Location-based Systems Research Articles

Profiling disaster risk management research: A bibliometric analysis

Background: Disaster risk management is essential to mitigate the adverse impacts of disasters and ensure sustainable development. This study explores the importance of disaster information management and integrating information and communication technologies in crisis management strategies. It emphasizes the role of technologies, such as location-based systems and big data analytics, in reducing disaster losses. Methods: This study uses a qualitative descriptive approach to understand the social and cultural context by analyzing documents from various sources such as papers, regulations, and academic literature. Data collection techniques were library review, 957 relevant documents, and a minimal keyword search on Scopus, resulting in 44 journal articles discussed in the public administration and governance framework. The articles included journals indexed from Google Scholar to WOS and Scopus and nationally accredited journals. Findings: This study underscores the importance of disaster resilience strategies, especially in urban areas vulnerable to earthquakes, volcanic eruptions, and floods due to inadequate urban planning. Furthermore, this study examines international efforts to integrate disaster risk reduction and climate change adaptation into sustainable development frameworks, including UN initiatives. Conclusions: This comprehensive analysis highlights the global relevance of disaster risk management and its implications for policy and community resilience. Novelty/Originality of this study: Research on disaster risk management integrates the latest information and communication technologies, such as location-based systems and big data analytics, into crisis management strategies. This provides a new approach to disaster mitigation.

An Optimized Location-Based System for the Improvement of E-Commerce Systems

An Optimized Location-Based System for the Improvement of E-Commerce Systems

Population and contact tracer uptake of New Zealand's QR-code-based digital contact tracing app for COVID-19.

This study aimed to understand the population and contact tracer uptake of the quick response (QR)-code-based function of the New Zealand COVID Tracer App (NZCTA) used for digital contact tracing (DCT). We used a retrospective cohort of all COVID-19 cases between August 2020 and February 2022. Cases of Asian and other ethnicities were 2.6 times (adjusted relative risk (aRR) 2.58, 99 per cent confidence interval (95% CI) 2.18, 3.05) and 1.8 times (aRR 1.81, 95% CI 1.58, 2.06) more likely than Māori cases to generate a token during the Delta period, and this persisted during the Omicron period. Contact tracing organization also influenced location token generation with cases handled by National Case Investigation Service (NCIS) staff being 2.03 (95% CI 1.79, 2.30) times more likely to generate a token than cases managed by clinical staff at local Public Health Units (PHUs). Public uptake and participation in the location-based system independent of contact tracer uptake were estimated at 45%. The positive predictive value (PPV) of the QR code system was estimated to be close to nil for detecting close contacts but close to 100% for detecting casual contacts. Our paper shows that the QR-code-based function of the NZCTA likely made a negligible impact on the COVID-19 response in New Zealand (NZ) in relation to isolating potential close contacts of cases but likely was effective at identifying and notifying casual contacts.

Development of an Algorithm to Evaluate the Quality of Geolocated Addresses in Urban Areas

The spatial and semantic data of geographic addresses are extremely important for citizens, governments, and companies. The addresses can georeference environmental, economic, security, health, and demographic parameters in urban areas. Additionally, address components can be used by users to locate any point of interest (POI) with location-based systems (LBSs). For this reason, errors in address data can affect the geographic location of events, map representations, and spatial analyses. Thus, this paper presents the development of an algorithm for evaluating the quality of semantic and geographic information in any geospatial address dataset. The reference datasets are accessible using open data platforms or spatial data infrastructure (SDI) and volunteered geographic information (VGI), and both have been compared with commercial datasets using geocoding web services. Address quality analysis was developed using several open-source data science code libraries combined with spatial databases and geographic information systems. In addition, the quality of geographic addresses was evaluated by carrying out normalized tests in accordance with International Geospatial Standards (ISO 19157). Finally, this methodology assesses the quality of authorized and VGI address datasets that can be used for geocoding any relevant information in specific urban areas.

Neuroadaptive LBS: towards human-, context-, and task-adaptive mobile geographic information displays to support spatial learning for pedestrian navigation

ABSTRACT Well-designed, neuroadaptive mobile geographic information displays (namGIDs) could improve the lives of millions of mobile citizens of the mostly urban information society who daily need to make time critical and societally relevant decisions while navigating. What are the basic perceptual and neurocognitive processes with which individuals make movement decisions when guided by human- and context-adaptive namGIDs? How can we study this in an ecologically valid way, also outside of the highly controlled laboratory? We report first ideas and results from our unique neuroadaptive research agenda that brings us closer to answering this fundamental empirical question. We present our first implemented methodological solutions of novel ambulatory evaluation methods to study and improve Location-based System (LBS) displays, by critical examination of how perceptual, neurocognitive, psychophysiological, and display design factors might influence decision-making and spatial learning in pedestrian mobility across broad ranges of users and mobility contexts.

PASPORT: A Secure and Private Location Proof Generation and Verification Framework

Abstract: Recently, there has been a rapid growth in locationbased systems and applications in which users submit their location information to service providers in order to gain access to a service, resource, or reward. We have seen that in these applications, dishonest users have an incentive to cheat on their location. Unfortunately, no effective protection mechanism has been adopted by service providers against these fake location submissions. This is a critical issue that causes severe consequences for these applications. Motivated by this, we propose the Privacy-Aware and Secure Proof Of pRoximiTy (PASPORT) scheme in this article to address the problem. Using PASPORT, users submit a location proof (LP) to service providers to prove that their submitted location is true. PASPORT has a decentralized architecture designed for ad hoc scenarios in which mobile users can act as witnesses and generate LPs for each other. It provides user privacy protection as well as security properties, such as unforgeability and non transferability of LPs. Furthermore, the PASPORT scheme is resilient to prover–prover collusions and significantly reduces the success probability of Prover–Witness collusion attacks. To further make the proximity checking process private, we propose P-TREAD, a privacy-aware distance bounding protocol and integrate it into PASPORT. To validate our model, we implement a prototype of the proposed scheme on the Android platform. Extensive experiments indicate that the proposed method can efficiently protect location-based applications against fake submissions

MLCEL: Machine Learning and Cost-Effective Localization Algorithms for WSNs

Introduction:: Wireless communication systems provide an indispensable act in real-life scenarios and permit an extensive range of services based on the users' location. The forthcoming implementation of versatile localization networks and the formation of subsequent generation Wireless Sensor Network (WSN) will permit numerous applications. Materials and Methods:: In this perspective, localization algorithms have converted into an essential tool to afford compact implementation for the location-based system to increase accuracy and reduce computational time, proposing a Machine Learning and Cost-Effective Localization (MLCEL) algorithm. MLCEL algorithm is assessed with considered localization algorithms called Support Vector Machine for Regression (SVR), Artificial Neural Network (ANN), and K Nearest Neighbor (KNN). Numerous outcomes show that the MLCEL algorithm performs better than state art algorithms. The simulation is implemented in MATLAB version 8.1 for a network size of 100 nodes. Sensor nodes are positioned in a network area of 100 ×100 m2. Conclusion and Results Discussion:: The results are assessed on different parameters, and MLCEL achieves better results in localization error 13% 16%, cumulative probability 19%-21%, root mean square error 14%-18%, distance error 17%-20%, and computational time 22%-24% than SVR, ANN, and KNN.

Reliability Comparison of Programmable Components Usable for Indoor Localization System

Nowadays, location-based systems are used in various spheres of people's lives. Tracking the movement and location of people can be used in the outdoor environment, but it is also used in enclosed spaces. Location systems can also be used in transport for the purpose of tracking the location of vehicles. Location information is useful for navigating vehicles or people to a destination. However, the implementation of systems for determining the location and movement of people and entities is also important within confined spaces. Indoor location systems are gaining importance in terms of people navigation in different types of facilities where there is a high concentration of people during operational hours. Indoor location systems also serve to enhance security in buildings by tracking the movement of the people who are present. Among other things, persons can receive useful information related to the location in which they are. It is also possible to control, coordinate and manage personnel or resources on the basis of location and movement information in order to increase the efficiency of processes. We are living in a time when the COVID-19 pandemic has significantly affected our daily lives. In the framework of the project APVV-20-0457, research is being carried out to design a system for monitoring and tracking the movement and contact of people in healthcare facilities in order to prevent and limit the spread of coronavirus and other respiratory diseases. The aim of this paper is to investigate the reliability of the programmable components forming the proposed confined space localization system. Based on the testing, the receiving devices were compared with each other, pointing out the advantages and disadvantages of their application in a given space.

A Deep Generative Model for Trajectory Modeling and Utilization

Modern location-based systems have stimulated explosive growth of urban trajectory data and promoted many real-world applications, e.g. , trajectory prediction. However, heavy big data processing overhead and privacy concerns hinder trajectory acquisition and utilization. Inspired by regular trajectory distribution on transportation road networks, we propose to model trajectory data privately with a deep generative model and leverage the model to generate representative trajectories for downstream tasks or directly support these tasks ( e.g. , popularity ranking), rather than acquiring and processing the original big trajectory data. Nevertheless, it is rather challenging to model high-dimensional trajectories with time-varying yet skewed distribution. To address this problem, we model and generate trajectory sequence with judiciously encoded spatio-temporal features over skewed distribution by leveraging an important factor neglected by the literature - the underlying road properties ( e.g. , road types and directions), which are closely related to trajectory distribution. Specifically, we decompose trajectory into map-matched road sequence with temporal information and embed them to encode spatio-temporal features. Then, we enhance trajectory representation by encoding inherent route planning patterns from the underlying road properties. Later, we encode spatial correlations among edges and daily and weekly temporal periodicity information. Next, we employ a meta-learning module to generate trajectory sequence step by step by learning generalized trajectory distribution patterns from skewed trajectory data based on the well-encoded trajectory prefix. Last but not least, we preserve trajectory privacy by learning the model differential privately with clipping gradients. Experiments on real-world datasets show that our method significantly outperforms existing methods.

Fine Time Measurement for the Internet of Things: A Practical Approach Using ESP32

In the world of Internet of Things (IoT), obtaining the physical location of devices has always been a task of great interest for developing increasingly complex location-based services (LBS). That is why in recent years wireless communication standards have been incorporating new additions focused on providing localization mechanisms to technologies widely used in the IoT world, such as Wi-Fi or Bluetooth. In particular, the IEEE 802.11-2016 Wi-Fi standard introduced ranging estimation between two devices through the so-called fine time measurement (FTM) protocol, defined by the IEEE 802.11mc. FTM is not yet widespread in the IoT field, but commercial modules capable of offering this functionality at a reasonable price are starting to appear. In early 2021, the most widespread system on a chip (SOC) family among IoT devices, the ESP32-XX series, added support for this Wi-Fi standard, enabling, for the first time, the use of a standard designed for location-based systems. This article analyzes the performance of this FTM implementation by carrying out and studying several measurement campaigns in different indoor and outdoor scenarios. Additionally, this work proposes an alternative real-time implementation for distance estimation inside the ESP32 using an approach based on machine learning. Such an implementation is successfully validated in a scenario totally different than those considered for the training and test sets. Finally, both the measurement sets and the developed software are available to the scientific community.

Automated Information Systems as a New Approach to Risk Management of Pharmacotherapy in the Treatment of Epilepsy

Epilepsy is a fairly common disease which challenges social life, therefore the use of information systems and software to support patients with epilepsy is a promising trend in electronic and mobile medicine. The aim of the study was to analyse data on the existing information systems, devices, and mobile applications used to support patients with epilepsy and control risks associated with pharmacotherapy, and to assess the prospects for the development of information systems to support patients with epilepsy. The results of an advanced search and systematisation of literature and Internet data suggest a high demand for mobile and e-health. Mobile applications for patients with epilepsy have a large share of e-health tools. The applications help to monitor seizures, record the frequency of taking medications, receive advice on emergency treatment of seizures, adjust the dosage regimen, and, in general, help to reduce the risks of anticonvulsant therapy, increase patient compliance and the competence of the participants in the treatment process. The analysis of the convenience and functionality of the foreign-produced mobile applications: Epilepsy Journal, Seizure Tracker, Helpilepsy, Seizure First Aide, did not reveal any significant differences between them. The following shortcomings of the mobile applications were identified: lack of Russian language support (except Epilepsy Journal), lack of advertisement blocking options in free applications, incomplete use of video recording capabilities and location-based systems. Global trends in the development of mobile medicine suggest the need for Russian-produced software that would address the identified shortcomings of the existing foreign applications and help support patients with epilepsy.

Experimental evaluation of using BLE beacon for outdoor positioning in GPS-denied environment

Abstract. Although Global Positioning System (GPS) is widely used in outdoor location-based services, it still lacks precision due to obstacles that reduce its performance, such as near tall buildings, with bad weather conditions, and under tree canopies. In some situations, inaccurate localization or delay in getting location locks can adversely affect some location-based services’ functionality. Furthermore, it might make these services less efficient or even completely useless, especially when the receiver device has no SIM card or when the service requires a precision higher than three meters. As a solution to this issue, this study designs, develops, and evaluates a prototype location-based system that uses Bluetooth Low Energy beacons for short-range positioning in outdoor environments as a GPS alternative. The proposed system is a game that includes navigational tasks, which can be accomplished by reaching the proximity of two meters from the beacon’s location. The study involved conducting an experiment outdoors with a focus on areas where GPS signals are degraded to assess our proposed system’s efficiency and feasibility compared to the usage of GPS. The results proved BLE beacons’ ability to provide better positioning results than GPS, not only in terms of accuracy but also in terms of stability of positioning results over time. Based on the findings, the study outlines a set of guidelines to be considered in choosing a suitable positioning technology.

Dual Battery Storage Technique for Remote, Location-Based Solar PV System and Standalone Applications

Nowadays, the usage of renewable energy resources (RER) is growing rapidly, but at the same time, the effective utilization of RER is also a challenging task. For the better usage of RER and the reduction of loss, the dual battery storage is proposed. The main aim of this work is to focus on the design and implementation of a reliable and renewable power generating system under a robust situation, along with a battery storage system. The perturb and observe (P&O) maximum power point tracking (MPPT) technique has been applied to improve the solar photovoltaic power production. In addition, the dual battery storage system is being introduced to improve the life cycle of the primary storage system. The proposed dual storage system is highly preferable for remote, location-based application systems, space applications and military operations. In the dual battery storage system, the batteries are working effectively with a good lifespan, when compared with the existing methods. To determine the state of charge (SOC) and depth of discharge (DOC), those batteries’ input charging and discharging levels were monitored closely. MATLAB Simulink (R2013) is used for simulation; finally, a real-time, three-phase inverter was designed and validated. Under this dual battery storage mode, the life time of battery is improved.

Developing a Location-Based Recommender System Using Collaborative Filtering Technique in the Tourism Industry

The rapid growth of new information and products in the virtual environment has made it time consuming to acquire relevant information and knowledge amidst a vast amount of information. Therefore, an intelligent system that can offer the most appropriate and desirable among the large amount of information and products by following the conditions and features selected by each user should be essentially efficient. Systems that perform this task are called recommendation systems. Given the volume of social network data, challenges such as short-term processing and increased accuracy of recommendations are discussed in this type of system. Hence, it can perform processes faster with less error and can be effective in improving the performance of social recommending systems in improving the classification and clustering of information with the help of collaboration filtering methods. This study first develops an innovative conceptual model of a social network-based tourism recommendation system using Flicker network data. This model is based on 9 key components. The comparison show that the proposed method has an accuracy of 0.3% and a lower error rate.

Can Twitter be a Reliable Proxy to Characterize Nation-wide Human Mobility? A Case Study of Spain

In recent years, there has been an increasing interest in the exploitation of geo-tagged documents posted on Online Social Networks (OSN) for human-mobility pattern mining. These patterns can ease the development of effective and intelligent location-based systems of different scenarios. However, the validation of OSN geo-data as a reliable source for human mobility has not been fully studied in literature. Therefore, this study proposes a comprehensive comparison of a nation-scale Twitter (TWT) dataset with an official mobility study published by the Spanish Ministry of Development. Both feeds have been compared considering different variables, such as population density and spatial granularity, among others. Results show that TWT can be a reliable source for human-mobility mining but only when certain socioeconomic, temporal, and spatial factors co-occur.

Toward Privacy Preservation Using Clustering Based Anonymization: Recent Advances and Future Research Outlook

With the continuous increase in avenues of personal data generation, privacy protection has become a hot research topic resulting in various proposed mechanisms to address this social issue. The main technical solutions for guaranteeing a user’s privacy are encryption, pseudonymization, anonymization, differential privacy (DP), and obfuscation. Despite the success of other solutions, anonymization has been widely used in commercial settings for privacy preservation because of its algorithmic simplicity and low computing overhead. It facilitates unconstrained analysis of published data that DP and the other latest techniques cannot offer, and it is a mainstream solution for responsible data science. In this paper, we present a comprehensive analysis of clustering-based anonymization mechanisms (CAMs) that have been recently proposed to preserve both privacy and utility in data publishing. We systematically categorize the existing CAMs based on heterogeneous types of data (tables, graphs, matrixes, etc.), and we present an up-to-date, extensive review of existing CAMs and the metrics used for their evaluation. We discuss the superiority and effectiveness of CAMs over traditional anonymization mechanisms. We highlight the significance of CAMs in different computing paradigms, such as social networks, the internet of things, cloud computing, AI, and location-based systems with regard to privacy preservation. Furthermore, we present various proposed representative CAMs that compromise individual privacy, rather than safeguarding it. Finally, we discuss the technical challenges of applying CAMs, and we suggest promising opportunities for future research. To the best of our knowledge, this is the first work to systematically cover current CAMs involving different data types and computing paradigms.

Community detection of trajectory data for location-based facility recommendation system

Community detection of trajectory data for location-based facility recommendation system

Map Matching Algorithm: Empirical Review Based on Indian Open Street Map Road Network Data

Locating devices on the road network is crucial for any location-based system. Accuracy of map matching algorithms may highly affect the accuracy of any location-based service. This paper includes an empirical review of five major map matching algorithms for locating a device on a digital road network. A standard dataset was used to simulate the working of map matching algorithms. After ascertaining the accuracy of map matching algorithms, it was tested on a real road network. Six different routes varying from 0.6 kilometers to 32 kilometers, covering a total distance of 82.2 kilometers were included in the experiment. Performance of map matching algorithms was evaluated on a total of 2094 road nodes with 1271070 Global Positioning System (GPS) points on the basis of matched, unmatched nodes with root mean square error. It was concluded that Hidden-Markov Model based map matching algorithms has reasonably good accuracy (96% using global data and 89% using Indian dataset) and execution time in comparison to geometric, topological, Kalman filter and Frechet distance based algorithms.

Location-based mobile learning system facilitating English learning

ABSTRACT The central aim of this study was to determine the potential of the GPS-based MCL system for English learning in terms of its relationship with learning achievements. For this, the research enrolled 51 students of Moscow Aviation Institute, National Research University (Moscow, Russian Federation) aged 20 to 22, 35% were males, and 65% were females. The paper defined the predictors of self-efficacy, self-regulation, and academic success. The main research tool was represented by 7-point Likert scale questionnaire surveys. The calculation of Cronbach’s alphas allowed concluding that they were sufficiently reliable insofar as the values obtained for the questionnaires on respondents’ self-regulation, self-efficacy, the use of GPS-based MCL system, and this system’s perceived usefulness and simplicity equaled 0.78, 0.89, 0.84, and 0.84, respectively. Overall, the experiment outcomes uncovered that student self-regulation and self-efficacy do influence learning outcomes and can even predict future academic performance. There was a significant difference in the results of pre- and post-testing conducted before and after the implementation of the GPS-based MCL system with the upward trend observed. Alongside this, the mean scores for the perceived usefulness and ease of use of the proposed MCL system constituted 4.96 and 5.28 (on a 7-point scale).

Restaurant Recommendation System Using User Based Collaborative Filtering

A recommendation system is an application that can identify entities of interest for a person and provide suggestions based on the past record of person’s likes and preferences. The entity of interest can be anything, for example it can be a product, a movie or a news article. Recommender system is an effective way to help users to obtain the personalized and useful information. However, due to complexity and dynamic, the traditional recommender system cannot work well in mobile environment. Keeping such things into consideration, this recommendation system aims to recommend restaurants to users using their past preferences so they do not need to go through a list of choices. The recommender system adopts a user preference model by using the features of user's visited restaurants, and utilizes the location information of user via GPS(Global Positioning System) using LBS(Location Based System) and restaurants to dynamically generate the recommendation results using collaborative filtering technique. The suggestions will be based on the user preferences obtained from the past ratings and reviews given by the user, frequently visited cuisines of the user and the time preference of the user. Moreover, a brief analysis of reviews is also made to provide user a computed synopsis of the restaurant using text mining algorithm.