User Position Research Articles

Redirection Controller Using Reinforcement Learning

There is a growing demand for redirected walking (RDW) techniques and their application. To apply appropriate RDW methods and manipulation, the RDW controllers are predominantly used. There are three types of RDW controllers: direct scripted controller, generalized controller, and predictive controller. The scripted controller type pre-scripts the mapping between the real and virtual environments. The generalized controller type employs the RDW method and manipulation quantities according to a certain procedure depending on the user's position in relation to the real space. This approach has the potential to be reused in any environment; however, it is not fully optimized. The predictive controller type predicts the user's future path using the user's behavior and manages RDW techniques. This approach is highly anticipated to be very effective and versatile; however, it has not been sufficiently developed. This paper proposes a novel RDW controller using reinforcement learning (RL) with advanced plannability/versatility. Our simulation experiments indicate that the proposed method can reduce the number of reset manipulations, which is one of the indicators of the effectiveness of the RDW controller, compared to the generalized controller under real environments with many obstacles. Meanwhile, the experimental results also showed that the gain output by the proposed method oscillates. The results of a user study conducted showed that the proposed RDW controller can reduce the number of resets compared to the conventional generalized controller. Furthermore, no adverse effects such as cybersickness associated with the oscillation of the output gain were evinced. The simulation and user studies demonstrate that the proposed RDW controller with RL outperforms the existing generalized controllers and can be applied to users.

Secure and location privacy in geographical data with electronic codebook mode-advanced encryption standard

Location-Based Service (LBS) is considered as one of the utmost encouraging as well as new ways of convergence technologies ensuing in the development of numerous fields together with database systems, mobile communication, internet technology and positioning systems. Even though being introduced in the mid 1990s, only contemporary LBS has achieved an efficient intense study owing to its commercial and scientific influence. As the LBS is associated to the user's location that can be preferred to track the actions of user, a strong privacy concern is elevated. To maintain the user's position, different intelligent works that are introduced with numerous challenges are still expecting solutions. To solve these issues, this paper proposes an encryption technique for improving the security of the LBS system named as Electronic Codebook Mode-Advanced Encryption Standard (ECBM-AES). The key purpose of this study is to offer security in addition to location privacy against the attackers. The cryptographic approach uses two veriers, and the claimed region could be in any intermission amongst the two veriers. The results show that the proposed technique provides better key generation ratio and network size, Packet Delivery Ratio (PDR), security and higher location privacy compared with the existing techniques.

Wireless Fingerprinting Localization in Smart Environments Using Reconfigurable Intelligent Surfaces

Reconfigurable Intelligent Surfaces (RISs) promise improved, secure, and more efficient wireless communications. One less understood aspect relates to the benefits of RIS towards wireless localization and positioning of mobile users and devices. In this paper we propose and demonstrate two practical solutions that exploit the diversity offered by RIS-enhanced indoor environments and to select RIS state configurations that generate easily differentiable radio maps for use with wireless fingerprinting localization estimators. Specifically, we first investigate supervised learning feature selection methods to prune the large state space of the RIS, thus reducing complexity and enhancing localization accuracy and device position acquisition time. We then analytically derive noise correlated heuristics that can further reduce the computational complexity of our proposed solution. Finally, we validate and benchmark our proposed solutions through accurate end-to-end models and computer simulations while demonstrating an average localization accuracy improvement of about 33%. Our explorations thus demonstrate how and why accuracy improvements are achieved and also hint towards how these can be further enhanced in practical localization settings while utilizing more than one RIS.

Influence of Mutual Position of Communication Network Users on Accuracy of Positioning by Telemetry Method

Influence of Mutual Position of Communication Network Users on Accuracy of Positioning by Telemetry Method

The Adoption of Mobile Payment Technologies, Social Interactive Consumer-Oriented Applications, and Online Purchasers’ Decision-Making Process

Research background: This paper analyzes the outcomes of an exploratory review of the current research on the relationship between the global adoption of mobile payment technologies, social interactive consumer-oriented applications, and online purchasers’ decision-making process. Purpose of the article: The data used for this study was obtained and replicated from previous research conducted by Econsultancy and Statista. We performed analyses and made estimates regarding mobile e-commerce sales worldwide, frequency of mobile retail app usage according to U.S. smartphone shoppers, how well organizations understand the customer journey for certain audiences, share of Internet users who are likely to use mobile payments on their smartphone in the next year (by country), and time spent per mobile app category. Methods: Data collected from 6,200 respondents are tested against the research model by using structural equation modeling. Findings & Value added: The advent of smartphones redesigns the routine of shopping, thus altering the agency of users. Retailers have instant access to data on the geographical position of users that can be employed in addition to other information to regulate the decision-making process. Perceived effortlessness in utilization of the smartphone is somewhat similar for various mobile shopping application settings. The degree to which mobile purchasing applications are time critical and location sensitive may differ substantially. Mobile retailers can make public the personal, collaborative, and instantaneous buying experience that mobile shopping can offer to customers.

Indoor Positioning System Based on Standardizing Waveform Tendency

Estimating the indoor position of users in commercial buildings remains a significant challenge to date. Although the WiFi‐based indoor localization has been widely explored in many works by employing received signal strength (RSS) patterns as the features, they usually lead to inaccurate results as the RSS could be easily affected by the indoor environmental dynamics. Besides, existing methods are computationally intensive, which have a high time consumption that makes them unsuitable for real‐life applications. In order to deal with those issues, we propose to use standardizing waveform tendency (SWT) of RSS for indoor positioning. We show that the proposed SWT is robust to the noise generated by the dynamic environment. We further develop a novel smartphone indoor positioning system by integrating SWT and kernel extreme learning machine (KELM) algorithm. Extensive real‐world positioning experiments are conducted to demonstrate the superiority of our proposed model in terms of both positioning accuracy and robustness to environmental changes when comparing with state‐of‐the‐art baselines.

Indoor Direct Positioning With Imperfect Massive MIMO Array Using Measured Near-Field Channels

In this article, we focus on indoor direct positioning using a practical massive multiple-input and multiple-output (MIMO) array with imperfections. First, the imperfect array needs to be calibrated in an anechoic chamber from the view of array signal processing. This brings inconveniences due to the large size of massive MIMO systems. To solve this problem, we propose onsite calibration, where a banded calibration matrix, calibrating both phase-gain error and mutual coupling, can be estimated by performing least-squares on the measure near-field channels, i.e., the site survey data. The feasibility of this calibration method can be explained by that the non-line-of-sight (NLOS) channel is able to be modeled using Rayleigh fading; i.e., the sum of the multipath components can be treated as Gaussian noise. Then, to estimate the user position, we propose a new two-stage direct positioning scheme. In the first stage, the coarse position is obtained via global search-based conventional beamforming (CBF) using a global calibration matrix. In the second stage, fine positioning is achieved via local search-based multiple signal classification (MUSIC) using a local calibration matrix. The proposed method is computationally efficient, insensitive to model order selection, free of dense site survey, and robust to partial line-of-sight (LOS) blocking. Finally, the proposed method is verified by practical measurements from a distributed array (DA), uniform linear array (ULA), and uniform rectangular array (URA), and compared with a deep learning positioning-method. The results show that the DA with the proposed method performs the best, achieving sub-centimeter accuracy (typically below 5 mm).

Outdoor Navigation System by AR

Mobile map applications such as Google Maps often don’t provide detailed information about facility areas such as amusement parks and university campuses. In addition, there are some people who cannot reach their destination just by reading a flat map. Therefore, in this research, we have developed an AR (Augmented Reality) navigation application for facilities to solve these problems. In addition, by using Kalman filtering to estimate user position, we could improve the accuracy of AR objects display.

Enhanced Fingerprinting Based Indoor Positioning Using Machine Learning

Due to the inability of the Global Positioning System (GPS) signals to penetrate through surfaces like roofs, walls, and other objects in indoor environments, numerous alternative methods for user positioning have been presented. Amongst those, the Wi-Fi fingerprinting method has gained considerable interest in Indoor Positioning Systems (IPS) as the need for line-of-sight measurements is minimal, and it achieves better efficiency in even complex indoor environments. Offline and online are the two phases of the fingerprinting method. Many researchers have highlighted the problems in the offline phase as it deals with huge datasets and validation of Fingerprints without pre-processing of data becomes a concern. Machine learning is used for the model training in the offline phase while the locations are estimated in the online phase. Many researchers have considered the concerns in the offline phase as it deals with huge datasets and validation of Fingerprints becomes an issue. Machine learning algorithms are a natural solution for winnowing through large datasets and determining the significant fragments of information for localization, creating precise models to predict an indoor location. Large training sets are a key for obtaining better results in machine learning problems. Therefore, an existing WLAN fingerprinting-based multi-story building location database has been used with 21049 samples including 19938 training and 1111 testing samples. The proposed model consists of mean and median filtering as pre-processing techniques applied to the database for enhancing the accuracy by mitigating the impact of environmental dispersion and investigated machine learning algorithms (kNN, WkNN, FSkNN, and SVM) for estimating the location. The proposed SVM with median filtering algorithm gives a reduced mean positioning error of 0.7959 m and an improved efficiency of 92.84% as compared to all variants of the proposed method for 108703 m2 area.

Efficient Beacon Deployment for Large-scale Positioning

Instant and precise localization of indoor mobile users is fundamental for supporting various sophisticated location-aware services. Using Bluetooth low-power beacons for mobile user positioning has been reported as an effective approach, where the beacon deployment positioning (BDP) problem has been defined. The paper introduces a novel approach for solving large-scale BDP problems, aiming to significantly reduce beacon consumption from existing solutions with much less computation complexity. Extensive simulations are conducted to verify the proposed algorithm, whose beacon consumption is about 1.14 to 1.67 times and 0.2 to 0.48 times compared to those of the Mixed Integer Linear Program (ILP) and a naive iBeacon solution respectively. We have also observed that the running time scales well with the growth of the number of Test Positions and attenuation factors.

Proactive Power Control and Position Deployment for Drone Small Cells: Joint Supervised and Unsupervised Learning

Since unmanned aerial vehicles (UAV) are easily deployed, highly mobile, and hover capability, they are utilized for many commercial applications. In particular, small cells mounted on UAVs, also known as drone small cells (DSC), may provide temporary relief or ancillary programs for the wireless network. In this paper, we design a prediction-based proactive drone management (P2DM) framework to reduce network interference and improve energy efficiency in the multiple DSCs scenario. The P2DM framework can be divided into offline and online phases. In the offline phase, supervised learning is used to build a highly accurate mobility prediction model according to the historical data. The prediction model is launched in the online phase to predict the user position only using a small sample set. The system proactively determines whether a DSC should be awake or asleep at the next timeslot due to the predicted user positions. Since DSC has more longer awake time in the deep sleeping mode, it is previously awoken to avoid data propagation delay. To further overcome the difficulty of obtaining the key performance indicator data (i.e., labeled data) in the online phase, an unsupervised learning technique is employed for DSC repositioning and power control to improve energy efficiency. Our simulation results show that the P2DM framework can demonstrate the advantage in terms of execution time and energy efficiency compared to the existing method based on genetic algorithm (i.e., a heuristic algorithm).

Preserving Secrecy in Mobile Social Networks

Location-based services are one of the most important services offered by mobile social networks. Offering this kind of service requires accessing the physical position of users together with the access authorizations, i.e., who is authorized to access what information. However, these physical positions and authorizations are sensitive information that have to be kept secret from any adversary, including the service providers. As far as we know, the problem of offering location-based services in mobile social networks with a revocation feature under collusion assumption, i.e., an adversary colludes with the service provider, has not been studied. In this article, we show how to solve this problem in the example of range queries. Specifically, we guarantee any adversary, including the service provider, is not able to learn (1) the physical position of the users, (2) the distance between his position and that of the users, and (3) whether two users are allowed to learn the distance between them. We propose two approaches, namely, two-layer symmetric encryption and two-layer attribute-based encryption. The main difference between them is that they use, among other encryption schemes, symmetric and attribute-based encryption, respectively. Next, we prove the secrecy guarantees of both approaches, analyze their complexity, and provide experiments to evaluate their performance in practice.

Hybrid Deep Learning Model Based Indoor Positioning Using Wi-Fi RSSI Heat Maps for Autonomous Applications

Positioning using Wi-Fi received signal strength indication (RSSI) signals is an effective method for identifying the user positions in an indoor scenario. Wi-Fi RSSI signals in an autonomous system can be easily used for vehicle tracking in underground parking. In Wi-Fi RSSI signal based positioning, the positioning system estimates the signal strength of the access points (APs) to the receiver and identifies the user’s indoor positions. The existing Wi-Fi RSSI based positioning systems use raw RSSI signals obtained from APs and estimate the user positions. These raw RSSI signals can easily fluctuate and be interfered with by the indoor channel conditions. This signal interference in the indoor channel condition reduces localization performance of these existing Wi-Fi RSSI signal based positioning systems. To enhance their performance and reduce the positioning error, we propose a hybrid deep learning model (HDLM) based indoor positioning system. The proposed HDLM based positioning system uses RSSI heat maps instead of raw RSSI signals from APs. This results in better localization performance for Wi-Fi RSSI signal based positioning systems. When compared to the existing Wi-Fi RSSI based positioning technologies such as fingerprint, trilateration, and Wi-Fi fusion approaches, the proposed approach achieves reasonably better positioning results for indoor localization. The experiment results show that a combination of convolutional neural network and long short-term memory network (CNN-LSTM) used in the proposed HDLM outperforms other deep learning models and gives a smaller localization error than conventional Wi-Fi RSSI signal based localization approaches. From the experiment result analysis, the proposed system can be easily implemented for autonomous applications.

Outage Analysis in Underlay OFDMA Based Cooperative Cognitive Radio Networks

Background: Efficient resource allocation in Cooperative Cognitive Radio Network (CCRN) is necessary in order to meet the challenges in future wireless networks. With proper resource allocation, the Quality of Service (QoS) comprising of outage probability and data rate are evaluated in this paper and sufficiently improved with proper subcarrier allocation. Objective: Another important parameter is Signal to Interference Ratio (SIR) which should be above a threshold called minimum protection ratio to maintain the required QoS. Results: The network considered is Orthogonal Frequency Division Multiple Access (OFDMA) based Hybrid Cooperative Cognitive Radio Network (HCCRN) in downlink in which licensed as well as unlicensed resources are used by cognitive user depending on it’s availability keeping the interference constraint in limit. The number of subcarriers required is different for every user depending upon its distance from the base station to satisfy the requirement of data rate which depends on the experienced SIR. To avoid outage of users at the boundary of a cell, it is necessary to allocate more number of subcarriers. Conclusion: It is observed that for a given user position and outage probability, as the number of subcarrier allocation in a subchannel increases high data rates can be achieved. This analysis can be useful in allocation of subcarriers to users depending upon their position.

Predicting User Location on Twitter Data: A Review

These days, there is substantial research into predicting the position of online social media users. Research into the automated recognition of places identified or quoted in documents has been under way for decades. Twitter has attracted a vast number of users worldwide as a standout among the online social network organizations that send millions of tweets on a daily basis. Twitter's real-time nature makes it an appealing disaster relief tool, as both victims and authorities can place their concerns and remedies in real-time at the same venue. Knowing the role of a social media user and their posts is critical for various purposes, such as the recommendation of location-based products/services, locality detection of emergencies, demographic analysis, or tracking of disaster outbreaks. This paper discusses the various techniques used for predicting the user locations using the data received from Twitter.

Take TOPBuzz as an Example to See the Copyright Disputes of Aggregated News Client

In the past decade, with the development of various technologies, the Internet Centered new media has been developing continuously, which has profoundly changed the mode of information dissemination and reshaped the pattern of media industry. With the support of commercial capital and technical strength, the aggregation news client with personalized recommendation function emerges as the times require. This paper reviews the concept and development process of aggregated news client. Taking TOPBuzz as an example, this paper analyzes the characteristics of aggregated news client in four aspects: information distribution, user positioning, content push and content sharing, and the changes in media industry, communication subject, user use of media, communication content and media form This paper will take TOPBuzzas an example to analyze the copyright disputes of aggregated news client and put forward feasible suggestions for this issue.

AN AR INDOOR POSITIONING SYSTEM BASED ON ANCHORS

Indoor navigation is a very interesting scientific domain due to its potential use compared with the GPS signals, which are restricted to outdoor environments. This paper describes commonly used methods of Indoor navigation, positioning, and mapping systems using Augmented Reality (AR) techniques. An Indoor navigation system, which is based on an AR application, is a pipelined procedure, which is consisted of three modules. Those are the positioning system, the map, and the route planning algorithms. In this paper, the emphasis is placed on the positioning system module and the creation of the map. The most notable options concerning the AR positioning systems use markers or detected planes in the environment in order to accurately define the position of the user in it. In this paper, we propose a new method of positioning which is based on anchors and unlike other methods can provide a total marker-less experience to the user. Anchors are a crucial feature of most AR Frameworks and used to add augmented content on top of a feature point. Also, we propose a mapping technique that fully supports the positioning method mentioned previously. Concepts like AR Frameworks, anchors, and feature points are also, deeply discussed. The proposed method for position tracking does not require any special hardware or component other than a smartphone with a camera. The proposed method for map creation is an enhanced version of an existing method of the ARKit framework. Finally, the paper analyzes the new methods in terms of accuracy in the estimated user position and measures the error in a distance calculation module that was developed to support the positioning method.

Efficient‐spectrum management based on localisation of primary user position towards 5G

Recent emergent technologies with the advent of 5G use cases have made it imperative for high demands in broadband radio spectrum as compared to the current 4G technologies and other outdated ones. This study introduces an efficient spectrum management based on localisation of the primary user (PU) position in interweave cognitive radio networks (CRNs) towards 5G. In a typical network consisting of licensed system with PUs; and unlicensed system with secondary users (SUs), the SU opportunistically exploits the unused frequency bands of the licensed network provided there is no harmful interference to the PUs. Since the network is dynamic and the PUs are moving targets, the unused bands become unstable; hence, the SUs meet with limitations of detecting white spaces; finally resulting the interference. To this end, both Kriging interpolation and Kalman filter (KF) techniques for tracking and estimating the PUs position are proposed. The performance work was evaluated with respect to received signal strength indicator (RSSI) scheme. Both KF and Kriging interpolation outperformed RSSI. Moreover, the KF leads to the best performance compared to the other two as per localisation error prediction rate and system throughput; which makes it as the preferable technique to tackle mentioned challenges encountered by the SUs in such environment.

Design of IoT Based Object Tracking System for Identifying the RSS Value Access Point to Trilaterate the Location

In an expansive environment, due to continuous change of location of objects, tracking a moving object is a challenging task, in intelligent IoT service provisioned. Tracking of objects indoors using conventional methods like GPS can give out signals not as accurately as we get outside due to the signal attenuation, thus GPS gets poor results, moreover, the inclusion of other effective positioning modules increases the overall cost of production of the device. Nowadays, Wi-Fi networks are prevalent in most public buildings and do not require any additional infrastructure, so we have come with an idea of this is that we could propose an efficient and reliable Wi-Fi-real time tracking system with help access points and signals strength. We are using a combination of two approaches where the first one uses pre-recorded received signal strengths. From the access points, and data is transmitted from the user on the move. It is known as the fingerprinting method. The second approach is trilateration from the known access points coordinates from the user’s device to find the position. The combination of these steps improves the accuracy of the user position in an indoor environment. The experiments presented in this research helps us understand and provide the source of a foundation for the indoor/outdoor positioning.

A Gamified Augmented Reality Application for Digital Heritage and Tourism

Although Augmented Reality (AR) technology has entered many market and knowledge domains such as games and leisure activities, it remains rather limited in digital heritage. After studying the potentiality of using modern AR elements in a museum context, this paper proposes the use of additional game and educational elements in the core AR application in order to enhance the overall on-the-spot museum visitor’s experience. An agile AR application design methodology was followed by taking into account the needs of small-to-medium sized real-world museums. Moreover, a heuristic evaluation protocol was applied by a group of experts in order to test the proof-of-concept AR application, in which some novel elements were proposed such as the AR quiz game. The main findings indicate that enhanced AR experiences in museum settings can make a nice fit with the user environment, physical and perceptual abilities, known metaphors, and user position and motion in 3D space. Moreover, AR services can be provided under a minimum distraction and physical effort. As a conclusion, AR technologies are mature enough to be standardized for museum usage, while the audience seems to be ready to take advantage of the related enhanced museum experiences to maximize both user satisfaction and learning outcomes.