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Wireless, satellite, and mobile networks are increasingly used in application scenarios to provide advanced services to mobile or nomadic devices. For example, to authenticate mobile users while obtaining access to remote services, a two-factor authentication mechanism is typically used, e.g., based on the ownership of a personal mobile phone, device, or (smart)card and the knowledge of a (static) username and password. Nevertheless, two-factor authentication is considered roughly "adequate" for security problems encountered today on the Internet and even less for ubiquitous or mobile environments. To increase the authentication level, several authentication methods of different classes may be combined to achieve more reliable user identification. In particular, location technologies allow ubiquitous applications to better exploit the (physical) location information in the authentication process. Consequently, in security applications based on multiple authentication factors, an additional authentication factor could be the location information protected for integrity against undesired modification. We present the SAM-PAY authentication method, which combines different authentication factors to obtain a more reliable user identification. The mechanism is based on the use of a (location-aware) device, the location information certified by a trusted external party, such as a component or element in a telecom network, and the knowledge of data, like a static PIN and a dynamically generated one-time password. We also describe the design and implementation of a real case scenario exploiting our SAM-PAY method, namely the refueling service at a self-service gas station. The test-bed put in place for this service demonstrates the feasibility and effectiveness of the SAM-PAY method in open mobile environments. [ABSTRACT FROM AUTHOR]

Generating photo-realistic images that align with the text descriptions is the goal of the text-to-image generation (T2I) model. They can assist in visualizing the descriptions thanks to advancements in machine learning algorithms. Using text as a source, generative adversarial networks (GANs) can generate a series of pictures that serve as descriptions. Recent GANs have allowed oldest T2I models to achieve remarkable gains. However, they have some limitations. The main target of this study is to address these limitations to enhance the text-to-image generation models to enhance location services. To produce high-quality photos utilizing a multi-step approach, we build an attentional generating network called AttnGAN. The fine-grained image-text matching loss needed to train the AttnGAN's generator is computed using our multimodal similarity model. With an inception score of 4.81 on the PatternNet dataset, our AttnGAN model achieves an impressive R-precision value of 70.61 percent. Because the PatternNet dataset comprises photographs, we've added verbal descriptions to each one to make it a text-based dataset instead. Many experiments have shown that AttnGAN's proposed attention procedures, which are critical for text-to-image production in complex circumstances, are effective. [ABSTRACT FROM AUTHOR]

The reverse Skyline query aims to identify a set of data points that dynamically dominate a query point from the decision-makers' perspective. It has been widely applied in business decision-making, recommendation systems, location-based services, knowledge discovery, and data mining. However, existing reverse Skyline queries mainly focus on single-point queries, overlooking multi-point combination queries. To address this, we propose the concept of combination reverse Skyline query (CRSQ), based on single query combinations. Furthermore, to handle multiple combinations with different cardinalities, we develop the Multiple Enhanced k combination reverse Skyline query method (MkECRSQ). MkECRSQ includes three main phases. Initially, we prove that k combination reverse Skyline query (kCRSQ) is NP-hard and propose a novel index structure called QR-GMap for combination queries to significantly accelerate kCRSQ. Subsequently, we compare the multiple kCRSQ results of various k values to determine the most dominant combinations. Finally, we expand the result set by proving the monotonicity of the ECRSQ algorithm. The final MkECRSQ results consist of the obtained combinations and the expanded result set. Theoretical and experimental results show that MkECRSQ not only rapidly yields results for CRSQ but also recommends the most dominant combinations to decision-makers among multiple combinations in the query dataset, while also overcoming the challenge of limited cardinality in the result sets. By introducing CRSQ and MkECRSQ, our work fills a significant research gap in reverse Skyline queries, extending their applicability to multi-point combination queries and offering enhanced decision-making support. [ABSTRACT FROM AUTHOR]

When protecting the location privacy of users who enjoy LBS, traditional k-anonymity techniques often fail to comprehensively consider time costs and location context during anonymization processes. To address this issues, this paper proposed a KLPPS-AGC. Firstly, utilizing location generalization and Alt-Geohash encoding technique enabled rapid retrieval of historical data. Secondly, selecting locations with high location entropy based on historical query probabilities enabled the construction of high location entropy. Furthermore, it enhanced the dispersion of the anonymous set by applying the Haversine formula. Lastly, this paper built a secure anonymous set to protect user's location privacy. Experiments show that this scheme has lower time cost and higher privacy. [ABSTRACT FROM AUTHOR]

Indoor location-based services and applications need to obtain information about the indoor spatial layouts and topological relationships of indoor spaces. The 3D city modeling data standard CityGML describes the indoor geometric and semantic information of buildings, but the surfaces composing a volume are discrete, leading to invalid volumes. Moreover, the topological adjacency relationships of adjacent indoor spaces have not yet been described, which makes it difficult to realize effective queries and analyses for indoor applications. In this paper, we present a 3D topological data model for indoor spaces that adopts five topological primitives, namely, node, edge, loop, face, and solid, to describe the topological relationships of indoor spaces. Then, by improving the existing face-sorting method according to vector products in 3D space, a method for constructing 3D topological relationships for indoor spaces is proposed, which successively constructs the topological hierarchical combination of volume and the topological adjacency relationships of adjacent volumes. The experimental results show that by using the improved face-sorting method proposed in this work, the relative positions of faces are directly determined to sort the faces set, which avoids relatively cumbersome calculations and improves the efficiency of constructing 3D topological relationships for indoor spaces. [ABSTRACT FROM AUTHOR]

In recent years, ensuring the privacy of location-based services (LBSs) has become a central concern. While various privacy protection strategies have been proposed, user collaboration remains a standard and widely-used solution to address service bottlenecks and attack vulnerabilities. Although it is widely used, challenges still remain. For collaboration to succeed, users must trust one another and be willing to cooperate, often forming anonymous groups. However, curious collaborators may attempt to learn other users' private information, or they may collude with service providers to extract location data. To address these issues, this paper proposes a Privacy Protection Scheme based on Verifiable Secret Sharing and Trust mechanism (VSS-TPPS). In this scheme, the requester encrypts and splits the main secret using a verifiable secret sharing algorithm, while providing a coefficient commitment to verify the sub-secret data, making it difficult for collaborative users to infer any information about the requester. Additionally, if fewer than t users collude, it becomes extremely challenging to form a complete query. By combining verifiable secret sharing with a trust mechanism, the scheme introduces competitive incentives, rewarding those cooperative users who submit partitioned information first. The simulation experiments verified the effectiveness of the proposed scheme in countering collusion attacks and inference attacks. Compared with the SCPPS, GCS, Tr-privacy, and Ik-anonymity schemes, the VSS-TPPS scheme improved average efficiency by approximately 23.64%, 99.80%, 96.26%, and 94.10%, respectively. The VSS-TPPS scheme not only enhances privacy protection but also significantly improves efficiency, demonstrating its effectiveness in user collaboration privacy protection. [ABSTRACT FROM AUTHOR]

Accurate non-line of sight (NLOS) identification technique in ultra-wideband (UWB) location-based services is critical for applications like drone communication and autonomous navigation. However, current methods using binary classification (LOS/NLOS) oversimplify real-world complexities, with limited generalisation and adaptability to varying indoor environments, thereby reducing the accuracy of positioning. This study proposes an extreme gradient boosting (XGBoost) model to identify multi-class NLOS conditions. We optimise the model using grid search and genetic algorithms. Initially, the grid search approach is used to identify the most favourable values for integer hyperparameters. In order to achieve an optimised model configuration, the genetic algorithm is employed to fine-tune the floating-point hyperparameters. The model evaluations utilise a wide-ranging dataset of real-world measurements obtained with a Qorvo DW1000 UWB device, covering various indoor scenarios. Experimental results show that our proposed XGBoost achieved the highest overall accuracy of 99.47%, precision of 99%, recall of 99%, and an F-score of 99% on an open-source dataset. Additionally, based on a local dataset, the model achieved the highest performance, with an accuracy of 96%, precision of 96%, recall of 97%, and an F-score of 97%. In contrast to current machine learning methods in the literature, the suggestion model enhances classification accuracy and effectively addresses the NLOS/LOS identification as a multiclass propagation channel. This approach provides a robust solution with generalisation and adaptability across various dataset types and environments for more reliable and accurate indoor positioning technologies. [ABSTRACT FROM AUTHOR]

Based on spatiotemporal sensing techniques, the cross-regional traveling welcome short messaging service (TW-SMS) has been adopted in China and has become popular, typically being used when travelers pass through or arrive in cities. In this service, governmental institutions in combination with telecom operators send welcome messages with the local characteristics. As a typical location-based service for mobile users, the TW-SMS includes reminders or alerts related to COVID-19 prevention and control. In this paper, we investigate the perceptions and behavior of mobile users regarding this special TW-SMS through mixed-methods research. An online survey was conducted among mobile users who engaged in intercity travel. After analyzing samples of TW-SMS data collected during the COVID-19 pandemic, we found that the respondents exhibited a relatively positive overall attitudes and recognized the necessity and helpfulness of the TW-SMS with its trusted content. For content analysis, we found that more than 70% of the messages transmitted by the TW-SMS were released by official departments (e.g., the COVID-19 Prevention and Control Office). Reminders about traveling registration and nucleic acid testing were assigned the highest importance, as they offer convenience in communicating the most up-to-date prevention and control information to mobile users during intercity travel. Through this study, we provide insights into epidemic prevention and control experiences during public health emergencies in cities. [ABSTRACT FROM AUTHOR]

Traditional, predominantly print-based, media companies with a local or regional focus have been struggling for years with the consequences of increasing digitalization and the associated rise of social media offerings. For traditional media, this means fewer readers, less circulation, decreasing advertising revenue in the context of the advertising circulation spiral and further concentration processes in the industry. Current research clearly indicates that participatory forms of content creation can provide the urgently needed basis for necessary business model innovation in journalism due to their potential to further expand the limited scope of journalistic offerings provided by conventional media and thus contribute to the future viability of local media offerings. However, users themselves are usually not given the opportunity to become communicators in their local environment. A key question arising is what is necessary to successfully and sustainably implement participatory communication offerings in the media industry? This article aims to identify factors influencing the willingness to participate in (local) news applications. For this purpose, we interviewed people who have been awarded for their voluntary commitment in their local environment. The objective is to find out what motivates these people with high intrinsic motivation to participate. The results show that a network character, non-monetary reward systems and the possibility of recruiting further members (in this case users) can serve as motivating factors for active participation in (local) news applications. The article also highlights the interplay between volunteering and participatory journalism, which offers plenty of potential, especially on a digital level.

Cellular network fingerprint localization technology utilizes signal feature analysis for location estimation, providing a crucial technological pathway to enhance the accuracy of location-based services (LBS) in Internet of Things (IoT) applications. However, this technology faces new challenges. Firstly, to address errors in sub-model outputs that traditional hybrid prediction models cannot effectively identify, we designed a novel hybrid prediction model. This model combines a one-dimensional convolutional neural network and a fully connected neural network in parallel, integrating a newly designed filtering process to eliminate most errors in sub-model outputs and reduce subsequent computational burden. Secondly, considering the limitation of fingerprint localization technology in providing localization error information concurrently with predicted locations, we proposed a localization error estimation method. This method offers uncertainty metrics with each predicted location to provide relevant uncertainty measures. Lastly, addressing the current lack of credibility analysis for fingerprint predicted locations, we devised a credibility assessment method aimed at enhancing the reliability of localization results by providing comprehensive information. Carefully selected evaluations in complex urban environments validate the effectiveness of the proposed localization technology. Compared to existing technologies, it demonstrates superior performance with a median localization error of 8.53 m and an average localization error of 13.36 m. In the future, the technology is expected to play a key role in LBS for the IoT, improving the accuracy and reliability of the system. [ABSTRACT FROM AUTHOR]

"Smart" devices, such as contemporary smartphones and PDAs (Personal Digital Assistance), play a significant role in our daily live, be it for navigation or location-based services (LBSs). In this paper, the use of Ultra-Wide Band (UWB) and Wireless Fidelity (Wi-Fi) based on RTT (Round-Trip Time) measurements is investigated for pedestrian user localization. For this purpose, several scenarios are designed either using real observation or simulated data. In addition, the localization of user groups within a neighborhood based on collaborative navigation (CP) is investigated and analyzed. An analysis of the performance of these techniques for ranging the positioning estimation using different fusion algorithms is assessed. The methodology applied for CP leverages the hybrid nature of the range measurements obtained by UWB and Wi-Fi RTT systems. The proposed approach stands out due to its originality in two main aspects: (1) it focuses on developing and evaluating suitable models for correcting range errors in RF-based TWR (Two-Way Ranging) technologies, and (2) it emphasizes the development of a robust CP engine for groups of pedestrians. The results obtained demonstrate that a performance improvement with respect to position trueness for UWB and Wi-Fi RTT cases of the order of 74% and 54%, respectively, is achieved due to the integration of these techniques. The proposed localization algorithm based on a P2I/P2P (Peer-to-Infrastructure/Peer-to-Peer) configuration provides a potential improvement in position trueness up to 10% for continuous anchor availability, i.e., UWB known nodes or Wi-Fi access points (APs). Its full potential is evident for short-duration events of complete anchor loss (P2P-only), where an improvement of up to 53% in position trueness is achieved. Overall, the performance metrics estimated based on the extensive evaluation campaigns demonstrate the effectiveness of the proposed methodologies. [ABSTRACT FROM AUTHOR]

Advancements in tracking technologies like GPS, RFID and mobile devices have made trajectory data collection widespread. This surge in tracking device usage and location-based services popularity has greatly increased moving object trajectory data availability. The ontological modelling of this kind of data is of paramount importance in understanding and utilising such data effectively. By incorporating maximum semantic data into this model, a variety of essential elements related to mobile object trajectories can be captured. An ontology model rich in semantics not only accurately represents trajectory characteristics but also links them to other relevant elements such as spatial and temporal contexts, movement types and mobile object behaviours. This semantic richness grants the model great adaptability, allowing it to be reused in various contexts related to object mobility and making it generic. Moreover, by integrating this semantic data, the process of analysis and decision-making experiences significant improvement, as it relies on more comprehensive and well-structured information, thereby facilitating informed conclusions and effective strategy implementation. Our objective is to propose a generic ontological model for trajectory data that is rich in semantics and considers the various aspects of moving objects, their movements, their trajectories and their interactions with their environment, aiming to fill the gap identified in other models proposed in the literature. [ABSTRACT FROM AUTHOR]

User location trajectory refers to the sequence of geographic location information that records the user's movement or stay within a period of time and is usually used in mobile crowd sensing networks, in which the user participates in the sensing task, the process of sensing data collection faces the problem of privacy leakage. To address the privacy leakage issue of trajectory data during uploading, publishing, and sharing when users use location services on mobile smart group sensing terminal devices, this paper proposes a privacy protection method based on generative adversarial networks and attention mechanisms (BiLS-A-GAN). The method designs a generator attention model, GAttention, and a discriminator attention model, DAttention. In the generator, GAttention, combined with a bidirectional long short-term memory network, more effectively senses contextual information and captures dependencies within sequences. The discriminator uses DAttention and the long short-term memory network to distinguish the authenticity of data. Through continuous interaction between these two models, trajectory data with the statistical characteristics of the original data is generated. This non-original trajectory data can effectively reduce the probability of an attacker's identification, thereby enhancing the privacy protection of user information. Reliability assessment of the Trajectory-User Linking (TUL) task performed on the real-world semantic trajectory dataset Foursquare NYC, compared with traditional privacy-preserving algorithms that focus only on the privacy enhancement of the data, this approach, while achieving a high level of privacy protection, retains more temporal, spatial, and thematic features from the original trajectory data, to not only guarantee the user's personal privacy, but also retain the reliability of the information itself in the direction of geographic analysis and other directions, and to achieve the win-win purpose of both data utilization and privacy preservation. [ABSTRACT FROM AUTHOR]

Location-based services have a wide range of applications; more recent among those include monitoring virus spread and disaster management. Nevertheless, they also open up new vulnerabilities in terms of the user's location privacy. Differential privacy has been widely accepted to provide location privacy as it provides a provable privacy guarantee. In this paper, we show that a differential privacy mechanism designed for individual queries is not effective when applied repeatedly to queries from correlated locations. We call such location-based queries from adjacent locations of a user as continuous queries. We quantify the reduction in privacy level of differential privacy when applied to continuous queries. The problem is solved from an adversarial viewpoint, given the perturbed location of trace size n, estimate the actual location. Assuming ϵ is the privacy level for applying noise independently to a location, we prove that the privacy level is reduced to n ϵ , when applied to n consecutive locations. We propose a privacy preserving mechanism and show that it handles continuous queries better than standard differential privacy mechanisms. The results are validated empirically using a real dataset. [ABSTRACT FROM AUTHOR]

Indoor positioning systems have become increasingly important due to the rapid expansion of Internet of Things (IoT) technologies, especially for providing precise location-based services in complex environments such as multi-floor campus buildings. This paper presents a WiFi fingerprint indoor localization system based on AdaBoost, combined with a new access point (AP) filtering technique. The system comprises offline and online phases. During the offline phase, a fingerprint database is created using received signal strength (RSS) values for two four-floor campus buildings. In the online phase, the AdaBoost classifier is used to accurately estimate locations. To improve localization accuracy, APs that always appear in the measurement data are selected for applying the AdaBoost algorithm, aiming to eliminate noise from the fingerprint database. The performance of the proposed method is compared with other well-known machine learning-based positioning algorithms in terms of positioning accuracy and error distances. The results indicate that the average positioning accuracy of the proposed scheme reaches 95.55%, which represents an improvement of 5.55% to 16.21% over the other methods. Additionally, the two-dimensional positioning error can be reduced to 0.25 m. [ABSTRACT FROM AUTHOR]

These days, location-based services, or LBS, are used for various consumer applications, including indoor localization. Due to the ease with which Wi-Fi can be accessed in various interior settings, there has been increasing interest in Wi-Fi-based indoor localisation. Deep learning in indoor localisation systems that use channel state information (CSI) fingerprinting has seen widespread adoption. Usually, these systems comprise two primary components: a positioning network and a tracking system. The positioning network is responsible for learning the planning from high-dimensional CSI to physical positions, and the following system uses historical CSI to decrease positioning error. This work presents a novel localization method that combines high accuracy and generalizability. However, existing convolutional neural network (CNN) fingerprinting placement algorithms have a limited receptive area, limiting their effectiveness since important data in CSI has not been thoroughly explored. We offer a unique attention-augmented residual CNN to remedy this issue so that the data acquired and the global context in CSI may be utilized to their full potential. On the other hand, while considering the generalizability of a monitoring device, we uncouple the scheme from the CSI environments to make it feasible to use a single tracking system across all contexts. To be more specific, we recast the tracking issue as a denoising task and then used a deep route before solving it. The findings illuminate perspectives and realistic interpretations of the residual attention-based CNN (RACNN) in device-free Wi-Fi indoor localization using channel state information (CSI) fingerprinting. In addition, we study how the precision change of different inertial dimension units may negatively influence the tracking performance, and we implement a solution to the problem of exactness variance. The proposed RACNN model achieved a localization accuracy of 99.9%, which represents a significant improvement over traditional methods such as K-nearest neighbors (KNN) and Bayesian inference. Specifically, the RACNN model reduced the average localization error to 0.35 m, outperforming these traditional methods by approximately 14% to 15% in accuracy. This improvement demonstrates the model's ability to handle complex indoor environments and proves its practical applicability in real-world scenarios. [ABSTRACT FROM AUTHOR]

The proportion of Shanghai residents' non-commuting trips in the total number of urban trips has been increasing year by year. Non-commuting trips, as an important symbol of modernized lifestyles, reflect the use of urban resources by people in production and life, and their dynamic distribution and changes have extremely important guiding roles in the city's fine-tuned management and resource allocation. Taking Shanghai as an example, this paper extracts three types of urban function grids, namely, urban park green space, commercial consumption and public service through the establishment of Shanghai urban grid function identification system based on multi-source data such as Baidu LBS (Location-BasedServices), data, POI (Point of Interest) data, etc. Then 3 types of non-commuting trips, namely, urban park, commercial and public service are further classified and identified, and their characteristics are analyzed. The results show that: ①Urban park travel frequency is low, and the proportion of long-distance trip is high. Zhujiajiao Town, Nanjing East Road Street and Lujiazui Street are three main travel gathering nodes in the city. There is a close relationship between the main urban area and the suburbs and the main urban area is more dependent on the suburbs. ②The travel frequency of commercial is the highest and the average travel distance is the shortest. The trend of trip agglomeration to the main urban area is significant and the trip distribution shows an obvious multi-center trend. ③The travel frequency of public service is high and the average travel distance is short. The trip distribution shows an obvious hierarchical structure, gathering in the main urban area and suburban new towns. ④Songjiang, Jiading and Qingpu in the suburban new towns have stronger attraction. The study suggests that non-commuting trips have an important impact on the urban spatial network structure, which needs to be further analyzed and compared with the spatial network structure formed by commuting trips to find the links and differences, so as to provide research support for the optimization of urban spatial structure. [ABSTRACT FROM AUTHOR]

Point-of-interest (POI) is a fundamental data type of maps. Anomalous POIs would make maps outdated and lead to user-unfriendly location-based services, and thus should be discovered as fast as possible. Traditional POI anomaly detection methods are inefficient owing to high investigation costs. The emergence of massive human activity data provides a new insight into monitoring POI states through time series modeling. When a POI turns into an anomaly, the associated human activity would disappear. However, human activity data have complicated temporal patterns and noises. It is challenging for existing time series methods to model human activity dynamics. More importantly, there is a lag between the time a POI becomes anomalous and the time we discover it. In this research, we develop a temporal state regression network (TSRNet) model for fast POI anomaly detection. The model can extract temporal features in human activity data, and predict POI state scores as anomaly indicators. Meanwhile, an inference approach is proposed to generate state score sequences as inexact labels for model training. Such weak labels enable TSRNet to identify abnormal temporal patterns as soon as they appear, so that POI outliers can be detected at an early time. Experiments on real-word datasets from AMAP validate the feasibility of our method. [ABSTRACT FROM AUTHOR]

Weighted k-nearest neighbor (WKNN)-based Wi-Fi fingerprinting is popular in indoor location-based services due to its ease of implementation and low computational cost. KNN-based methods rely on distance metrics to select the nearest neighbors. However, traditional metrics often fail to capture the complexity of indoor environments and have limitations in identifying non-linear relationships. To address these issues, we propose a novel WKNN-based Wi-Fi fingerprinting method that incorporates distance metric learning. In the offline phase, our method utilizes a Siamese network with a triplet loss function to learn a meaningful distance metric from training fingerprints (FPs). This process employs a unique triplet mining strategy to handle the inherent noise in FPs. Subsequently, in the online phase, the learned metric is used to calculate the embedding distance, followed by a signal-space distance filtering step to optimally select neighbors and estimate the user's location. The filtering step mitigates issues from an overfitted distance metric influenced by hard triplets, which could lead to incorrect neighbor selection. We evaluate the proposed method on three benchmark datasets, UJIIndoorLoc, Tampere, and UTSIndoorLoc, and compare it with four WKNN models. The results show a mean positioning error reduction of 3.55% on UJIIndoorLoc, 16.21% on Tampere, and 16.49% on UTSIndoorLoc, demonstrating enhanced positioning accuracy. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of preserving user privacy in location-based services (LBSs) by proposing a novel, complementary approach to existing privacy-preserving techniques such as k-anonymity and l-diversity. Our approach implements collaborative caching strategies within a mobile ad hoc network (MANET), exploiting the geographic of location-based queries (LBQs) to reduce data exposure to untrusted LBS servers. Unlike existing approaches that rely on centralized servers or stationary infrastructure, our solution facilitates direct data exchange between users' devices, providing an additional layer of privacy protection. We introduce a new privacy entropy-based metric called accumulated privacy loss (APL) to quantify the privacy loss incurred when accessing either the LBS or our proposed system. Our approach implements a two-tier caching strategy: local caching maintained by each user and neighbor caching based on proximity. This strategy not only reduces the number of queries to the LBS server but also significantly enhances user privacy by minimizing the exposure of location data to centralized entities. Empirical results demonstrate that while our collaborative caching system incurs some communication costs, it significantly mitigates redundant data among user caches and reduces the need to access potentially privacy-compromising LBS servers. Our findings show a 40% reduction in LBS queries, a 64% decrease in data redundancy within cells, and a 31% reduction in accumulated privacy loss compared to baseline methods. In addition, we analyze the impact of data obsolescence on cache performance and privacy loss, proposing mechanisms for maintaining the relevance and accuracy of cached data. This work contributes to the field of privacy-preserving LBSs by providing a decentralized, user-centric approach that improves both cache redundancy and privacy protection, particularly in scenarios where central infrastructure is unreachable or untrusted. [ABSTRACT FROM AUTHOR]

As Location-Based Services (LBSs) rapidly develop, indoor localization technology is garnering significant attention as a critical component. Smartphones have become tools for indoor localization due to their highly integrated sensors, fast-evolving computational capabilities, and widespread user adoption. With the rapid advancement of smartphones, methods for smartphone-based indoor localization have increasingly attracted attention. Although there are reviews on indoor localization, there is still a lack of systematic reviews focused on smartphone-based indoor localization methods. In particular, existing reviews have not systematically analyzed smartphone-based indoor localization methods or considered the combination of smartphone sensor data with prior knowledge of the indoor environment to enhance localization performance. In this study, through systematic retrieval and analysis, the existing research was first categorized into three types to dissect the strengths and weaknesses based on the types of data sources integrated, i.e., single sensor data sources, multi-sensor data fusion, and the combination of spatial context with sensor data. Then, four key issues are discussed and the research gaps in this field are summarized. Finally, a comprehensive conclusion is provided. This paper offers a systematic reference for research and technological applications related to smartphone-based indoor localization methods. [ABSTRACT FROM AUTHOR]

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The problem of user equipment positioning based on radio signals is considered via deep learning. As in most supervised‐learning tasks, a critical aspect is the availability of a relevant dataset to train a model. However, in a cellular network, the data‐collection step may induce a high communication overhead. As a result, to reduce the required size of the dataset, it may be interesting to carefully choose the positions to be labelled and to be used in the training. Therefore, an active learning approach for efficient data collection is proposed. It is first shown that significant gains (both in terms of positioning accuracy and size of the required dataset) can be obtained for the considered positioning problem using a genie. This validates the interest of active learning for positioning. Then, a practical method is proposed to approximate this genie. [ABSTRACT FROM AUTHOR]

Online ride-hailing services have become a vital component of urban transportation worldwide due to their convenience and flexibility. However, the expansion of their user base has dramatically heightened the risks of user privacy information leakage. Among these risks, the privacy leakage problem caused by the direct correlation between user (driver and passenger) identity information and location-based ride information is of particular concern. This paper proposes a novel privacy protection scheme for ride-hailing services. In this scheme, decentralized identities are employed for user authentication, separating the identity registration service from the ride-hailing platform, thereby preventing the platform from obtaining user privacy data. The scheme also employs a fuzzy matching strategy based on location Points of Interest (POI) and a ciphertext-policy attribute-based hybrid encryption algorithm to hide the user's precise location and restrict access to location information. Crucially, the scheme achieves the complete decoupling of identity registration services and location-based ride services on the ride-hailing platform, ensuring that users' real identities and ride data cannot be directly associated, effectively protecting user privacy. Within the decoupled architecture, regulatory authorities are established to handle emergencies within ride-hailing services. Through simulation experiments and security analysis, this scheme is demonstrated to be both feasible and practical, providing a new privacy protection solution for the ride-hailing industry. [ABSTRACT FROM AUTHOR]

In mobile communication networks for integrated sensing and communication, site planning of access points fundamentally determines both communication quality and sensing performance. This paper aims to tackle the AP placement problem under the setting of cell‐free massive multiple‐input multiple‐output, which represents a promising direction for network architecture evolution. Seeking a balance between the sum‐throughput of communication and the Cramér–Rao lower bound of target sensing, the access points are first pre‐placed by solving a convex optimization and further adjusted with optimal angular geometry. The final step involves employing nearest neighbour projection to refine access point positions in the cell‐free massive multiple‐input multiple‐output system, ensuring alignment with integrated sensing and communication requirements. The numerical results show that the proposed algorithm achieves 90% of the optimal communication sum‐rate and 95% of the optimal target localization accuracy. [ABSTRACT FROM AUTHOR]

POI data play an important role in various location-based services, including navigation, positioning, and local search applications. However, as cities rapidly develop, a substantial amount of new POI data are generated daily, often accompanied by issues with the quality of their labels. Therefore, there is an urgent need to implement intelligent inference and enhancement processing for POI data labels. Conventional neural network models primarily target balanced data distribution, but they fail to address the issue of imbalanced distribution of POI data labels in terms of quantity. Furthermore, most neural network classification models implicitly learn the semantic knowledge of different categories from training datasets, neglecting the explicit semantic information offered by natural language labels. Considering the above problems, several negative samples are introduced for each input to a positive class, thereby transforming the multi-classification task into a binary classification problem. Simultaneously, POI data labels are introduced to provide explicit semantic information, and the semantic relationship between POI data labels and their names is determined using cross-coding. Experiments demonstrate that the macro − F1 score for the test dataset, which contains 75 different categories of POI data, reaches 0.84. This result surpasses the performance of traditional methods, highlighting the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Efficient spatial query processing in Graph Database Management Systems (GDBMSs) has become increasingly important owing to the prevalence of spatial graph data. However, current GDBMSs lack effective spatial indexing, causing performance issues with complex spatial graph queries. This study proposes a spatial index called Subgraph Integrated R-Tree (SGIR-Tree) for efficient spatial query processing in GDBMSs. The SGIR-Tree integrates the hierarchical R-Tree structure with the graph structure of GDBMSs by converting R-Tree elements into graph components like nodes and edges. The Minimum Bounding Rectangle (MBR) information of spatial objects and R-Tree nodes is stored as properties of these graph elements, and the leaf nodes are directly connected to the spatial nodes. This approach combines the efficiency of spatial indexing with the flexibility of graph databases, thereby allowing spatial query results to be directly utilized in graph traversal. Experiments using OpenStreetMap datasets demonstrate that the SGIR-Tree outperforms the previous approaches in terms of query overhead and index overhead. The results are expected to improve spatial graph data processing in various fields, including location-based service and urban planning, significantly advancing spatial data management in GDBMSs. [ABSTRACT FROM AUTHOR]

In today's era, the address plays a crucial role as one of the key components that enable mobility in daily life. Address data are used by global map platforms and location-based services to pinpoint a geographically referenced location. Geocoding provided by online platforms is useful in the spatial tracking of reported cases and controls in the spatial analysis of infectious illnesses such as COVID-19. The first and most critical phase in the geocoding process is address matching. However, due to typographical errors, variations in abbreviations used, and incomplete or malformed addresses, the matching can seldom be performed with 100% accuracy. The purpose of this research is to examine the capabilities of machine learning classifiers that can be used to measure the consistency of address matching results produced by online geocoding services and to identify the best performing classifier. The performance of the seven machine learning classifiers was compared using several text similarity measures, which assess the match scores between the input address data and the services' output. The data utilized in the testing came from four distinct online geocoding services applied to 925 addresses in Türkiye. The findings from this study revealed that the Random Forest machine learning classifier was the most accurate in the address matching procedure. While the results of this study hold true for similar datasets in Türkiye, additional research is required to determine whether they apply to data in other countries. [ABSTRACT FROM AUTHOR]

With the spread of GPS-equipped portable devices, Location-Based Services (LBSs) flourished. Some crucial LBSs require real-time processing of moving spatial-keyword queries over moving objects, such as an ambulance seeking for volunteers. The research community proposed solutions for scenarios assuming that either the queries or the queried objects are moving, but solutions are needed assuming that both are moving. This work proposes SkyEye; a model that efficiently processes moving continuous top-k spatial-keyword queries over moving objects in a directed streets network. SkyEye computes queries' answer sets for time intervals and smartly updates the answer sets based on the recent history. Novel optimization techniques and indexing structures are leveraged to improve SkyEye's efficiency and scalability. The mathematical foundations of these optimization techniques are thoroughly demonstrated. Finally, extensive experiments showed that SkyEye has significant performance improvements in terms of efficiency, scalability, and accuracy compared to a baseline model. [ABSTRACT FROM AUTHOR]

Against the backdrop of rapidly advancing Artificial Intelligence of Things (AIOT) and sensing technologies, there is a growing demand for indoor location-based services (LBSs). This paper proposes a through-the-wall localization and tracking (TWPT) system based on an improved ultra-wideband (IR-UWB) radar to achieve more accurate localization of indoor moving targets. The TWPT system overcomes the limitations of traditional localization methods, such as multipath effects and environmental interference, using the high penetration and high accuracy of IR-UWB radar based on multi-sensor fusion technology. In the study, an improved Kalman filter (KF) algorithm is used for clutter reduction, while the CLEAN algorithm, combined with a compensation mechanism, is utilized to increase the target detection probability. Finally, a three-point localization estimation algorithm based on multi-IR-UWB radar is applied for the precise position and trajectory estimation of the target. Experimental validation indicates the TWPT system achieves a high positioning accuracy of 96.91%, with a root mean square error (RMSE) of 0.082 m and a Maximum Position Error (MPE) of 0.259 m. This study provides a highly accurate and precise solution for indoor TWPT based on IR-UWB radar. [ABSTRACT FROM AUTHOR]

Advertising is the most crucial part of all social networking sites. The phenomenal rise of social media has resulted in a general increase in the availability of customer tastes and preferences, which is a positive development. This information may be used to improve the service that is offered to users as well as target advertisements for customers who already utilize the service. It is essential while delivering relevant advertisements to consumers, to take into account the geographic location of the consumers. Customers will be ecstatic if the offerings displayed to them are merely available in their immediate vicinity. As the user’s requirements will vary from place to place, location-based services are necessary for gathering this essential data. To get users to stop thinking about where they are and instead focus on an ad, location-based advertising (LBA) uses their mobile device’s GPS to pinpoint nearby businesses and provide useful information. Due to the increased two-way communication between the marketer and the user, mobile consumers’ privacy concerns and personalization issues are becoming more of a barrier. In this research, we developed a collaborative filtering-based hybrid CNN-LSTM model for recommending geographically relevant online services using deep neural networks. The proposed hybrid model is made using two neural networks, i.e., CNN and LSTM. Geographical information systems (GIS) are used to acquire initial location data to collect precise locational details. The proposed LBA for GIS is built in a Python simulation environment for evaluation. Hybrid CNN-LSTM recommendation performance beats existing location-aware service recommender systems in large simulations based on the WS dream dataset. [ABSTRACT FROM AUTHOR]

Studies on human mobility from abundant trajectory data have become more and more popular with the development of location-based services. Prediction for locations people may visit in the future is a significant task, helping to make visiting recommendations and manage traffic conditions. Different from other time series prediction tasks, location prediction is temporally dependent as well as spatial-aware. In this paper, we propose a novel multi-graph attention network with sequence-to-sequence structures based on spatial–temporal long short-term memory to predict future locations. Specifically, we build three graphs with movements in geographic space and apply graph attention networks to explore the latent spatial associations among geographic regions. Additionally, we come up with spatial–temporal long short-term memory and use it to establish a sequence-to-sequence framework, which collects the temporal dependence as well as some spatial information from history trajectories. The predictions of future location are finally made by aggregating spatial–temporal contexts. [ABSTRACT FROM AUTHOR]

In location-based service (LBS), private information retrieval (PIR) is an efficient strategy used for preserving personal privacy. However, schemes with traditional strategy that constructed by information indexing are usually denounced by its processing time and ineffective in preserving the attribute privacy of the user. Thus, in order to cope with above two weaknesses, in this paper, based on the conception of ciphertext policy attribute-based encryption (CP-ABE), a PIR scheme based on CP-ABE is proposed for preserving the personal privacy in LBS (location privacy preservation scheme with CP-ABE based PIR, short for LPPCAP). In this scheme, query and feedback are encrypted with security two-parties calculation by the user and the LBS server, so as not to violate any personal privacy and decrease the processing time in encrypting the retrieved information. In addition, this scheme can also preserve the attribute privacy of users such as the query frequency as well as the moving manner. At last, we analyzed the availability and the privacy of the proposed scheme, and then several groups of comparison experiment are given, so that the effectiveness and the usability of proposed scheme can be verified theoretically, practically, and the quality of service is also preserved. [ABSTRACT FROM AUTHOR]

Modern systems and devices, including unmanned aerial systems (UASs), autonomous vehicles, and other unmanned and autonomous systems, commonly rely on the Global Positioning System (GPS) for positioning, navigation, and timing (PNT). Cellular mobile devices rely on GPS for PNT and location-based services. Many of these systems cannot function correctly without GPS; however, GPS signals are susceptible to a wide variety of signal-related disruptions and cyberattacks. GPS threat detection and mitigation have received significant attention recently. There are many surveys and systematic reviews in the literature related to GPS security; however, many existing reviews only briefly discuss GPS security within a larger discussion of cybersecurity. Other reviews focus on niche topics related to GPS security. There are no existing comprehensive reviews of GPS security issues in the literature. This paper fills that gap by providing a comprehensive treatment of GPS security, with an emphasis on UAS applications. This paper provides an overview of the threats to GPS and the state-of-the-art techniques for attack detection and countermeasures. Detection and mitigation approaches are categorized, and the strengths and weaknesses of existing approaches are identified. This paper also provides a comprehensive overview of the state-of-the-art on alternative positioning and navigation techniques in GPS-disrupted environments, discussing the strengths and weaknesses of existing approaches. Finally, this paper identifies gaps in existing research and future research directions. [ABSTRACT FROM AUTHOR]

The popularity of global GPS location services and location-enabled personal terminal applications has contributed to the rapid growth of location-based social networks. Users can access social networks at anytime and anywhere to obtain services in the relevant location. While accessing services is convenient, there is a potential risk of leaking users' private information. In data processing, the discovery of issues and the generation of optimal solutions constitute a symmetrical process. Therefore, this paper proposes a symmetry–trajectory differential privacy-protection mechanism based on multi-dimensional prediction (TPPM-MP). Firstly, the temporal attention mechanism is designed to extract spatiotemporal features of trajectories from different spatiotemporal dimensions and perform trajectory-sensitive prediction. Secondly, class-prevalence-based weights are assigned to sensitive regions. Finally, the privacy budget is assigned based on the sensitive weights, and noise conforming to localized differential privacy is added. Validated on real datasets, the proposed method in this paper enhanced usability by 22% and 37% on the same dataset compared with other methods mentioned, while providing equivalent privacy protection. [ABSTRACT FROM AUTHOR]

In recent years, Indoor Positioning Systems (IPS) have emerged as a critical technology to enable a diverse range of Location-based Services (LBS) across different sectors, such as retail, healthcare, and transportation. Despite their strong demand and importance, existing implementations of IPS face significant challenges concerning accuracy and privacy. The accuracy issue is mainly rooted in the inherent characteristics of Received Signal Strength (RSS), which is widely integrated into current IPS as it only requires readily available WiFi infrastructure. Several studies have demonstrated that RSS suffers from instability and inaccuracy in the presence of environmental changes, making it an inadequate choice for precise IPS. Furthermore, most state-of-the-art IPS encounter privacy and data security issues as they often require users to share their privacy-sensitive location data with a centralized server. Unfortunately, centralized data collection and processing potentially expose users to privacy breaches. To tackle these shortcomings, we advocate for a comprehensive, accurate, and multifaceted solution that enables users to harness the benefits of IPS without provoking privacy concerns. First, we address the positional inaccuracy problem by combining the strengths and synergies between RSS and Channel State Information (CSI). Fusing these complementary metrics delivers increased stability against environmental fluctuations. Thereby, it provides a robust foundation for reliable and accurate positioning outcomes. Second, to address the privacy challenge, we integrate Federated Learning (FL) into the proposed solution to enable the collaborative development of machine learning-based IPS models while ensuring that user data remains decentralized. We conducted a comprehensive assessment to evaluate the performance of the proposed IPS and the corresponding overheads compared to established baseline techniques that utilize either RSS or CSI independently. The results indicate significant enhancements, highlighting our solution's ability to effectively address accuracy and privacy challenges. [ABSTRACT FROM AUTHOR]

In recent years, Vehicular Ad‐hoc Network (VANET) has become an essential component of intelligent transportation systems that, along with the previous systems such as traffic condition, accident alert, automatic parking, and cruise control, use the communication of vehicle to vehicle and vehicle to the roadside unit to facilitate road transportation. Several challenges hinder efforts to improve traffic conditions and reduce traffic fatalities through VANET. A critical challenge is achieving highly accurate and reliable vehicle localization within the VANET. Additionally, the frequent unavailability of Global Positioning System (GPS), particularly in tunnels and parking lots, presents another significant obstacle. Traditional methods like Dead Reckoning offer low accuracy and reliability due to accumulating errors. Similarly, GPS positioning, map matching with mobile phone location services, and other existing solutions struggle with accuracy and economic feasibility. In this article, two Kalman filter approaches are used based on signal statistical information and the other learning‐based networks, including traditional neural network, deep neural network and LSTM (long short‐term memory) to locate the car. The prediction error of car position with root mean square measures. The squared error and distance prediction error are evaluated. It is shown that in terms of prediction time and processing time of vehicle localization, all the vehicle localization methods are efficient in terms of response time for localization, and Kalman filter methods, traditional neural network and deep neural network are faster than LSTM method. Also, in terms of localization error, Kalman filter works better than learning‐based methods, and in learning‐based methods, both deep neural network and LSTM methods perform better than traditional neural network in terms of localization error. [ABSTRACT FROM AUTHOR]

Accurate indoor–outdoor detection (IOD) is essential for location-based services, context-aware computing, and mobile applications, as it enhances service relevance and precision. However, traditional IOD methods, which rely only on GPS data, often fail in indoor environments due to signal obstructions, while IMU data are unreliable on unseen data in real-time applications due to reduced generalizability. This study addresses this research gap by introducing the DeepIOD framework, which leverages IMU sensor data, GPS, and light information to accurately classify environments as indoor or outdoor. The framework preprocesses input data and employs multiple deep neural network models, combining outputs using an adaptive majority voting mechanism to ensure robust and reliable predictions. Experimental results evaluated on six unseen environments using a smartphone demonstrate that DeepIOD achieves significantly higher accuracy than methods using only IMU sensors. Our DeepIOD system achieves a remarkable accuracy rate of 98–99% with a transition time of less than 10 ms. This research concludes that DeepIOD offers a robust and reliable solution for indoor–outdoor classification with high generalizability, highlighting the importance of integrating diverse data sources to improve location-based services and other applications requiring precise environmental context awareness. [ABSTRACT FROM AUTHOR]

With the rapid development of geo-positioning technologies, location-based services have become increasingly widespread. In the field of location-based services, range queries on geographical data have emerged as an important research topic, attracting significant attention from academia and industry. In many applications, data owners choose to outsource their geographical data and range query tasks to cloud servers to alleviate the burden of local data storage and computation. However, this outsourcing presents many security challenges. These challenges include adversaries analyzing outsourced geographical data and query requests to obtain privacy information, untrusted cloud servers selectively querying a portion of the outsourced data to conserve computational resources, returning incorrect search results to data users, and even illegally modifying the outsourced geographical data, etc. To address these security concerns and provide reliable services to data owners and data users, this paper proposes an efficient and verifiable range query scheme (EVRQ) for encrypted geographical information in untrusted cloud environments. EVRQ is constructed based on a map region tree, 0–1 encoding, hash function, Bloom filter, and cryptographic multiset accumulator. Extensive experimental evaluations demonstrate the efficiency of EVRQ, and a comprehensive analysis confirms the security of EVRQ. [ABSTRACT FROM AUTHOR]

Information is data that is necessary and essential in the era of technological development. One of the information needs is to find the location of craft shops in Tasikmalaya City, especially for foreigners who need information to buy craft products who do not know the location. Currently, information is only created manually through official documents in the form of paper posted on walls or in public places where people gather. Based on the description of the problems obtained, it is necessary to improve services in the information sector, designing a manual craft shop location search information system which will be developed into a technology-based digital information system using the Android Studio application and MySQL database. With the Location Service (LBS) and Global Positioning System (GPS) methods, with this system, users will be given location information and routes to their destination points easily to craft shop locations in Tasikmalaya City. All of these systems provide convenience in conveying information on searching for the location of craft shops in the city of Tasikmalaya. [ABSTRACT FROM AUTHOR]

Urban Vitality (UV) is a critical indicator for measuring sustainable urban development and quality. It reflects the dynamic interactions and supply–demand coordination within urban systems, especially concerning the human–land relationship. This study aims to quantify the UV of Luohe City, China, for the year 2023, analyze its spatial characteristics, and investigate the driving patterns of socio-ecological land functions on UV intensity and heterogeneity under different scenarios. Utilizing multi-source data, including human mobility data from Baidu Location-Based Services (LBSs), Landsat-9, MODIS, and diverse geo-information datasets, we conducted factor screening and comprehensive assessments. Firstly, Self-Organizing Maps (SOMs) were employed to identify typical activity patterns, and the Urban Vitality Index (UVI) was calculated based on Human Mobility Intensity (HMI) data. Subsequently, a framework for quantity–quality–structure assessments weighted and aggregated sub-indicators to evaluate the Land Social Function (LSF) and Land Ecological Function (LEF). Following the screening process, a Multi-scale Geographically Weighted Regression (MGWR) was applied to analyze the scale and driving relationships between UVI and the land assessment sub-indicators. The results were as follows: (1) The UV distribution in Luohe City was highly uneven, with high vitality areas concentrated within the built-up regions. (2) UV showed significant correlations with both LSF and LEF. The influence of LSF on UV was stronger than that of LEF, with the effectiveness of LEF relying on the well-established provisioning of LSF. (3) Artificial Surface Ratio (ASR) and Corrected Night Lights (LERNCI) were identified as key drivers of UV across multiple scenarios. Under the weekend scenario, the Green Space Ratio (GSR) and the Vegetation Quality (VQ) notably enhanced the attractiveness of human activities. (4) The impacts of drivers varied at the urban, township, and street scales. The analysis focuses on factors with significant bandwidth changes across multiple scenarios: VQ, Remote-Sensing-based Ecological Index (RSEI), GSR, ASR, and ALSI. This study underscores the importance of socio-ecological land functions in enhancing urban vitality, offering valuable insights and data support for urban planning. [ABSTRACT FROM AUTHOR]

The Ultra-Wideband (UWB) Location-Based Service is receiving more and more attention due to its high ranging accuracy and good time resolution. However, the None-Line-of-Sight (NLOS) propagation may reduce the ranging accuracy for UWB localization system in indoor environment. So it is important to identify LOS and NLOS propagations before taking proper measures to improve the UWB localization accuracy. In this paper, a deep learning-based UWB NLOS/LOS classification algorithm called FCN-Attention is proposed. The proposed FCN-Attention algorithm utilizes a Fully Convolution Network (FCN) for improving feature extraction ability and a self-attention mechanism for enhancing feature description from the data to improve the classification accuracy. The proposed algorithm is evaluated using an open-source dataset, a local collected dataset and a mixed dataset created from these two datasets. The experiment result shows that the proposed FCN-Attention algorithm achieves classification accuracy of 88.24% on the open-source dataset, 100% on the local collected dataset and 92.01% on the mixed dataset, which is better than the results from other evaluated NLOS/LOS classification algorithms in most scenarios in this paper. [ABSTRACT FROM AUTHOR]

Location-based services (LBS) offer various functionalities, but ensuring secure access to sensitive user data remains a challenge. Traditional access control methods often need more detail to enforce location-specific restrictions. This paper proposes a new approach that utilizes the Generalized Spatio-Temporal Role-Based Access Control Model (GSTRBAC) within the context of LBS. GSTRBAC grants access based on user credentials, authorized locations, and access times, providing a detailed approach to securing LBS data. We introduce an optimized cloud-based GSTRBAC implementation suitable for deployment in modern LBS architectures. The system uses two secure communication protocols tailored to different security requirements. This allows for efficient communication for less-sensitive data while offering robust protection for highly sensitive information. Additionally, a proof-of-concept mobile application demonstrates the system's functionality and efficiency within an LBS environment. Our evaluation confirms the effectiveness of the cloud-based GSTRBAC implementation in enforcing location-specific access control while maintaining resource and time efficiency. [ABSTRACT FROM AUTHOR]

A study on tourist motivation and satisfaction in Pototan found that young adults, singles, college-level, and college graduates are the most motivated demographic. Both males and females were motivated, with single respondents being particularly motivated. Educational attainment was high, with high school and college levels being the most motivated. The study identified five main motivational themes: relaxation, warmth, learning, family togetherness, and adventure. Four satisfaction themes emerged: food quality, recreational facilities, services, and safety. The Department of Tourism and tourist spot owners may develop unique offerings catering to all types and age groups, emphasising authentic, location-based services and experiences. [ABSTRACT FROM AUTHOR]