User Location Research Articles

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 User Location Reset Method through Object Recognition in Indoor Navigation System Using Unity and a Smartphone (INSUS)

A User Location Reset Method through Object Recognition in Indoor Navigation System Using Unity and a Smartphone (INSUS)

Tourism Guide Application for Heritage Sites in Surabaya Based on Augmented Reality and GPS Gamification

One of the most important revenue streams for the nation's economy and development is tourism. The COVID-19 pandemic is, however, causing a reduction in tourism in Indonesia; Surabaya, the country's second-largest city, has seen a drop in visitors from 6.5 million to 500 thousand. To solve this problem, an innovation is required to boost the number of visitors to Indonesia's heritage sites who use contemporary technology, specifically smartphone with interactive media (with AR and GPS for instance). The author will develop an app that, depending on the user's location, will let them visit several Surabaya heritage sites and provide them with comprehensive details and instructions. Indonesia can stimulate its economy and return visitors to its heritage sites by employing this interactive gamification apps. However, on the examination to the user, we found a good implementation in detecting and measuring the nearest destination to the current user position, and how to parse into google map and guiding the destination to that heritage site. But we have some distraction when pointing the apps to the Augmented Reality (AR) marker on the site, since various historical places have various lighting, some devices are unable to take a clear picture of the marker due to its lack of lighting and abnormal size or form. Thus, it will be effective when the user points his smartphone camera to the AR marker, he/she supposed to use the smartphone lighting as well.

Mental health service user organizations in times of crises: adaptions, challenges and opportunities experienced by local associations during COVID-19

ABSTRACT Purpose This study examines how local user associations of mental health service-user organizations were affected by the pandemic, in order to provide guidance to user organizations and surrounding actors on future advancements. The pandemic is used as a case to explore organizational resilience and digitalization during crisis. Methods Data from focus group interviews and individual interviews with representatives of ten local associations were analysed using qualitative content analysis. A theoretical framework combining governance theory and organizational theory was applied. Results Typically, associations swiftly restructured activities to support members to meet the urgent needs that arose, not least in relation to the digital transition. Simultaneously, face-to-face interactions was valued and some members became isolated. Public sector actors often did not prioritize collaboration, and the associations had limited agency and influence in advocacy activities. Conclusions User organizations can play an important role in times of crisis. Surrounding social systems should provide resources to counteract resource dependencies and allow organizations to develop operating reserves. They should value collaboration and establish collaborative practices to ensure a readiness to utilize the organizations’ capacities when needed. User organizations should have control over future developments, both to harness the potential of digital connectivity and to prevent a digital divide.

Improving Spatio-Temporal Topic Modeling with Swarm Intelligence

This study introduces innovative methodologies for spatiotemporal topic modeling applied to the TripAdvisor forum of Morocco, leveraging the diverse and geographically tagged user-generated content. We develop and evaluate two schemas integrating Latent Dirichlet Allocation (LDA) with advanced clustering techniques, including a hybrid K-Means algorithm that incorporates Genetic Algorithms and the Artificial Bee Colony method. The first schema independently processes user threads, publication times, and locations using LDA, followed by clustering, while the second schema combines these dimensions into a unified vector for holistic LDA application, facilitating direct comparisons of clustering efficacy. Our findings demonstrate that swarm intelligence significantly boosts clustering performance, especially for larger clusters, and enhances the visualization of complex data relationships. These insights offer actionable intelligence for tourism stakeholders and underscore the practical benefits of advanced computational techniques in harnessing user-generated content for strategic decision-making.

Prediction of influential nodes in social networks based on local communities and users’ reaction information

Identifying influential nodes is one of the basic issues in managing large social networks. Identifying influence nodes in social networks and other networks, including transportation, can be effective in applications such as identifying the sources of spreading rumors, making advertisements more effective, predicting traffic, predicting diseases, etc. Therefore, it will be important to identify these people and nodes in social networks from different aspects. In this article, a new method is presented to identify influential nodes in the social network. The proposed method utilizes the combination of users’ social characteristics and their reaction information to identify influential users. Since the identification of these users in the large social network is a complex process and requires high processing power and time, clustering and identifying communities have been used in the proposed method to reduce the complexity of the problem. In the proposed method, the structure of the social network is divided into its constituent communities and thus the problem of identifying influential nodes (in the entire network) turns into several problems of identifying an influential node (in each community). The suggested method for predicting the nodes first predicts the links that may be created in the future and then identifies the influential nodes based on an iterative strategy. The proposed algorithm uses the criteria of centrality and influence domain to identify this category of users and performs the identification process both at the community and network levels. The efficiency of the method has been evaluated using real databases and the results have been compared with previous works. The results demonstrate that the proposed method provides a more suitable performance in detecting the influential nodes and is superior in terms of accuracy, recall and processing time.

Next location prediction using heterogeneous graph-based fusion network with physical and social awareness

Location prediction based on social media information is highly valuable in human mobility research and has multiple real-life applications. However, existing research methods often ignore social influences, largely ignoring implicit information regarding interactions between users and geographical locations. Additionally, they generally employ single modeling structures, which restricts the effective integration of complex spatiotemporal characteristics and factors influencing user mobility. In this context, we propose a novel network with physical and social awareness that expresses both physical and social influences of user mobility from a global perspective based on a heterogeneous graph constructed using users and spatial locations as nodes and relationships between them as edges. This graph enables the model to leverage information from connected nodes and edges to infer missing or unobserved data. The model predicts future locations of users by effectively integrating the temporal and spatial features of user trajectory series. The proposed model is validated using three social media datasets. The experimental results demonstrate that the proposed method outperforms the state-of-the-art baseline models. This indicates the importance of considering complex interactions between users and locations, as well as the various influences of physical and social spaces.

TUMDOT–MUC: Data Collection and Processing of Multimodal Trajectories Collected by Aerial Drones

Currently available trajectory data sets undoubtedly provide valuable insights into traffic events, the behavior of road users and traffic flow theory, thus enabling a wide range of applications. However, there are still shortcomings that need to be addressed: (i) the continuous temporal recording (ii) of a coherent area covering several intersections (iii) with the detection of all road users, including pedestrians and cyclists. Therefore, this study focuses on the design of a large-scale aerial drone observation in the city of Munich, Germany, as well as the processing steps and the description of the resulting data set. Using twelve camera-equipped, unmanned aerial drones, the observation monitored an inner urban road section with a length of 700 m continuously for several hours during the afternoon peak hours on two working days. The trajectories of all road users were then extracted from the videos and post-processed in order to obtain a coherent and accurate data set. The resulting trajectories contain information on the category, dimensions, location, velocity, acceleration and orientation of each road user at each frame, merged continuously in time and space across several drone observation areas and subsequent time slots. The data, therefore, includes various interactions between different modes of motorized traffic and active mobility users like pedestrians and cyclists. The whole data set and the supporting data are available open source for research purposes to ensure global accessibility.

Dynamic SNR, Spectral Efficiency, and Rate Characterization in 5G/6G mmWave/sub-THz Systems with Macro- and Micro-Mobilities

The performance of 5G/6G cellular systems operating in millimeter wave (mmWave, 30–100 GHz) and sub-terahertz (sub-THz, 100–300 GHz) bands is conventionally assessed by utilizing the static distributions of user locations. The rationale is that the use of the beam tracking procedure allows for keeping the beams of a base station (BS) and user equipment (UE) aligned at all times. However, by introducing 3GPP Reduced Capability (RedCap) UEs utilizing the Radio Resource Management (RRM) Relaxation procedure, this may no longer be the case, as UEs are allowed to skip synchronization signal blocks (SSB) to improve energy efficiency. Thus, to characterize the performance of such UEs, methods explicitly accounting for UE mobility are needed. In this paper, we will utilize the tools of the stochastic geometry and random walk theory to derive signal-to-noise ratio (SNR), spectral efficiency, and rate as an explicit function of time by accounting for mmWave/sub-THZ specifics, including realistic directional antenna radiation patterns and micro- and macro-mobilities causing dynamic antenna misalignment. Different from other studies in the field that consider time-averaged performance measures, these metrics are obtained as an explicit function of time. Our numerical results illustrate that the macro-mobility specifies the overall trend of the time-dependent spectral efficiency, while local dynamics at 1–3 s scales are mainly governed by micro-mobility. The difference between spectral efficiency corresponding to perfectly synchronized UE and BS antennas and time-dependent spectral efficiency in a completely desynchronized system is rather negligible for realistic cell coverages and stays within approximately 5–10% for a wide range of system parameters. These conclusions are not affected by the utilized antenna array at the BS side. However, accounting for realistic radiation patterns is critical for a time-dependent performance analysis of 5G/6G mmWave/sub-THz systems.

Negotiating Hegemonic Masculinities and Mutual Exoticization within Grindr Tourism Interactions: “Everyone in Tel Aviv is a Muscle God”

ABSTRACT Dating apps are spaces where masculinities are communicated digitally, affecting inequalities around gender expressions. This study aims to identify the masculinities communicated within Grindr tourism interactions in Tel Aviv and analyze how masculinities shape relations among international tourists and locals. Methods consist of interviews and audio diaries with nineteen tourist, local, and immigrant Grindr users in Tel Aviv. Theories of affordances, hegemonic masculinities, and relational sociology are used to analyze the data. In discussion of the results, the concept of mutual exoticization is put forth to interpret the social dynamics valued within Grindr tourism interactions, thereby revealing oppositional fantasies tied to nationalities and masculinities. The study also explores ethnicity’s association with social and erotic capital in digital circulations of a localized Mizrahi masculinity. Findings reveal that while hegemonic masculinities are circulated on Grindr, resistance is also a part of Grindr tourism relations around masculinities. This research concludes by shedding light on masculinity inequalities within digital spaces where different nationalities co-congregate, revealing everyday negotiations of gender regimes. Grindr is the site of both reproduction of and resistance to social inequalities around masculinities.

IoT Privacy Risks Revealed.

A user's devices such as their phone and computer are constantly bombarded by IoT devices and associated applications seeking connection to the user's devices. These IoT devices may or may not seek explicit user consent, thus leaving the users completely unaware the IoT device is collecting, using, and/or sharing their personal data or, only marginal informed, if the user consented to the connecting IoT device but did not read the associated privacy policies. Privacy policies are intended to inform users of what personally identifiable information (PII) data will be collected about them and the policies about how those PII data will be used and shared. This paper presents novel tools and the underlying algorithms employed by the Personal Privacy Assistant app (UTCID PPA) developed by the University of Texas at Austin Center for Identity to inform users of IoT devices seeking to connect to their devices and to notify those users of potential privacy risks posed by the respective IoT device. The assessment of these privacy risks must deal with the uncertainty associated with sharing the user's personal data. If privacy risk (R) equals the consequences (C) of an incident (i.e., personal data exposure) multiplied by the probability (P) of those consequences occurring (C × P), then efforts to control risks must seek to reduce the possible consequences of an incident as well as reduce the uncertainty of the incident and its consequences occurring. This research classifies risk according to two parameters: expected value of the incident's consequences and uncertainty (entropy) of those consequences. This research calculates the entropy of the privacy incident consequences by evaluating: (1) the data sharing policies governing the IoT resource and (2) the type of personal data exposed. The data sharing policies of an IoT resource are scored by the UTCID PrivacyCheck™, which uses machine learning to read and score the IoT resource privacy policies against metrics set forth by best practices and international regulations. The UTCID Identity Ecosystem uses empirical identity theft and fraud cases to assess the entropy of privacy incident consequences involving a specific type of personal data, such as name, address, Social Security number, fingerprint, and user location. By understanding the entropy of a privacy incident posed by a given IoT resource seeking to connect to a user's device, UTCID PPA offers actionable recommendations enhancing the user's control over IoT connections, interactions, their personal data, and, ultimately, user-centric privacy control.

Edge computing collaborative offloading strategy for space‐air‐ground integrated networks

SummaryDue to geographical factors, it is impossible to build large‐scale communication network infrastructure in remote areas, which leads to poor network communication quality in these areas and a series of delay‐sensitive tasks cannot be timely processed and responded. Aiming at the problem of limited coverage in remote areas, the space‐air‐ground integrated networks (SAGIN) combined with mobile edge computing (MEC) can provide low latency and high reliability transmission for offloading delay‐sensitive tasks for users in remote areas. Considering the strong limitation of satellite resources in the space‐ground integrated network and insufficient energy of local user equipment, firstly, a satellite‐UAV cluster‐ground three‐layer edge computing network architecture is proposed in this paper. Under the condition that the delay requirements of various ground tasks are met, the task offloading problem is transformed into a Stackelberg game between ground user equipment and edge servers. In addition, it is proved that the existence of Nash equilibrium in non‐cooperative game between ground user equipment by using potential game. Finally, a Nash equilibrium iterative offloading algorithm based on Stackelberg game (NEIO‐SG) is proposed to find the optimal offloading strategy for tasks to minimize the system offloading cost and the optimal forwarding percentage strategy for offloading tasks to maximize the utility function of the edge server. Simulation results show that compared to other baseline algorithms, NEIO‐SG reduces the total system latency during task offloading by about 13 and the energy consumption of the edge server by about 35.

Multi-strategy improved sand cat optimization algorithm-based workflow scheduling mechanism for heterogeneous edge computing environment

Edge computing is one of the predominant technologies which facilitates the option of bringing out the computing resources closer to the location of the end users when they are utilized by them. This facility offered by edge computing technology need to reduce the utilization of network bandwidth and response time with respect to the user’s workflow. In this paper, Multi-Strategy Improved Sand Cat Swarm Optimisation Algorithm (MSISCSOA)-based workflow scheduling mechanism is proposed for handling the challenges of workflow scheduling in cloud-edge computing environment. The core objective of this MSISCSOA-based workflow scheduling algorithm targets on minimizing the execution latency and energy consumption to facilitate timely and on-demand end users’ satisfaction of resources. This MSISCSOA scheme is adopted with the improvement introduced using random variation and elite collaborative strategies, such that well-balanced the trade-off between exploration and exploitation is achieved. This improvement is introduced over Sand Cat Optimization Algorithm (SCOA) using the merits of dynamic random search and joint opposite selection strategies that accelerates the convergence of the algorithm with increased global optimization and searching efficiency. It specifically improved SCOA using random variation for escaping from the local point of optimality. It also used well distributed pareto fronts and population evolution multi-strategy that aids in searching solutions with maximized diversity. The simulation experiments conducted using the datasets of Montage, Cybershake, LIGO and SIPHT an average confirmed minimized execution latency of 21.38 % and energy consumptions of 19.56 %, better than the baseline Ant Colony Optimization Algorithm-Based Workflow Scheduling (IACOAWS), Quadratic Penalty Function-based Particle Swarm Optimization Algorithm (QPF-PSOA), Biogeography Optimization (BBO) Algorithm based Multi-Objective Task Scheduling (BBOAMOTS) and Different Evolution-based Task Clustering and Scheduling (DETCS) approaches used for comparative investigation.

The Role of Transparency in Digital Contact Tracing During COVID-19: Insights from an Expert Survey.

Health technologies such as apps for digital contract tracing [DCT] played a crucial role in containing and combating infections during the COVID-19 pandemic. Their primary function was to prevent the spread of SARS-CoV-2 by consistently generating and disseminating information related to various events such as encounters, vaccinations or infections. While the functionality of DCT has been well researched, the necessity of transparency in the use of DCT and the consent to share sensitive information such as users' health, vaccination and location status remains unclear. On one hand, DCT enabled the continuous monitoring of various risk factors, including data-based calculations of infection probabilities. On the other hand, digital monitoring of health risks was closely associated with various uncertainties, such as the ambiguous storage of personal data and its potential future misuse, e.g., by tech companies or health authorities. Our contribution aims to retrospectively analyze the COVID-19 pandemic from a post-pandemic perspective and utilize it as a case study for the implementation of new technological measures. We argue that under the condition of voluntary use of DCT, transparency plays a key role in convincing individuals to install health technologies on their mobile devices, keep them activated and consent to the sharing of sensitive data. We support our argument with qualitative data from an expert survey conducted between 2020 and 2021 and analyzed according to the principles of Grounded Theory.

The importance of ecosystem services to support the governance of critical ecological assets

Ecosystem services (ES) are the ecosystem contribution to human well-being: they bridge ecosystems with socio-economic systems, in terms of both impacts and dependencies. So far, most of the research on ES focused on the services delivered “here and now”, i.e. where spatial location of ecosystem providers and human users can be defined and when the delivery of the needed services can be allocated to the current generations. However, especially when considering the medium- and long-term effects of climate change adaptation, there is the need to start projecting the scope of the services beyond national boundaries and to the future generations. Although formally listed and acknowledged, many ES currently miss applications able to support appropriate biophysical assessment and valuation. In this respect, it is time to start considering ES that go beyond the “here” because they serve the global society, and beyond the “now” because they consider long terms impacts. This article identifies possible streams of these “not-here, not-now” ES that requires developing applications, as it is already happening for many other ES. Such ES, in fact, contribute to identify long-term “critical ecological asset”, whose assessment can provide important environmental metrics for economic and financial analyses.

Analisis Aplikasi Augmented Reality dalam Bidang Pariwisata Terhadap Keamanan Privasi Data

This study investigates the benefits of augmented reality (AR) in the tourism industry and its impact on data security. The background of the research highlights that information technology is becoming increasingly important in the tourism industry, with augmented reality (AR) being a major focus for enhancing visitor experiences. In this context, concerns about user data protection arise since AR applications may require personal information such as user location and travel preferences. A comparative research method was used to compare two popular augmented reality (AR) applications: Smartify, Arts and Culture, Zome, Augmented Reality, Maps AR, Guideo, and AR Trial. The results indicate that most applications have adequate data security policies, including data transfer encryption and user control over their data. Zome was found to be the best in terms of data security and user privacy, scoring 90% in the assessment. This research underscores the importance of using AR applications to maintain data security and user privacy in the tourism industry and suggests further research to learn more about the data privacy practices of AR applications.

Utilizing External Knowledge to Enhance Location Prediction for Twitter/X Users in Low Resource Settings

Accurate estimates of user location are important for many online services, including event detection, disaster management and determining public opinion. Neural network-based techniques have proven to be highly effective in predicting user location. However, these models typically require a large amount of labeled training data, which can be difficult to obtain in real-world scenarios. In this paper, we present two approaches to tackle the issue of limited training data when predicting city level location. First, we consider a self-supervised approach that trains a state level model without labeled data and then integrate this knowledge into the training data set used for city level predictions. Second, we explore the option of increasing the number of training examples by utilizing external resources to generate synthetic users . Finally, we combine these two strategies, exploiting the benefits of both. We empirically evaluate our proposed techniques on multiple Twitter/X data sets and show that our models perform significantly better than the state-of-the-art with improvements of up to 6% for Acc@161 and 8% for F1 score.

Including local knowledge in conservation planning: the case of the western coastal protected areas in Suriname

ABSTRACT There is increasing recognition of the importance of local knowledge in conservation governance and sustainable use of natural resources. This article studies three social-ecological systems: Bigi Pan, Noord Coronie, and Noord Saramacca – each designated as a Multiple Use Management Area (MUMA) by the Surinamese government. The main focus of this article is how local knowledge and user perspectives may contribute to the management of a MUMA, including to making decisions about biodiversity and natural resources. We use an action research approach that includes qualitative case study methodology, participant observations, group and individual interviews. The analytical framework is based on the SES model from Ostrom (2009) to understand interactions between subsystems as mediated by local and other forms of knowledge. We also explore how participatory engagement with MUMA resource users includes local knowledge and user perspectives. The findings show how local knowledge can offer tools and solutions to improve conservation governance by supporting the development of realistic management plans and by supporting better understanding of the interactions between social and ecological subsystems. Moreover, action research helps to recognize local knowledge as valuable for science and promotes social learning among stakeholders. This paper concludes that local knowledge has contributed to policy decisions that are connected to the use practices of the people who are of the place and know the context well. The inclusion of local knowledge through participatory drafting of management plans has contributed to overcoming some major management challenges and helped to build a supportive social environment for conservation.

Life cycle environmental evaluation of medical oxygen masks in the UK

In a clinical setting, medical oxygen masks (MOMs) are made using lightweight and transparent materials with Polyvinyl Chloride (PVC) being a popular choice. Environmental concerns around the use of PVC have arisen due to the toxicity of plasticisers required; in particular the use of increasingly regulated phthalate (Pht) based plasticisers. Non-Pht plasticisers and alternative materials to PVC are being sought as potential replacements in order to keep MOMs in line with current and expected regulations. This study explores the environmental impacts of three (low-flow) MOMs using a life cycle assessment approach from cradle to grave with a functional unit defined as ‘one single-use low-flow medical oxygen mask for adult use in the UK’. The results account for all 11 impact categories as provided by the CML-IA baseline v3.03 methodology. PVC is the main component of two MOMs: mask A using a non-Pht based plasticiser and mask C (a hypothetical mask) using Pht-based plasticiser DEHP. For Mask B, styrene-ethylene-butadiene-styrene based thermoplastic elastomer (TPE-S) and polypropylene is used instead of PVC. Mask B shows the lowest environmental impact across all impact categories. For six out of 11 impact categories (including global warming potential and ozone depletion), mask C scores highest, whereas mask A is highest for the other five categories (with large impacts in human toxicity and ecotoxicities). Two scenario analyses show the importance of supply chain logistics (i.e., the location of the manufacturing site and location of end user) on overall environmental impact. The results of this study intend to provide evidence to policy makers, healthcare professionals, and manufacturers of MOMs to improve the overall environmental impacts of these products, as they contribute around 4437 tonnes of CO2 eq. yearly in the UK alone. The results show that switching from plasticised PVC to TPE-S would reduce the impacts of the use of MOMS within the UK by 2755 tonnes of CO2 eq. per year.

Institutional arrangements and sustainable maintenance management of community-based mini-grids in Tanzania

This article presents empirical research on the maintenance management model for containerised solar community-based mini-grids and corresponding institutional arrangements. It employs Agrawal's (2001) “institutional arrangements” subset of enabling conditions and examines its applicability in explaining the sustainability of maintenance management of community mini-grids. The paper analyses and synthesises distinctive sustainability experiences from two solar community mini-grids in Tanzania. Using data from 18 semi-structured interviews with village energy committee members, electricity users, technicians and local leaders, the paper explicates a maintenance management model practised in the two mini-grids. The paper further presents institutional arrangements assessment for the maintenance management of the two mini-grids and analyses how rules, sanctions and accountability for community mini-grids maintenance affect the corresponding maintenance sustainability outcomes. The study found reactive maintenance to be a dominant maintenance strategy and is complemented by insufficient balances in the mini-grids' maintenance funds. Proper enforcement of maintenance rules, implementation of arranged graduated sanctions, and presence of low-cost adjudication coupled with the accountability of mini-grid leaders can potentially enhance the maintenance sustainability of community mini-grids. The paper concludes that, institutional arrangements for communally owned mini-grids need a collective interactive approach among local users for sustainable mini-grids maintenance.