User Location Research Articles

Research on the Factors That Influence and Improve the Quality of Informal Learning Spaces (ILS) in University Campus

Currently, with the intensification of global education competition, how to cultivate innovative and versatile talents has become the focus of education today. Informal learning spaces (ILS) have received widespread attention due to their benefits for interdisciplinary interaction. Many forms of ILS have been built, and coffee shops are one of them. However, there is no uniform standard for the design of cafes as ILS, and there are many problems with the spatial quality. This study took a university cafe as a case study and conducted research on the environmental quality and influencing factors from the perspective of environmental behavior. In terms of research methods, this study adopted a combination of ultra-wideband positioning, questionnaire surveys, and environmental data measurements. This research method could obtain the complete relationship between user behavior, time, and location, which improved the shortcomings of traditional research methods of fragmented and accidental data acquisition, and helped to obtain more accurate research results. The study identified the impact proportions of four criteria layer elements and 26 scheme layer factors, which was also the main contribution of this study. In terms of four criteria layer elements, the physical elements had the highest proportion, accounting for 32.46%. The ontology elements ranked second, accounting for 27.07%. The atmosphere elements ranked third, accounting for 24.19%. Finally, facility elements accounted for 16.29%. The top three scheme layer factors were equipment and facilities, noise, and illumination, with weights of 11.18%, 10.71%, and 8.35%, respectively. The study summarized the development directions of cafes as ILS, such as considering the learning needs of different groups, installing sound-absorbing and soundproof devices, adding grilles to external windows, and improving glare problems.

Leveraging quantum blockchain for secure multiparty space sharing and authentication on specialized metaverse platform

In the era of digital transformation, securing data on metaverse platforms poses significant challenges. This paper proposes Multiparty Space Sharing and Authentication (MSSA), a novel approach for secure user login and location access control within specialized metaverse platforms. MSSA leverages Quantum Multiparty Secret Computation (QMSC) integrated with a quantum blockchain network. This integration facilitates user verification within the presence of potentially untrusted metaverse authority. The underlying quantum blockchain employs a Delegated Proof-of-Stake (DPoS) consensus mechanism with a Borda voting scheme for authority node selection. By harnessing the principles of quantum cryptography, MSSA offers enhanced security against both classical and anticipated future quantum attacks. This research demonstrates the feasibility and potential of quantum blockchain for securing metaverse platforms, paving the way for secure and decentralized digital ecosystems.

Policy interventions and urban characteristics in modeling electric vehicle charging infrastructure utilization

The surge in electric vehicles adoption necessitates understanding the impact of policy interventions on public electric vehicle charging infrastructure in urban areas. This research investigates the influence of pricing frameworks on the usage of public charging facilities by analyzing both behavioral and spatial attributes of these infrastructures. Utilizing open data from Palo Alto, United States, this study employs descriptive statistical methods and interpretable machine learning approaches to scrutinize the relationship between policy initiatives and charging behaviors. The analysis underscores the significance of spatial attributes on charging behaviors. Policy interventions yield noticeable alterations in charging metrics, with locations near commercial hubs showing higher utilization, while local and frequent users resist fee adjustments. The research emphasizes the necessity for customized strategies to optimize infrastructure development and management, offering a framework for policymakers and stakeholders in sustainable urban transportation. Future research should explore similar interventions in diverse urban settings using real-time data and advanced optimization techniques to better tailor policies to the unique characteristics of specific facilities.

Pedestrian Path Choice in Crowded Indoor Environments

Abstract. Indoor crowds can be regarded as dynamic obstacles and they could impede the movement of pedestrians. For instance, the scope of crowds could significantly impact accurate path choices. There are few discussions from an individual’s perspective to reveal the decision-making process of reasonable path choices. In this paper, we aim to address how an indoor pedestrian would walk in a crowded indoor environment. We propose a method to simulate a pedestrian’s path choices during wayfinding process. It is wrapped in a workflow that can capture near-real-time crowd uncertainty. First, the measurement errors of crowds are alleviated with a Bayesian filter. According to crowd locations at each moment, the method of Kernel Density Estimation (KDE) is applied to a grid model of the building to obtain the uncertainty map of the crowds. Based on a two-level spatial model, the logical path represented by a room sequence can be derived from a ’room-door’ network, while detailed path choices on the ’operational’ level are refined in each room. By considering both crowd uncertainty and time cost, path choices for a user are determined by using the A* algorithm at each time slot. By iterating the optimal path to the next door/the target in the current room, the user location of the next moment can be inferred. Our test initially validated the feasibility of the method on the path choices of a pedestrian. In the future, we plan to further conduct field tests with different users in crowded indoor environments.

Implementation of the Haversine Formula Method in Geographic Information Systems for Searching the Nearest Sea Freight Expedition Services in East Jakarta

In export and import activities, a partner is needed for the shipping process. There are three types of shipping routes available for export and import: land, sea, and air. A company that offers shipping services via sea routes is known as an EMKL. This study designs a Geographic Information System (GIS) to facilitate the search for locations of Marine Cargo Expedition Services (EMKL) in East Jakarta using the Haversine Formula. The main problem faced is the difficulty in finding nearby and relevant expedition service providers in a large and densely populated area like East Jakarta. The proposed solution involves developing a system that integrates location data with the Haversine Formula to accurately calculate the distance between the user's location and the service providers. By using this method, the system can provide precise location information and help users select the nearest expedition service. The goal of this research is to enhance efficiency and accuracy in locating expedition services. The expected outcome is the creation of a user-friendly and effective system that simplifies the process of selecting expedition services by considering distance and service availability in real-time.

Deep deterministic policy gradient caching method for privacy protection in Internet of Vehicles

To address the problem of low cache hit ratio in edge nodes for privacy-preserving in the Internet of Vehicles (IoV), a deep deterministic policy gradient caching (DDPGC) method was proposed. First, a taxi certified by a trusted authority acted as a second-level caching edge node to acquire hotspot data and store it in the local cache. It then broadcasted this information to the neighboring service requesting vehicles (SRVs). SRVs cached the broadcasted data locally and search for service requests in the order of priority of local cache, taxi, and cloud server when such requests arise. Secondly, a neural network was deployed in taxis and SRVs to maximize the caching benefit through deep reinforcement learning for decision replacement of their cached data. Finally, when SRVs were located in vehicle sparsity and could not obtain request data from neighboring vehicles, a combination of k-anonymity and random response perturbation mechanisms generated anonymity sets to send requests to cloud servers in an anonymous manner to obtain services while protecting user location privacy. Simulation experimental results show that DDPGC can effectively improve the vehicle cache hit ratio, reduce the frequency of SRV interaction with the cloud server, and effectively protect user privacy security.

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.

Development and Evaluation of the Telehealth in Motor Neuron Disease System: The TIME Study Protocol.

For more responsive care provision for motor neuron disease and caregivers, a digital system called Telehealth in MND-Care (TiM-C) was created. TiM-C sends regular symptom questionnaires to users; their responses are sent to health care professionals (HCPs). To enable people with motor neuron disease to participate in research studies more easily, a parallel platform was developed from TiM-C, called Telehealth in MND-Research (TiM-R). TiM-R can advertise studies, collect data, and make them available to MND researchers. This study has 4 work packages (WPs) to facilitate service approval, codevelop the TiM systems, and evaluate the service. Each WP aims to understand (1) what helps and hinders the approval of the TiM-C system as a National Health Service; (2) what aspects of MND care and research are currently unmet and can be addressed through the TiM-C and TiM-R systems; (3) how TiM-C influences MND care, from the perspective of people with motor neuron disease, their caregivers, and HCPs; and (4) the costs and benefits associated with TiM-C. WP1 will use semistructured interviews with 10-15 people involved in the approval of TiM-C to understand the barriers and facilitators to governance processes. WP2 will use individual and group interviews with 25-35 users (people with motor neuron disease, caregivers, HCPs, MND researchers, and industry) of TiM-C and TiM-R to understand the current unmet needs of these user groups and how TiM services can be developed to meet these needs. WP3 will use a process evaluation involving 5 elements; local context, engagement, user experiences, service impact, and mechanisms of action. A range of methods, including audits, analysis of routine data, questionnaires, interviews, and observations will be used with people with motor neuron disease, caregivers, and HCPs, both those using the system and those who declined the service when invited. WP4 will use data collected through the process evaluation and known costs to conduct a cost-consequence and budget impact analysis to explore the cost-benefit of the TiM-C service. Most data collected will be qualitative, with thematic and framework analysis used to develop themes from transcripts and observations. Descriptive statistics or t tests and chi-square tests will be used to describe and analyze quantitative data. This study has received ethical approval and has begun recruitment in 1 site. Further, 13 specialist MND centers will adopt TiM-C and the TIME study, beginning in July 2024. The study will conclude in November 2026 and a final report will be produced 3 months after the completion date. This study will facilitate the implementation and development of TiM-C and TiM-R and fully evaluate the TiM-C service, enabling informed decision-making among health care providers regarding continued involvement and contribute to the wider literature relating to how technology-enabled care services can affect clinical care. DERR1-10.2196/57685.

Investigation on Thermal Performance in Japan’s Local Station and Shopping District during Cold Season: Study Case of Kurosaki Arcade

Abstract This research delves into the spatial and thermal comfort conditions within the Kurosaki Shopping District, a market with a century-long history in Kitakyushu, Fukuoka. Despite the existing uncomfortable thermal conditions during winter, users actively engage in outdoor activities such as selling and opening stores. This research aims to understand the condition that the local user had to frequently withstand in the semi outdoor and outdoor area of Kurosaki District station and market’s urban area. Identifying issues related to thermal comfort in the outdoor areas, the study focuses on measuring and analyzing crucial parameters including air velocity, humidity, temperature, and spatial conditions. The method of data gathering uses Anemometer, Thermal recorder, and Globe Thermometer are utilized in 5 selected public areas that is actively frequented by local users. The research employs a comparative analysis method, juxtaposing the collected data with findings from literature reviews on standards and general comfort temperatures. The analysis reveals that temperature fluctuations are influenced by geographical factors and activity levels. The lack of building density contributes significantly to deviations in temperature and humidity from established standards. The study highlights the importance of addressing climatic conditions to enhance comfort in open spaces, especially in historically significant markets with extended operational histories. This research is expected to help policy makers understand the conditions of the local users and gather data for further research in the future.

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.

Transmission scheme for collaborative‐RIS‐aided NOMA system with multiple channels

AbstractThis paper investigates the combination of collaborative reconfigurable intelligent surface (RIS) with non‐orthogonal multiple access (NOMA) to enhance the quality of service (QoS) in communication systems. By introducing collaborative RIS, a virtual line‐of‐sight (LoS) link can be established between the base station (BS) and the users. In this article, the aim is to minimize the total transmit power under the constraint of each user's QoS by optimizing the phase shifts and the power allocation coefficients. To proceed, two approaches are proposed with different complexities to solve the formulated power minimization problem. Specifically, in the first method, a sub‐channel allocation scheme based on the user locations is developed, and the phase shifts are optimized to obtain the minimum transmit power for fixed channel assignment, which is further addressed by using the alternating optimization and the sequence rotation methods. In the second approach, a joint optimization algorithm that achieves the channel assignment vectors and the phase shift coefficients in an alternative manner, and a sub‐channel assignment algorithm based on the equivalent channel gain is developed. The simulation results show that the proposed transmission scheme has the capability of improving system performance significantly compared with the conventional schemes.

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.

Privacy Slider: Fine-Grain Privacy Control for Smartphones

Today, users are constrained by binary choices when configuring permissions. These binary choices contrast with the complex data collected, limiting user control and transparency. For instance, weather applications do not need exact user locations when merely inquiring about local weather conditions. We envision sliders to empower users to fine-tune permissions. First, we ran two online surveys (N=123 & N=109) and a workshop (N=5) to develop the initial design of Privacy Slider. After the implementation phase, we evaluated our functional prototype using a lab study (N=32). The results show that our slider design for permission control outperforms today's system concerning all measures, including control and transparency.

Salp Swarm Algorithm-Based Kalman Filter for Seamless Multi-Source Fusion Positioning with Global Positioning System/Inertial Navigation System/Smartphones

With the rapid development of high-precision positioning service applications, there is a growing demand for accurate and seamless positioning services in indoor and outdoor (I/O) scenarios. To address the problem of low localization accuracy in the I/O transition area and the difficulty of achieving fast and accurate I/O switching, a Kalman filter based on the salp swarm algorithm (SSA) for seamless multi-source fusion positioning of global positioning system/inertial navigation system/smartphones (GPS/INS/smartphones) is proposed. First, an Android smartphone was used to collect sensor measurement data, such as light, magnetometer, and satellite signal-to-noise ratios in different environments; then, the change rules of the data were analyzed, and an I/O detection algorithm based on the SSA was used to identify the locations of users. Second, the proposed I/O detection service was used as an automatic switching mechanism, and a seamless indoor–outdoor localization scheme based on improved Kalman filtering with K-L divergence is proposed. The experimental results showed that the SSA-based I/O switching model was able to accurately recognize environmental differences, and the average accuracy of judgment reached 97.04%. The localization method achieved accurate and continuous seamless navigation and improved the average localization accuracy by 53.79% compared with a traditional GPS/INS system.

Floor-Level Occupancy Estimation of a Multi-Story Building Using Coarse Wi-Fi Data

In recent years, there has been an extraordinary increase in wireless capable devices and network infrastructure, which spawned a corresponding rise in data produced from the interactions of these technologies. Mobile devices constantly roam, leading to a perpetual dialog between a mobile device and wireless access points. This dialogue generates a continuous stream of device-specific data, including but not limited to a device’s media access control address, time of access, and received signal strength. Given the knowledge of the access point’s location and received signal strength, it is possible to infer the position of user devices and estimate their mobility and occupancy. This article presents two methods for accurately measuring floor-level occupancy in a multi-story building at Texas State University using coarse Wi-Fi log data. The first method employs a static filter, while the second incorporates user-role data and user location to create a dynamic filter. Quantitative methods are used to evaluate these filters against field-collected reference data and existing internal people-counting sensors. Our results demonstrate that the dynamic filter, leveraging variable thresholds, provides a more accurate estimation of occupancy compared to the fixed 5-min static filter which consistently overestimated occupancy. This research sheds light on the potential of dynamic filters derived from user-role data for precise floor-level occupancy estimations, with implications for various applications.

Protecting unauthenticated messages in LTE/5G mobile networks: A two-level Hierarchical Identity-Based Signature (HIBS) solution

As an essential public infrastructure, the security and reliability of mobile networks have a profound impact on people’s production and life. Although the security of LTE/5G networks has been improved a lot with the evolution of standards, there are still some unprotected messages being transmitted between the cellular network and device due to the symmetric key-based security architecture and the trade-off between security and other criteria like network availability. By exploiting these messages, various security attacks have been proposed and demonstrated against commercial mobile networks and devices in existing literature, such as user location tracking, bidding-down, and DoS attacks. To address this security issue, in this paper, we aim to protect these unauthenticated messages in mobile networks using digital signatures. Based on the idea of Hierarchical Identity-Based Signature (HIBS) in existing work, we analyse and design a two-level HIBS solution in detail in terms of different aspects such as keys generation and provisioning procedures, replay mitigation, and cell selection. Unlike previous work, our proposed solution also supports the protection of individual vulnerable RRC and NAS layer signalling in addition to authenticating the base station. We evaluated the efficiency and feasibility of several existing HIBS schemes and implemented the most efficient one in the 5G standalone network setup using open-source software. The implementation results further proved the feasibility of the solution in practice.

AoU-Based Local Update and User Scheduling for Semi-Asynchronous Online Federated Learning in Wireless Networks

AoU-Based Local Update and User Scheduling for Semi-Asynchronous Online Federated Learning in Wireless Networks

Influence of Indian Chettiyar community heritage on Chettie house architecture of Malaysia

The Indian Chettinadu cultural legacy was introduced to Malaysia by Indian merchants who made their home there. The aim of this research is to examine the impact of Indian Chettinadu culture on the architecture of Chetti Melaka houses in Malaysia, as well as its continued significance in the modern era. A qualitative approach is used. There was observation, documentation, and open-ended interviewing. There is documentation about the Chetti's residence at Gajah Berang, Melaka, Malaysia, and the Chettiyar residence in Pallathur, Karaikudi, India. To comprehend how users, Chettiyar culture, and architecture relate to one another, the data triangulation method is used. The findings demonstrate how Indian Cettinadu culture has influenced Malaysian home design. But there is an enormous concession made about the size of the house and intricate decorations. In both situations, the local climate and user requirements have an impact on the house's design. The study's findings are used to propose house design guidelines that might be used in a modern setting.

E-Waste Facility Locator using Integrated Map Interface

The E-Waste (Electronic Waste) Facility Locator application utilizes the user's current location to help find a nearby e-waste disposal center. The application features a user-friendly map interface that allows the user to select an e-waste facility based on their preferences. The user can either go to the facility to dispose of the e-waste or request the facility to pick up the waste at their doorstep. The entire system is managed by a system admin. Overall, the E-waste Facility Locator app helps users dispose of electronic waste responsibly, reducing pollution and conserving resources.

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.