Semantic localization

2017 ◽  
Vol 01 (01) ◽  
pp. 1630010 ◽  
Author(s):  
Shang Ma ◽  
Qiong Liu
Keyword(s):  
Domain Knowledge ◽  
Indoor Localization ◽  
System Modeling ◽  
Location Based Services ◽  
Real Application ◽  
Location Data ◽  
Advantages And Disadvantages ◽  
Personalized Services ◽  
Semantic Localization ◽  
Application Requirements

Improvements in sensor and wireless network enable accurate, automated, instant determination and dissemination of a user’s or objects position. The new enabler of location-based services (LBSs) apart from the current ubiquitous networking infrastructure is the enrichment of the different systems with semantics information, such as time, location, individual capability, preference and more. Such semantically enriched system-modeling aims at developing applications with enhanced functionality and advanced reasoning capabilities. These systems are able to deliver more personalized services to users by domain knowledge with advanced reasoning mechanisms, and provide solutions to problems that were otherwise infeasible. This approach also takes user’s preference and place property into consideration that can be utilized to achieve a comprehensive range of personalized services, such as advertising, recommendations, or polling. This paper provides an overview of indoor localization technologies, popular models for extracting semantics from location data, approaches for associating semantic information and location data, and applications that may be enabled with location semantics. To make the presentation easy to understand, we will use a museum scenario to explain pros and cons of different technologies and models. More specifically, we will first explore users’ needs in a museum scenario. Based on these needs, we will then discuss advantages and disadvantages of using different localization technologies to meet these needs. From these discussions, we can highlight gaps between real application requirements and existing technologies, and point out promising localization research directions. By identifying gaps between various models and real application requirements, we can draw a roadmap for future location semantics research.

An Overview of Location Semantics Technologies and Applications

2015 ◽  
Vol 09 (03) ◽  
pp. 373-393 ◽  
Author(s):  
Shang Ma ◽  
Qiong Liu ◽  
Henry Tang
Keyword(s):  
Coordinate System ◽  
Semantic Information ◽  
Indoor Localization ◽  
Research Directions ◽  
Real Application ◽  
Location Data ◽  
Advantages And Disadvantages ◽  
The World ◽  
Pros And Cons ◽  
Application Requirements

A localization system is a coordinate system for describing the world, organizing the world, and controlling the world. Without a coordinate system, we cannot specify the world in mathematical forms; we cannot regulate processes that may involve spatial collisions; we cannot even automate a robot for physical actions. This paper provides an overview of indoor localization technologies, popular models for extracting semantics from location data, approaches for associating semantic information and location data, and applications that may be enabled with location semantics. To make the presentation easy to understand, we will use a museum scenario to explain the pros and cons of different technologies and models. More specifically, we will first explore users' needs in a museum scenario. Based on these needs, we will then discuss advantages and disadvantages of using different localization technologies to meet these needs. From these discussions, we can highlight gaps between real application requirements and existing technologies, and point out promising localization research directions. Similarly, we will also discuss context information required by different applications and explore models and ontologies for connecting users, objects, and environment factors with semantics. By identifying gaps between various models and real application requirements, we can draw a roadmap for future location semantics research.

scholarly journals Privacy vs. Reward in Indoor Location-Based Services

2016 ◽  
Vol 2016 (4) ◽  
pp. 102-122 ◽  
Author(s):  
Kassem Fawaz ◽  
Kyu-Han Kim ◽  
Kang G. Shin
Keyword(s):  
Location Privacy ◽  
Indoor Localization ◽  
Service Providers ◽  
Location Based Services ◽  
Location Information ◽  
Privacy Concerns ◽  
Indoor Location ◽  
Location Data ◽  
Personalized Services ◽  
Privacy Risks

AbstractWith the advance of indoor localization technology, indoor location-based services (ILBS) are gaining popularity. They, however, accompany privacy concerns. ILBS providers track the users’ mobility to learn more about their behavior, and then provide them with improved and personalized services. Our survey of 200 individuals highlighted their concerns about this tracking for potential leakage of their personal/private traits, but also showed their willingness to accept reduced tracking for improved service. In this paper, we propose PR-LBS (Privacy vs. Reward for Location-Based Service), a system that addresses these seemingly conflicting requirements by balancing the users’ privacy concerns and the benefits of sharing location information in indoor location tracking environments. PR-LBS relies on a novel location-privacy criterion to quantify the privacy risks pertaining to sharing indoor location information. It also employs a repeated play model to ensure that the received service is proportionate to the privacy risk. We implement and evaluate PR-LBS extensively with various real-world user mobility traces. Results show that PR-LBS has low overhead, protects the users’ privacy, and makes a good tradeoff between the quality of service for the users and the utility of shared location data for service providers.

Location-Based Services

2011 ◽  
pp. 18-34 ◽  
Author(s):  
Ramaprasad Unni ◽  
Robert Harmon
Keyword(s):  
Value Chain ◽  
Business Models ◽  
Service Providers ◽  
Mobile Network ◽  
Location Based Services ◽  
Privacy Concerns ◽  
Location Data ◽  
Personalized Services ◽  
Wireless Users ◽  
Integral Role

Location-based services are expected to play an integral role in the mobile-commerce domain. Mobile network operators and service providers have the opportunity to add value and create additional revenue streams through a variety of personalized services based on location of individual wireless users. However, strategic thinking in this area is still evolving. Many issues surrounding location data such as ownership and their use by network operators and third parties, privacy concerns of consumers, and business models for these services are not well understood. This chapter provides (1) an overview of location-based wireless services and their related technologies, (2) an examination of the LBS value chain, and (3) strategic implications for network operators and service providers.

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 An Indoor Localization System Using Residual Learning with Channel State Information

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 574
Author(s):  
Chendong Xu ◽  
Weigang Wang ◽  
Yunwei Zhang ◽  
Jie Qin ◽  
Shujuan Yu ◽  
...  
Keyword(s):  
Channel State Information ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Location Based Services ◽  
Channel State ◽  
State Information ◽  
Localization Accuracy ◽  
Localization System ◽  
Increasing Demand ◽  
Degradation Problem

With the increasing demand of location-based services, neural network (NN)-based intelligent indoor localization has attracted great interest due to its high localization accuracy. However, deep NNs are usually affected by degradation and gradient vanishing. To fill this gap, we propose a novel indoor localization system, including denoising NN and residual network (ResNet), to predict the location of moving object by the channel state information (CSI). In the ResNet, to prevent overfitting, we replace all the residual blocks by the stochastic residual blocks. Specially, we explore the long-range stochastic shortcut connection (LRSSC) to solve the degradation problem and gradient vanishing. To obtain a large receptive field without losing information, we leverage the dilated convolution at the rear of the ResNet. Experimental results are presented to confirm that our system outperforms state-of-the-art methods in a representative indoor environment.

scholarly journals Complying with Privacy Legislation: From Legal Text to Implementation of Privacy-Aware Location-Based Services

2018 ◽  
Vol 7 (11) ◽  
pp. 442 ◽  
Author(s):  
Mehrnaz Ataei ◽  
Auriol Degbelo ◽  
Christian Kray ◽  
Vitor Santos
Keyword(s):  
Data Privacy ◽  
Location Privacy ◽  
Service Providers ◽  
Location Based Services ◽  
Legal Text ◽  
General Data Protection Regulation ◽  
Location Data ◽  
Privacy Laws ◽  
Technical Issues ◽  
General Data

An individual’s location data is very sensitive geoinformation. While its disclosure is necessary, e.g., to provide location-based services (LBS), it also facilitates deep insights into the lives of LBS users as well as various attacks on these users. Location privacy threats can be mitigated through privacy regulations such as the General Data Protection Regulation (GDPR), which was introduced recently and harmonises data privacy laws across Europe. While the GDPR is meant to protect users’ privacy, the main problem is that it does not provide explicit guidelines for designers and developers about how to build systems that comply with it. In order to bridge this gap, we systematically analysed the legal text, carried out expert interviews, and ran a nine-week-long take-home study with four developers. We particularly focused on user-facing issues, as these have received little attention compared to technical issues. Our main contributions are a list of aspects from the legal text of the GDPR that can be tackled at the user interface level and a set of guidelines on how to realise this. Our results can help service providers, designers and developers of applications dealing with location information from human users to comply with the GDPR.

scholarly journals Uso da Região Ventroglútea como Alternativa para Administração de Imunobiológicos: Uma Revisão Bibliográfica

2012 ◽  
Vol 1 (1) ◽  
Author(s):  
Tavares V.R ◽  
Sousa Neto M.A ◽  
Trindade N.R ◽  
Silva G.A
Keyword(s):  
Domain Knowledge ◽  
Knowledge And Skills ◽  
Vaccine Administration ◽  
Advantages And Disadvantages

A administração de imunobiológicos é uma atividade que deve ser desempenhada por profissionais com domínio de habilidades e conhecimento. Para escolha do local de aplicação das mais diversas vacinas, critérios como menor reatogenicidade e melhor imunogenicidade devem ser considerados. Este estudo teve como objetivo investigar as evidências disponíveis na literatura sobre a administração de imunobiológicos na região ventroglútea, ressaltando as vantagens e desvantagens desta via. Tratou-se de uma pesquisa descritiva de natureza bibliográfica realizada por meio das bases de dados. Foram pesquisados artigos que estudaram a utilização da região ventroglútea como área de escolha para administração de injetáveis. O estudo demonstrou apesar de ser uma região segura para administração de vacinas, ainda não é bem aceita pelos profissionais de saúde.Palavras-chaves: vacina, ventroglúetaABSTRACTThe administration of immunobiological is an activity that should beperformed by professionals with domain knowledge and skills. To choose thesite of application of various vaccines, criteria such as low reactogenicity andimmunogenicity best should be considered. This study aimed to investigate theevidence available in the literature on the management of the immunobiologicalventrogluteal site, highlighting the advantages and disadvantages of this route.This was a descriptive nature of bibliographic through databases. We searchedarticles that studied the use of ventrogluteal site as an area of choice forintravenous administration. The study demonstrated despite being a safe regionfor vaccine administration, yet not well accepted by health professionals.KEYWORDS: vaccine, ventrogluteal

A Novel Approach to Segmentation Using Customer Locations Data and Intelligent Techniques

2018 ◽  
pp. 1792-1810
Author(s):  
Başar Öztayşi ◽  
Ugur Gokdere ◽  
Esra Nur Simsek ◽  
Ceren Salkin Oner
Keyword(s):  
Clustering Algorithm ◽  
Customer Segmentation ◽  
Location Based Services ◽  
Location Data ◽  
Novel Approach ◽  
Product Service ◽  
Fuzzy C Means Clustering ◽  
Initial Segmentation ◽  
Application Data ◽  
New Perspective

Customer segmentation has been one of hottest topics of marketing efforts. The traditional sources of data used for segmentation are demographics, monetary value of transactions, types of product/service selected. Today, data gathered by location based services can also be used for customer segmentation. In this chapter a real world case study is summarized and the initial segmentation results are presented. As the application, data gathered from beacons sited in 4000 locations and Fuzzy c-means clustering algorithm are used. The steps of the application are as follows: (1) Categorization of the shops, (2) Summarization of the location data, (3) Applying fuzzy clustering technique, (4) Analyzing the results and profiling. Results show that customers' location data can provide a new perspective to customer segmentation.

A New Spatial Transformation Scheme for Preventing Location Data Disclosure in Cloud Computing

2016 ◽  
pp. 1752-1776
Author(s):  
Min Yoon ◽  
Hyeong-il Kim ◽  
Miyoung Jang ◽  
Jae-Woo Chang
Keyword(s):  
Cloud Computing ◽  
Performance Analysis ◽  
Success Rate ◽  
Data Privacy ◽  
Spatial Database ◽  
Data Distribution ◽  
Location Based Services ◽  
Spatial Transformation ◽  
Location Data

Because much interest in spatial database for cloud computing has been attracted, studies on preserving location data privacy have been actively done. However, since the existing spatial transformation schemes are weak to a proximity attack, they cannot preserve the privacy of users who enjoy location-based services in the cloud computing. Therefore, a transformation scheme is required for providing a safe service to users. We, in this chapter, propose a new transformation scheme based on a line symmetric transformation (LST). The proposed scheme performs both LST-based data distribution and error injection transformation for preventing a proximity attack effectively. Finally, we show from our performance analysis that the proposed scheme greatly reduces the success rate of the proximity attack while performing the spatial transformation in an efficient way.

scholarly journals A Novel Passive Indoor Localization Method by Fusion CSI Amplitude and Phase Information

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 875 ◽  
Author(s):  
Xiaochao Dang ◽  
Xiong Si ◽  
Zhanjun Hao ◽  
Yaning Huang
Keyword(s):  
Indoor Localization ◽  
Rapid Development ◽  
Location Based Services ◽  
Signal Interference ◽  
Indoor Environments ◽  
Phase Information ◽  
Localization Accuracy ◽  
Localization Method ◽  
Indoor Location ◽  
Conference Room

With the rapid development of wireless network technology, wireless passive indoor localization has become an increasingly important technique that is widely used in indoor location-based services. Channel state information (CSI) can provide more detailed and specific subcarrier information, which has gained the attention of researchers and has become an emphasis in indoor localization technology. However, existing research has generally adopted amplitude information for eigenvalue calculations. There are few research studies that have used phase information from CSI signals for localization purposes. To eliminate the signal interference existing in indoor environments, we present a passive human indoor localization method named FapFi, which fuses CSI amplitude and phase information to fully utilize richer signal characteristics to find location. In the offline stage, we filter out redundant values and outliers in the CSI amplitude information and then process the CSI phase information. A fusion method is utilized to store the processed amplitude and phase information as a fingerprint database. The experimental data from two typical laboratory and conference room environments were gathered and analyzed. The extensive experimental results demonstrate that the proposed algorithm is more efficient than other algorithms in data processing and achieves decimeter-level localization accuracy.

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