User Location Data Research Articles

Identifying the spatial patterns of population displacement during wildfires in Valparaíso, Chile

ABSTRACT We present a data visualisation approach to support the rapid humanitarian response during the 2024 Valparaíso wildfires in Chile. Combining Meta user location data for population displacement, NASA FIRMS satellite imagery for wildfire locations, and census data, we identify key origins and destinations of displaced people during recent wildfires in Valparaíso, Chile. Our choropleth maps reveal spatial patterns of movement and socioeconomic factors, demonstrating the value of integrating diverse data sources for near real-time crisis response.

INFORMATION SYSTEM FOR DETECTION OF PARAMETERS OF DANGEROUS INDUSTRIAL FACILITIES BASED ON GEOINFORMATION TECHNOLOGIES

With the development of industry, the issue of environmental safety of countries worldwide has become increasingly acute. Currently, there is a deficiency of information systems capable of effectively and comprehensively informing the public about the state of the environment, analyzing the dynamics of environmental indicators, and assessing regional disparities in terms of environmental safety. The objective of this study is to develop an information system for monitoring the environmental condition of a country's territory based on geoinformation technologies, considering emissions of pollutants. This system is conceived as a multi-regional monitoring system focused on industrial areas. It incorporates a geo-module for user location determination and data representation tailored to the user's location. Additionally, the system regularly updates information on hazardous enterprises and notifies the population in case of emergencies.

Effective Route Recommendation Leveraging Differentially Private Location Data

The proliferation of GPS-enabled devices and advances in positioning technologies have greatly facilitated the collection of user location data, making them valuable across various domains. One of the most common and practical uses of these location datasets is to recommend the most probable route between two locations to users. Traditional algorithms for route recommendation rely on true trajectory data collected from users, which raises significant privacy concerns due to the personal information often contained in location data. Therefore, in this paper, we propose a novel framework for computing optimal routes using location data collected through differential privacy (DP)-based privacy-preserving methods. The proposed framework introduces a method for accurately extracting transitional probabilities from perturbed trajectory datasets, addressing the challenge of low data utility caused by DP-based methods. Specifically, to effectively compute transitional probabilities, we present a density-adjusted sampling method that enables the collection of representative data across all areas. In addition, we introduce an effective scheme to approximately estimate transitional probabilities based on sampled datasets. Experimental results on real-world data demonstrate the practical applicability and effectiveness of our framework in computing optimal routes while preserving user privacy.

DiSA-CF: A distance-integrated self-attention model for collaborative filtering in web service recommendation

The ubiquity of the Internet of Things (IoT) across diverse applications underscore its pivotal role in seamlessly integrating physical devices to facilitate efficient data collection, analysis, and automation. Consequently, ensuring the Quality of Service (QoS) emerges as a critical imperative. While numerous studies have proposed methodologies focusing on resource optimization, network management, and data processing techniques, several previous approaches to QoS prediction may face constraints in the Internet of Things (IoT) environment, where the dynamic nature of IoT environments demands predictive models capable of adapting to varying conditions. Integrating user and service location data into QoS prediction models is paramount, as it enables personalized service delivery tailored to the user’s specific context, thus enhancing the user experience and overall system performance. This paper presents a novel approach to QoS prediction in IoT, harnessing self-attention and collaborative filtering (CF) techniques while incorporating location-based features such as distance from the user to the service. Experimental evaluations on benchmark datasets reveal that our proposed model improves prediction accuracy by up to 7 % compared to existing methods, underscoring its efficacy in enhancing QoS provisioning in IoT environments.

Improving Data Utility in Privacy-Preserving Location Data Collection via Adaptive Grid Partitioning

The widespread availability of GPS-enabled devices and advances in positioning technologies have significantly facilitated collecting user location data, making it an invaluable asset across various industries. As a result, there is an increasing demand for the collection and sharing of these data. Given the sensitive nature of user location information, considerable efforts have been made to ensure privacy, with differential privacy (DP)-based schemes emerging as the most preferred approach. However, these methods typically represent user locations on uniformly partitioned grids, which often do not accurately reflect the true distribution of users within a space. Therefore, in this paper, we introduce a novel method that adaptively adjusts the grid in real-time during data collection, thereby representing users on these dynamically partitioned grids to enhance the utility of the collected data. Specifically, our method directly captures user distribution during the data collection process, eliminating the need to rely on pre-existing user distribution data. Experimental results with real datasets show that the proposed scheme significantly enhances the utility of the collected location data compared to the existing method.

Highly Efficient Hybrid Reconfigurable Intelligent Surface Approach for Power Loss Reduction and Coverage Area Enhancement in 6G Networks

This paper introduces a novel efficient hybrid reconfigurable intelligent surface (RIS) approach designed to significantly reduce power loss and enhance coverage area in 6G networks. The core innovation of this approach lies in an advanced iterative algorithm introduced as the Hybrid reconfigurable intelligent surface decision-making algorithm (HRIS-DMA) that integrates precise user location data into the RIS configuration process. By dynamically adjusting RIS elements to reflect and direct signals based on real-time user positions, this method minimises signal attenuation and optimises signal propagation. The mechanism driving the performance gains includes precise beamforming and intelligent reflection, continuously refined through iterative updates. This technique ensures robust signal strength and expanded coverage, addressing the challenges of dense and diverse deployment scenarios in 6G networks. The proposed scheme’s application in 6G networks demonstrates substantial improvements in signal quality and network reliability, paving the way for enhanced user experiences and efficient communication infrastructures. This novel approach was tested using MATLAB R2023a , and its performance was evaluated using three downlink scenarios: zero to few, few to moderate, and moderate to many obstacles. The three scenarios show higher coverages than conventional simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) and base station (BS) handover. Based on the evaluation metrics, the analysis results of the novel HRIS-DMA show 70% less signal power loss, 0.17 μs less system delay, 25 dB and 12 dB channel gain compared with the conventional STAR-RIS and BS handover, respectively, and 95% improvement in the overall system’s efficiency compared to STAR-RIS and 13% compared to BS-BS handover.

A privacy-preserving location data collection framework for intelligent systems in edge computing

With the rise of smart city applications, the accessibility of users’ location data by smart devices has increased significantly. However, this poses a privacy concern as attackers can deduce personal information from the raw location data. In this paper, we propose a framework to collect user location data while ensuring local differential privacy (LDP) in the last-mile delivery system of Unmanned Aerial Vehicles (UAVs) within an edge computing environment. Firstly, we obtain the user location distribution Quad-tree by employing a region partitioning method based on Quad-tree retrieval in the specified data collection area. Next, the user location matrix is retrieved from the obtained Quad-tree, and we perturb the user location data using an LDP perturbation scheme on the location matrix. Finally, the collected data is aggregated using blockchain to evaluate the utility of the dataset from various regions. Furthermore, to validate the effectiveness of our framework in a real-world scenario, we conduct extensive simulations using datasets from multiple cities with varying urban densities and mobility patterns. These simulations not only demonstrate the scalability of our approach but also showcase its adaptability to different urban environments and delivery demands. Finally, our research opens new avenues for future work, including the exploration of more sophisticated LDP mechanisms that can offer higher levels of privacy without significantly compromising the quality of service. Additionally, the integration of emerging technologies such as 5G and beyond in the edge computing environment could further enhance the efficiency and reliability of UAV-based delivery systems, while also offering new challenges and opportunities for privacy-preserving data collection and analysis.

Deep learning-based glioma grading and feature visualization analysis

With the rapid development of geolocation information services, which involves large-scale location data querying, including map data and user location data. Traditional data storage and query methods face challenges of storage space and query efficiency, and the ability to process real-time geolocation data becomes critical in Geographic Information Services positioning systems that require servers to achieve millisecond-level response speeds. In addition to this, in geolocation services, for a given latitude and longitude information, it is necessary to quickly determine whether it exists or matches some specific location in the map service. The common traditional information finding algorithm in geographic location service is B-tree, and this paper proposes a geographic location information finding algorithm based on the Bloom filter, which mainly solves two problems. One is the storage problem of large amounts of latitude and longitude information; the other is how to convert the data stored in bytes level to bits level, which greatly reduces the space complexity and the inefficient information finding. The Bloom filter reduces the query time to linear time complexity O(n). The fastest query time for 5 million items of latitude and longitude information can reach 3.12 seconds. This article will illustrate the efficiency of the proposed method through a real-time traffic navigation scenario.

Research on the fast geolocation algorithm based on Bloom filter

With the rapid development of geolocation information services, which involves large-scale location data querying, including map data and user location data. Traditional data storage and query methods face challenges of storage space and query efficiency, and the ability to process real-time geolocation data becomes critical in Geographic Information Services positioning systems that require servers to achieve millisecond-level response speeds. In addition to this, in geolocation services, for a given latitude and longitude information, it is necessary to quickly determine whether it exists or matches some specific location in the map service. The common traditional information finding algorithm in geographic location service is B-tree, and this paper proposes a geographic location information finding algorithm based on the Bloom filter, which mainly solves two problems. One is the storage problem of large amounts of latitude and longitude information; the other is how to convert the data stored in bytes level to bits level, which greatly reduces the space complexity and the inefficient information finding. The Bloom filter reduces the query time to linear time complexity O(n). The fastest query time for 5 million items of latitude and longitude information can reach 3.12 seconds. This article will illustrate the efficiency of the proposed method through a real-time traffic navigation scenario.

Testing a Web-Based Food Ordering System with the Application of Geolocation

In the modern restaurant industry, there is intense competition to achieve customer satisfaction through Service Excellence or Service Differentiation. One service advantage that restaurants can offer is a Food Ordering System. The Self-Service Food Ordering System is a solution for restaurants to enhance the customer experience by providing the freedom to order food independently. It is crucial for restaurants to protect this system from external misuse, such as fraudulent orders that can cause financial losses. One solution that can be implemented is the utilization of Geolocation. Implementing Geolocation allows restaurants to obtain real-time user location data with the user's consent, thereby providing better protection for the self-service food ordering system. Testing is necessary to ensure the proper functioning of the system, including testing its features, compatibility, and the geolocation component.

CryptoDrive: A Decentralized Car Sharing System

Abstract: Carsharing initiatives can aid in addressing various urban issues by providing access to shared vehicles while restricting the use of private cars. With the growth of the Internet of Things, individuals can use their mobile devices to carry out basic tasks and access shared cars. However, such programs have security concerns, as confidential information, including user identification, location data, and access codes, is transmitted through public channels. This makes it possible for attackers to obtain this information for malicious purposes, underscoring the importance of developing a reliable authentication protocol to enhance security.

Maximizing privacy and security of collaborative indoor positioning using zero-knowledge proofs

The increasing popularity of wearable-based Collaborative Indoor Positioning Systems (CIPSs) has led to the development of new methods for improving positioning accuracy. However, these systems often rely on protocols, such as iBeacon, that lack sufficient privacy protection. In addition, they depend on centralized entities for the authentication and verification processes. To address the limitations of existing protocols, this paper presents a groundbreaking contribution to the field of wearable-based CIPSs. We propose a decentralized Attribute-based Authentication (ABA) protocol that offers superior levels of privacy protection, untraceability, and unlinkability of user actions. Unlike existing protocols that rely on centralized entities, our approach leverages decentralized mechanisms for authentication and verification, ensuring the privacy of user location data exchange. Through extensive experimentation across multiple platforms, our results demonstrate the practicality and feasibility of the proposed protocol for real-world deployment. Overall, this work opens up new avenues for secure and privacy-preserving wearable-based CIPSs, with potential implications for the rapidly growing field of Internet of Things (IoT) applications.

Improvement of blockchain-based multi-layer location data sharing scheme for Internet of Things

With the continuous development of the Internet of Things, user location data sharing has attracted increasing attention in various fields. However, current state-of-the-art data sharing system rely on centralized servers. This can create a single point of failure, with deleted data being unrecoverable. In addition, when performing data integrity verification, present schemes are only applicable to static scenarios. Dynamic update scenarios will incur enormous computational and communication costs. In this paper, we design a multi-layer location sharing scheme based on blockchain, which solves the single point of failure problem well. Our proposed scheme can also be adapted to dynamic scenarios. We utilize an accumulator to make our scheme more efficient in data updating and verification. The scheme also has a constant communication cost. Finally, we adopt a formal security analysis based on Real-Or-Random (ROR), and evaluate the performance of our scheme. The experimental results show that our scheme in comparison with existing methods, finding it to be superior in terms of computational cost, communication cost, safety, and efficiency.

Robot location privacy protection based on Q-learning particle swarm optimization algorithm in mobile crowdsensing.

In the recent years, with the rapid development of science and technology, robot location-based service (RLBS) has become the main application service on mobile intelligent devices. When people use location services, it generates a large amount of location data with real location information. If a malicious third party gets this location information, it will cause the risk of location-related privacy disclosure for users. The wide application of crowdsensing service has brought about the leakage of personal privacy. However, the existing privacy protection strategies cannot adapt to the crowdsensing environment. In this paper, we propose a novel location privacy protection based on the Q-learning particle swarm optimization algorithm in mobile crowdsensing. By generalizing tasks, this new algorithm makes the attacker unable to distinguish the specific tasks completed by users, cuts off the association between users and tasks, and protects users' location privacy. The strategy uses Q-learning to continuously combine different confounding tasks and train a confounding task scheme that can output the lowest rejection rate. The Q-learning method is improved by particle swarm optimization algorithm, which improves the optimization ability of the method. Experimental results show that this scheme has good performance in privacy budget error, availability, and cloud timeliness and greatly improves the security of user location data. In terms of inhibition ratio, the value is close to the optimal value.

We Know You Are Living in Bali: Location Prediction of Twitter Users Using BERT Language Model

Twitter user location data provide essential information that can be used for various purposes. However, user location is not easy to identify because many profiles omit this information, or users enter data that do not correspond to their actual locations. Several related works attempted to predict location on English-language tweets. In this study, we attempted to predict the location of Indonesian tweets. We utilized machine learning approaches, i.e., long-short term memory (LSTM) and bidirectional encoder representations from transformers (BERT) to infer Twitter users’ home locations using display name in profile, user description, and user tweets. By concatenating display name, description, and aggregated tweet, the model achieved the best accuracy of 0.77. The performance of the IndoBERT model outperformed several baseline models.

A Trajectory Privacy Protect Method Based on Location Pair Reorganization

With the rapid development of mobile Internet and communication technology, location-based services (LBS) are widely used in our daily life. The server stores a large amount of user location data, and these location data constitute user trajectories. If trajectory information on the server is leaked, it will seriously endanger users’ privacy. Trajectory k -anonymity technology is one of the most important methods to protect the privacy of user trajectory. However, current trajectory k -anonymity methods have less discussion on the semantic of stop point when selecting dummy trajectory, which leads to the fact that attacker can still exclude the dummy trajectory from the k -anonymity set and infer the real trajectory by combining background knowledge with the semantic information of stop points. To address this problem, this paper decomposes the real trajectory into location pairs set; the set consists of start-end points and stop points. According to the similarity of location pairs, the similar location pairs in history trajectory set are used to generate dummy trajectory: firstly, extracting the start-end points and stop points from real trajectory and assigning semantic to them. Then, based on the semantic, temporal, and geographical attributes, eligible location pairs are selected from history trajectory set to construct equivalence class. Finally, according to the location pairs in equivalence class, k − 1 dummy trajectories are generated to form a k -anonymity set. We evaluate our method thoroughly with real dataset. The results show that our method achieve an effective data availability and higher privacy protection than other methods.

Using mHealth Technologies to Promote Public Health and Well-Being in Urban Areas with Blue-Green Solutions.

European and International cities face crucial global geopolitical, economic, environmental, and other changes. All these intensify threats to and inequalities in citizens' health. The implementation of Blue-Green Solutions in urban and rural areas have been broadly used to tackle the above challenges. The Mobile health (mHealth) technologies contribution in people's well-being has found to be significant. In addition, several mHealth applications have been used to support patients with mental health or cardiovascular diseases with very promising results. The patients' remote monitoring can be a valuable asset in chronic diseases management for patients suffering from diabetes, hypertension or arrhythmia, depression, asthma, allergies and others. The scope of this paper is to present the specifications, the design and the development of a mobile application which collects health-related and location data of users visiting areas with Blue-Green Solutions. The mobile application has been developed to record the citizens' and patients' physical activity and vital signs using wearable devices. The proposed application can also monitor patients physical, physiological, and emotional status as well as motivate them to engage in social and self-caring activities. Additional features include the analysis of the patients' behavior to improve self-management. The "HEART by BioAsssist" application could be used as a health and other data collection tool as well as an "intelligent assistant" to monitor and promote patient's physical activity.

Understanding service connectivity based on digital serendipity: An actor-network approach

The term smart city is an emerging urban concept that provides optimal public services in a city as a hybrid combination of people, technology, and place. However, previous research does not consider various factors and either depends on technology development or studies of human and non-human factors separately. In addition, even if the relationship between human and non-human elements is analyzed, it is difficult to realize the analyzed content as a service for real users. The actor-network theory (ANT), established by Latour, is a theory that provides a deep understanding of the underlying mechanisms of smart cities by illustrating how the networks of human and non-human actors create the weave of unpredictable connections. We call this digital serendipity when it is realized as a service from this network. This study aims to analyze the actor-network, where digital serendipity can occur, and apply it to increase the possibility of service formation. To this end, we collect the location data of users on a campus as a living lab and analyze the network between users and buildings. Further, by applying the results of the analyzed network, we develop a web-based application as an example where digital serendipity can be implemented for voluntary service formation. To verify the service connectivity, we conducted a test utilizing library book rental data.

Using Tencent User Location Data to Modify Night-Time Light Data for Delineating Urban Agglomeration Boundaries

The study of urban agglomeration boundaries is helpful to understand the internal spatial structure of urban agglomeration, evaluate the development level of urban agglomeration, and thus, assist in the formulation of regional planning and policies. However, previous studies often used only static spatial elements to delineate the boundaries of urban agglomerations, ignoring the spatial connections within urban agglomerations. In this study, night-time light and Tencent user location data were evaluated separately and fused to delineate urban agglomeration boundaries from both static and dynamic spatial perspectives. Additionally, it has been shown in the study results that the accuracy of urban agglomeration boundary delineated by night-time light data is 84.90%, with Kappa coefficient as 0.6348. The accuracy delineated by Tencent user location data is 82.40%, with Kappa coefficient as 0.5637, while the accuracy delineated by data fusion is 92.70%, with Kappa coefficient as 0.7817. Therefore, it can be concluded that the fusion of night-time light and Tencent user location data had the highest accuracy in delineating urban agglomeration boundaries, which verified that the fusion of dynamic spatial elements on a single static spatial element can supplement the spatial connection of urban agglomeration. Our findings enrich the understanding of urban agglomerations, and the accurate delineation of urban agglomerations boundaries can aid urban agglomeration planning and management.

On the privacy protection of indoor location dataset using anonymization

Indoor positioning is becoming more popular with increasing user demands on Location-based Services (LBS) and Social Networking Services (SNS). Location fingerprinting is widely deployed in indoor localization, and extensive daily use of different kinds of mobile devices in indoor environments results in generating a huge amount of location data of people. However, publishing this location based dataset by Location Service Provider (LSP) to any third party, such as researchers or commercial organizations, potentially threatens users’ privacy. Several studies have been done to anonymize the user location data, but protecting the users’ information against various attacks in an indoor environment is still a challenging issue. This paper proposes a novel framework for publishing privacy preserving location data employing five anonymization techniques: k-anonymity, ℓ-diversity, t-closeness, (α,k)-anonymity, and δ-presence. In the proposed framework, although the LSP cannot find the exact location of the users, it can provide them online location services at the same time and publish the anonymized privacy protected dataset afterward. The practical feasibility of applying the proposed framework is verified on both simulated and two real-world datasets. The results indicate that the published location dataset can protect the identity and location information of users while providing location information for third parties for further purposes.