Signal Strength Indication

The rapid growth of mobile communication and the proliferation of smartphones have drawn significant attention to location-based services (LBSs). One of the most important factors in the vitalization of LBSs is the accurate position estimation of a mobile device. The Wi-Fi positioning system (WPS) is a new positioning method that measures received signal strength indication (RSSI) data from all Wi-Fi access points (APs) and stores them in a large database as a form of radio fingerprint map. Because of the millions of APs in urban areas, radio fingerprints are seriously contaminated and confused. Moreover, the algorithmic advances for positioning face computational limitation. Therefore, we present a novel irregular grid structure and data analytics for efficient fingerprint map management. The usefulness of the proposed methodology is presented using the actual radio fingerprint measurements taken throughout Seoul, Korea.

scholarly journals RSS Based Wi-Fi Positioning Method Using Recursive Least Square (RLS) Algorithm

2018 ◽  
Vol 7 (2.24) ◽  
pp. 492
Author(s):  
Sreevardhan Cheerla ◽  
D Venkata Ratnam
Keyword(s):  
Least Square ◽  
Recursive Least Square ◽  
Rls Algorithm ◽  
Positioning Systems ◽  
Indoor Location ◽  
Positioning Method ◽  
Accurate Position ◽  
Software And Hardware ◽  
Newton Raphson ◽  
Raphson Method

Due to rapid increase in demand for services which depends upon exact location of devices leads to the development of numerous Wi-Fi positioning systems. It is very difficult to find the accurate position of a device in indoor environment due to substantial development of structures. There are many algorithms to determine the indoor location but they require expensive software and hardware. Hence receiving signals strength (RSS) based algorithms are implemented to find the self-positioning. In this paper Newton-Raphson, Gauss-Newton and Steepest descent algorithms are implemented to find the accurate location of Wi-Fi receiver in Koneru Lakshmaiah (K L) University, Guntur, Andhra Pradesh, India. From the results it is evident that Newton -Raphson method is better in providing accurate position estimations. 

scholarly journals The Method of Train Positioning Based on Digital Track Map Matching

2018 ◽  
Vol 246 ◽  
pp. 03024
Author(s):  
Pengfei Wang ◽  
Weidong Li ◽  
Xinping Wang ◽  
Xianwu Chu
Keyword(s):  
Correction Method ◽  
Position Estimation ◽  
Map Matching ◽  
Spline Curve ◽  
Positioning Method ◽  
Rail Line ◽  
Accurate Position ◽  
Train Positioning ◽  
Cubic Spline Curve ◽  
Track Line

A train positioning method based on GPS and digital rail line matching is proposed. Firstly, the digital track line is generated based on the fitting and interpolation algorithm of train track line. And then the GPS data are corrected by the track line positioning correction method, and the more accurate position estimation of the train is obtained. Finally, the data track line is simulated and analyzed with some measured data from Harbin to Qigihar track line. The analysis results show that cubic spline curve is better than cubic B-spline curve on the establishment of digital track map.

scholarly journals Algorithm for Dynamic Fingerprinting Radio Map Creation Using IMU Measurements

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2283
Author(s):  
Peter Brida ◽  
Juraj Machaj ◽  
Jan Racko ◽  
Ondrej Krejcar
Keyword(s):  
Dead Reckoning ◽  
Indoor Positioning ◽  
Position Estimation ◽  
Reference Points ◽  
Location Based Services ◽  
Position Information ◽  
Vast Number ◽  
Positioning Systems ◽  
Time Required ◽  
Radio Map

While a vast number of location-based services appeared lately, indoor positioning solutions are developed to provide reliable position information in environments where traditionally used satellite-based positioning systems cannot provide access to accurate position estimates. Indoor positioning systems can be based on many technologies; however, radio networks and more precisely Wi-Fi networks seem to attract the attention of a majority of the research teams. The most widely used localization approach used in Wi-Fi-based systems is based on fingerprinting framework. Fingerprinting algorithms, however, require a radio map for position estimation. This paper will describe a solution for dynamic radio map creation, which is aimed to reduce the time required to build a radio map. The proposed solution is using measurements from IMUs (Inertial Measurement Units), which are processed with a particle filter dead reckoning algorithm. Reference points (RPs) generated by the implemented dead reckoning algorithm are then processed by the proposed reference point merging algorithm, in order to optimize the radio map size and merge similar RPs. The proposed solution was tested in a real-world environment and evaluated by the implementation of deterministic fingerprinting positioning algorithms, and the achieved results were compared with results achieved with a static radio map. The achieved results presented in the paper show that positioning algorithms achieved similar accuracy even with a dynamic map with a low density of reference points.

scholarly journals A novel motion-model-free UWB short-range positioning method

2019 ◽  
Vol 14 (4) ◽  
pp. 815-820
Author(s):  
Navid Ayoobi ◽  
Mohammad Ghavami ◽  
Amir Masoud Rabiei
Keyword(s):  
Indoor Environment ◽  
Channel Model ◽  
Real Data ◽  
Location Based Services ◽  
Outdoor Environment ◽  
Motion Model ◽  
Floor Plan ◽  
Positioning Systems ◽  
Model Free ◽  
Positioning Method

AbstractIn recent years, the number of location-based services is increasing and consequently, the researchers’ attentions are captivated in designing accurate real-time positioning systems. Despite having a good performance in outdoor environment, global positioning system is not capable of estimating an object’s position in an indoor environment precisely. In this paper, we present a novel tracking algorithm for indoor environment with a known floor plan. The object location is estimated by utilizing the information of the multipath components which are created by one physical and some virtual anchors. We will link this information to the floor plan by defining a channel model that has a combination of stochastic and deterministic traits. As we have used only one physical anchor in this paper, we would encounter several challenges such as lack of data association and existence of clutters amid real data. We dealt with these problems through random finite set methodology. Additionally, we will demonstrate that the proposed method is not restricted by the model of the motion and is capable to precisely track the trajectory. It will be shown that it provides a better accuracy, particularly in nonlinear trajectories, compared with two other relevant models which are adopting linear motion model.

scholarly journals Outdoor Node Localization Using Random Neural Networks for Large-Scale Urban IoT LoRa Networks

Algorithms ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 307
Author(s):  
Winfred Ingabire ◽  
Hadi Larijani ◽  
Ryan M. Gibson ◽  
Ayyaz-UI-Haq Qureshi
Keyword(s):  
Neural Networks ◽  
Long Range ◽  
Urban Areas ◽  
Large Scale ◽  
Low Cost ◽  
Wireless Sensor ◽  
Node Localization ◽  
Positioning Systems ◽  
Power Characteristics ◽  
Random Neural Networks

Accurate localization for wireless sensor end devices is critical, particularly for Internet of Things (IoT) location-based applications such as remote healthcare, where there is a need for quick response to emergency or maintenance services. Global Positioning Systems (GPS) are widely known for outdoor localization services; however, high-power consumption and hardware cost become a significant hindrance to dense wireless sensor networks in large-scale urban areas. Therefore, wireless technologies such as Long-Range Wide-Area Networks (LoRaWAN) are being investigated in different location-aware IoT applications due to having more advantages with low-cost, long-range, and low-power characteristics. Furthermore, various localization methods, including fingerprint localization techniques, are present in the literature but with different limitations. This study uses LoRaWAN Received Signal Strength Indicator (RSSI) values to predict the unknown X and Y position coordinates on a publicly available LoRaWAN dataset for Antwerp in Belgium using Random Neural Networks (RNN). The proposed localization system achieves an improved high-level accuracy for outdoor dense urban areas and outperforms the present conventional LoRa-based localization systems in other work, with a minimum mean localization error of 0.29 m.

Engineering in the time of cholera: overcoming institutional and political challenges to rebuild Zimbabwe's water and sanitation infrastructure in the aftermath of the 2008 cholera epidemic

2013 ◽  
Vol 3 (2) ◽  
pp. 222-229 ◽  
Author(s):  
Clarissa Brocklehurst ◽  
Murtaza Malik ◽  
Kiwe Sebunya ◽  
Peter Salama
Keyword(s):  
Urban Areas ◽  
Large Scale ◽  
Humanitarian Aid ◽  
Urban Systems ◽  
Political Crisis ◽  
Rapid Improvement ◽  
Cholera Epidemic ◽  
Rapid Action ◽  
Water And Sanitation Infrastructure ◽  
Urban Rehabilitation

A devastating cholera epidemic swept Zimbabwe in 2008, causing over 90,000 cases, and leaving more than 4,000 dead. The epidemic raged predominantly in urban areas, and the cause could be traced to the slow deterioration of Zimbabwe's water and sewerage utilities during the economic and political crisis that had gripped the country since the late 1990s. Rapid improvement was needed if the country was to avoid another cholera outbreak. In this context, donors, development agencies and government departments joined forces to work in a unique partnership, and to implement a programme of swift improvements that went beyond emergency humanitarian aid but did not require the time or massive investment associated with full-scale urban rehabilitation. The interventions ranged from supply of water treatment chemicals and sewer rods to advocacy and policy advice. The authors analyse the factors that made the programme effective and the challenges that partners faced. The case of Zimbabwe offers valuable lessons for other countries transitioning from emergency to development, and particularly those that need to take rapid action to upgrade failing urban systems. It illustrates that there is a ‘middle path’ between short-term humanitarian aid delivered in urban areas and large-scale urban rehabilitation, which can provide timely and highly effective results.

Research and Develop on Static Frequency Converter of Pumped Storage Power Plant

2012 ◽  
Vol 608-609 ◽  
pp. 1120-1126 ◽  
Author(s):  
De Shun Wang ◽  
Bo Yang ◽  
Lian Tao Ji
Keyword(s):  
Power Plant ◽  
Control Strategy ◽  
Large Scale ◽  
Frequency Converter ◽  
Position Estimation ◽  
Position Sensor ◽  
Storage Unit ◽  
Rotor Position ◽  
Start Up ◽  
Pumped Storage

A static frequency converter start-up control strategy for pumped-storage power unit is presented. And rotor position detecting without position sensor is realized according to voltage and magnetism equations of ideal synchronous motor mathematics model. The mechanism and implementation method of initial rotor position determination and rotor position estimation under low frequency without position sensor are expounded and validated by simulations. Based on the mentioned control strategy, first set of a static frequency converter start-up device in China for large-scale pumped-storage unit is developed, which is applied to start-up control test in the 90 MW generator/motor of Panjiakou Pumped-storage Power Plant. Test results show that rotor position detecting, pulse commutation, natural commutation, and unit synchronous procedure control of static start-up are all proved. The outcomes have been applied in running equipment, which proves the feasibility of mentioned method.

scholarly journals Modeling Spatiotemporal Population Changes by Integrating DMSP-OLS and NPP-VIIRS Nighttime Light Data in Chongqing, China

2021 ◽  
Vol 13 (2) ◽  
pp. 284
Author(s):  
Dan Lu ◽  
Yahui Wang ◽  
Qingyuan Yang ◽  
Kangchuan Su ◽  
Haozhe Zhang ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Relative Error ◽  
Urban Areas ◽  
Large Scale ◽  
Population Distribution ◽  
Spatial Optimization ◽  
Distribution Data ◽  
Mountainous Areas ◽  
Mean Relative Error ◽  
Nighttime Light

The sustained growth of non-farm wages has led to large-scale migration of rural population to cities in China, especially in mountainous areas. It is of great significance to study the spatial and temporal pattern of population migration mentioned above for guiding population spatial optimization and the effective supply of public services in the mountainous areas. Here, we determined the spatiotemporal evolution of population in the Chongqing municipality of China from 2000–2018 by employing multi-period spatial distribution data, including nighttime light (NTL) data from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). There was a power function relationship between the two datasets at the pixel scale, with a mean relative error of NTL integration of 8.19%, 4.78% less than achieved by a previous study at the provincial scale. The spatial simulations of population distribution achieved a mean relative error of 26.98%, improved the simulation accuracy for mountainous population by nearly 20% and confirmed the feasibility of this method in Chongqing. During the study period, the spatial distribution of Chongqing’s population has increased in the west and decreased in the east, while also increased in low-altitude areas and decreased in medium-high altitude areas. Population agglomeration was common in all of districts and counties and the population density of central urban areas and its surrounding areas significantly increased, while that of non-urban areas such as northeast Chongqing significantly decreased.

The Economic Imperative: Cities Need Migrants

2021 ◽  
pp. 001946622110132
Author(s):  
Astha Agarwalla ◽  
Errol D’Souza
Keyword(s):  
Rural Areas ◽  
Urban Areas ◽  
Large Scale ◽  
Market Failure ◽  
Jel Classification ◽  
Industry Concentration ◽  
Wage Curve ◽  
Economic Agents ◽  
Reverse Migration ◽  
Local Labour

The policy responses to Covid-19 have triggered large-scale reverse migration from cities to rural areas in developing countries, exposing the vulnerability of migrants living precarious lives in cities, giving rise to debates asserting to migration as undesirable and favouring policy options to discourage the process. However, the very basis of spatial concentration and formation of cities is presence of agglomeration economies, benefits accruing to economic agents operating in cities. Presence of these agglomeration benefits in local labour markets manifests themselves in the form of an upward sloping wage curve in urban areas. We estimate the upward sloping wage curve for various size classes of cities in Indian economy and establish the presence of positive returns to occupation and industry concentration at urban locations. Controlling for worker-specific characteristics influencing wages, we establish that higher the share of an industry or an occupation in local employment as compared to national economy, the desirability of firms to pay higher wages increases. For casual labourers, occupational concentration results in higher wages. However, impact of industry concentration varies across sectors. Results supporting presence of upward sloping urban wage curve, therefore, endorse policies to correct the market failure in cities and promote migration as a desirable process. JEL Classification Codes: J2, R2

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.

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