Location-based Services Research Articles

Spatial Relationship of Inter-City Population Movement and Socio-Economic Determinants: A Case Study in China Using Multiscale Geographically Weighted Regression

In the current field of regional studies, there is a growing focus on regional spatial relationships from the perspective of functional linkages between cities. Inter-city population movement serves as an embodiment of the integrated functionality of cities within a region, and this is closely tied to the socio-economic development of urban areas. This study utilized Location-Based Services (LBSs) to collect the scale of inter-city population movement across 355 cities in China. Additionally, socio-economic data published by local governments were incorporated. By establishing a Multiscale Geographically Weighted Regression (MGWR) model, this research explores the spatial relationships between inter-city population movement and socio-economic influencing factors in China. This study aims to elucidate the spatial scales of the relationships between various variables. Our research findings indicate that the relationship between inter-city population movement and potential socio-economic determinants exhibits spatial non-stationarity. It is better to explore this spatial relationship through the MGWR model as there are different determinants operating on inter-city population movement at different spatial scales. The spatial distribution of the coefficient estimates shows significant regional differences and numerical variations. In China’s economically developed coastal regions, there is relatively balanced development among cities, with advanced manufacturing and producer service industries acting as significant drivers of mobility. In inland regions of China, city size is the most influential variable, directing a substantial flow of human and economic resources towards regional socio-economic hubs such as provincial capitals. The main contribution of this study is the re-examination of the relationship between inter-city population movement and socio-economic factors from the perspective of spatial scales. This approach will help China to consider the heterogeneity of different regions more extensively when formulating regional development policies, thereby facilitating the targeted promotion of regional element flow.

Classifying Location Points as Daily Activities using Simultaneously Optimized DBSCAN-TE Parameters.

Location-based services data collected from mobile phones represent a potentially powerful source of travel behavior data, but transforming the location points into semantic activities – where and when activities occurred – is non-trivial. Existing algorithms to label activities require multiple parameters calibrated to a particular dataset. In this research, we apply a simulated annealing optimization procedure to identify the values of four parameters used in a density-based spatial clustering with additional noise and time entropy (DBSCAN-TE) algorithm. We develop a spatial accuracy scoring function to use in the calibration methodology and identify paths for future research.

Towards Building a Faster and Incentive Enabled Privacy-Preserving Proof of Location Scheme from GTOTP

In recent years, there has been significant growth in location-based services (LBSs) and applications. These services empower users to transmit their location data to location service providers, thereby facilitating the provisioning of pertinent resources and services. However, in order to prevent malicious users from sending fake location data, users must attest to their location for service providers, namely, through a proof of location (PoL). Such a proof should additionally prevent attackers from being able to obtain users’ identity and location information through it. In this paper, we propose an efficient privacy-preserving proof of location (PPPoL) scheme. The scheme is based on the standard cryptographic primitives, including Group Time-based One-Time Password (GTOTP) and public key encryption, which achieves entity privacy, location privacy, and traceability. Unlike the previous GTOTP-based PPPoL scheme, our scheme enables instant location verification with additional hash operations. To encourage the active participation of witnesses in location proofs, we propose an incentive mechanism based on smart contracts. Additionally, we implement a proof of concept of our PPPoL scheme on an Android device. Our experimental results show that proof generation and verification time are on the order of milliseconds. Meanwhile, the total overhead for the incentive mechanism amounts to 0.0011 ETH. This result is practical for mobile device-based LBSs.

Towards achieving geo‐indistinguishability for 3D GPS location: A 3D Laplace mechanism approach

SummaryAs the scope of human exploration continues to expand from land to space and the oceans, location‐based data analysis and services are facing unprecedented opportunities and challenges. The wide application of various services based on spatial location in the fields of medical, transportation, financial, social and so forth not only provides great convenience but also exposes the disadvantages of location privacy leakage. Most present location privacy protection solutions are only relevant to 2D locations. However, with the widespread use of positioning‐enabled aircraft, sensing devices and acquisition equipment, there is an urgent need to achieve privacy protection for 3D spatial location data. Therefore, this article proposes a 3D geo‐indistinguishability perturbation method for 3D GPS Locations. The suggested approach calculates GPS coordinate perturbation accurately using the 3D Laplace mechanism, allowing for point‐to‐point perturbation while protecting the privacy of 3D spatial GPS coordinates. Experimental examination of real spatial location datasets demonstrates that the proposed 3D spatial location perturbation method outperforms other existing methods in terms of location service quality loss, data availability, and operational efficiency with the same privacy budget.

Streamlining trajectory map-matching: a framework leveraging spark and GPU-based stream processing

Real-time online trajectory map-matching has emerged as a critical component in the era of location-based services (LBS) and intelligent transportation systems (ITS). It refers to the process of aligning a user’s GPS trajectory data with the corresponding road network in real-time. This technology has significant implications for various industries and applications. As our reliance on LBS and ITS continues to grow, the demand for faster, more accurate, and more reliable trajectory map-matching methods becomes increasingly important. Contemporary online map-matching predominantly employs stream processing techniques. Based on stream processing frameworks, we propose a heterogeneous hybrid architecture for map-matching. The architecture integrates Spark Streaming and graphics processing unit (GPU) heterogeneous computing for the first time. The hidden Markov model is employed as the map-matching algorithm, and Spark Streaming serves as the distributed processing platform. We conduct map-matching experiments using a GPS taxi trajectory dataset in Beijing’s Haidian District. The results demonstrate that in comparison to other analogous research, our framework’s performance has increased by over ten times, possessing a superior data processing capability and lower latency. This research provides a novel approach of stream-based heterogeneous computation for processing large-scale geographic data.

Implementation on Location-Based Services Using Geofencing

Geo-fencing (geofencing) is an innovative technology that utilizes the Global Positioning System (GPS) to create virtual boundaries, enabling various applications such as electronic toll collection, contextual advertising, and tourist information systems. This system aims to explore the effectiveness of spatial data in enhancing advertising performance. By leveraging tracking systems and monitoring based on satellite navigation services, including the geofencing function, businesses can accurately pinpoint their locations, boost sales, and improve overall efficiency. Geofencing facilitates remote monitoring of defined geographic areas through virtual fences, allowing for automatic detections when tracked mobile objects enter or exit these areas. This paper introduces the fundamental concepts of geofencing and explores its applications in the transportation and logistics sector. Key Words: Geofence boundaries, Shopping app, User preferences, Geofence triggers, GPS technology, User notifications, Marketing strategy, Mobile commerce, Location-aware technology, Mobile user engagement, Retail industry.

Efficient and Secure Communication in VANETS Using Block Chain

We have made great progress in resolving the crucial problem of location privacy in Vehicular Impromptus Networks (VANETs), an essential component of Intelligent Traffic Systems (ITS), using our innovative block chain-based trust management methodology. VANETs' high inherent mobility has long presented security issues, especially when it comes to using Location-Based Services (LBS). Our creative solution gives cars the ability to request LBS while protecting their personal data with a secure verification process. Vehicle privacy is protected via the creation of anonymous shrouding zones, and vehicle behaviour is controlled and governed by an advanced trust management algorithm. By using block chain technology, the system's data security is strengthened even more, resulting in a strong, impenetrable architecture. Thorough testing has proven the system's resilience against a range of assaults based on trust models, proving its efficacy in protecting vehicle privacy. The outcomes of our simulation demonstrate the practicality of our suggested method in actual VANET circumstances, pointing to the possibility of a new era in safe and private ITC systems.

After Forced Relocation: Assessing the Impact of Urban Regeneration on Residents’ Living Spaces in Wuhan, China

Tracking forced relocation due to urban regeneration can help deepen the understanding of urban regeneration beyond the perceived limits of displacement. Existing research has not sufficiently explored new experiences following the adjustment of China’s urban-regeneration policy. This article explores an approach that uses location-based service data to study forced relocatees for urban regeneration, which can be generalized across different regions to expand international knowledge and comparative research. Focusing on Wuhan, the impact of urban regeneration on the living environment of forced relocatees was evaluated from three aspects: housing conditions, neighborhood facilities, and jobs–housing commuting, using the difference-in-difference model. The research found: (1) most forced relocatees chose to live near their original residences; and (2) urban regeneration has improved housing conditions and decreased jobs–housing commuting for forced relocatees but has a negative effect on neighborhood facilities. We conclude by discussing a methodology for applying mobile phone data to urban-regeneration studies.

Evacuation information methodology that combined a flooded environment and pedestrian behavioral model

Disaster information influences decision-making. Wireless internet and location-based services, aided by recent technological advances, have laid the foundation for providing personalized disaster information. Despite the provision of simple disaster forecasts and warnings, provision of specific evacuation actions (“where” and “how,” i.e., location and reachability of safe areas) and location-based personalized information that consider real-time disaster situations are a pressing priority. Thus, this study proposed and developed a three-step methodology for providing disaster information during floods that combined a flooded environment and pedestrian behavioral model, namely, 1) prediction of the flooded environment, 2) analysis of evacuation routes based on the movement time of evacuees according to the flood depth, and 3) provision of evacuation information by selecting the optimal evacuation route. This model was demonstrated in the Gangnam Station area in Seoul, Korea. Flooded environment prediction modeling of the target area was performed by applying the 100-year rainfall design to the Storm Water Management Model. The optimal evacuation route, according to the flood depth, was selected by applying the behavioral model (movement time calculation formula) for each evacuee derived through an evacuation behavior experiment. The pre- and post-flooding age- and gender-wise total evacuation distance and time were compared and analyzed. Post-flooding, the evacuation distance and time increased by 168.3% (381.34 m) and 172.3% (3.16 min) on average, respectively, indicating that evacuees must travel farther and longer. The analysis of post-flooding evacuation route change types revealed changes in evacuation distance and time in cases where shelters and evacuation routes and time were significantly different depending on evacuee characteristics (age and gender). This indicated that specific and visual disaster information can be provided according to evacuees’ age and gender using the proposed methodology. This study can serve as the basis for designing an advanced and personalized disaster information system that can provide specific evacuation actions to aid fast evacuation decision-making during floods.

Privacy-protecting predictive cache method based on blockchain and machine learning in Internet of vehicles

To solve the privacy leakage problem faced by Internet of Vehicles (IoV) users when enjoying location-based services (LBS), a privacy-protecting predictive cache method based on blockchain and machine learning (BML-PPPCM) is proposed. First, a Bi-directional Long-Short Term Memory (Bi-LSTM) model is used to predict query requests over a future period based on historical request information. The predicted results are recommended to neighbors and broadcast to requestors. Then, deep Q-learning (DQN) is utilized to determine the optimal cache decision. Finally, a trust mechanism is introduced to calculate trust values, and blockchain is used to store transaction data and trust data, preventing malicious tampering by attackers. The simulation results show that BML-PPPCM has a higher cache hit ratio than other similar schemes and performs well in privacy protection and suppression of malicious and incentive denial of service providers.

Vector-Indistinguishability: Location Dependency Based Privacy Protection for Successive Location Data

With the wide use of GPS enabled devices and Location-Based Services, location privacy has become an increasingly worrying challenge to our community. Existing approaches provide solid bases for addressing this challenge. However, they mainly focus on independent location perturbation or whole trajectory perturbation with little attention to location dependency based perturbation between two successive locations. This may result in that perturbed successive locations lose mutual distance and direction dependency, which in turn can lead to significant data utility loss. To address this problem, we propose a new location dependency based privacy notion named as vector-indistinguishability (vector-ind). Vector-ind defines a vector to represent the dependency relationship between two successive locations. Correspondingly, it consists of distance-indistinguishability for distance dependency and direction-indistinguishability for direction dependency. Noise generation for applying Differential Privacy is based on the distance and direction, hence can reflect the location dependency to ensure data utility after perturbation. We also present four mechanisms to achieve vector-ind notion. Finally, we evaluate the empirical privacy and utility of vector-ind in real-world GPS data to demonstrate how our proposed mechanisms for vector-ind can protect location privacy with high data utility in successive location data.

A Comprehensive Study of Trajectory Forgery and Detection in Location-Based Services

A Comprehensive Study of Trajectory Forgery and Detection in Location-Based Services

Determining the Antenna Phase Center for the High-Precision Positioning of Smartphones.

In recent years, smartphones have emerged as the primary terminal for navigation and location services among mass users, owing to their universality, portability, and affordability. However, the highly integrated antenna design within smartphones inevitably introduces interference from internal signal sources, leading to a misalignment between the antenna phase center (APC) and the antenna geometric center. Accurately determining a smartphone's APC can mitigate system errors and enhance positioning accuracy, thereby meeting the increasing demand for precise and reliable user positioning. This paper delves into a detailed analysis of the generation of Global Navigation Satellite System (GNSS) receiver antenna phase center errors and proposes a method for correcting the receiver antenna phase center. Subsequently, a smartphone positioning experiment was conducted by placing the smartphone on an observation column with known coordinates. The collected observations were processed in static relative positioning mode, referencing observations from geodetic-grade equipment, and the accuracy of the static relative positioning fixed solution was evaluated. Following weighted estimation, we determined the antenna phase center of the Xiaomi Mi8 and corrected the APC. A comparison of the positioning results of the Xiaomi Mi8 before and after APC correction revealed minimal impact on the standard deviations (STDs) but significant influence on the root mean square errors (RMSEs). Specifically, the RMSEs in the E/N/U direction were reduced by 59.6%, 58.5%, and 42.0%, respectively, after APC correction compared to before correction. Furthermore, the integer ambiguity fixing rate slightly improved after the APC correction. In conclusion, the determination of a smartphone's APC can effectively reduce system errors in the plane direction of GNSS positioning, thereby enhancing smartphone positioning accuracy. This research holds significant value for advancing high-precision positioning studies related to smartphones.

Peaked too soon? Analyzing the shifting patterns of PM peak period travel in Southern California

Daily vehicle travel collapsed with the onset of the COVID-19 pandemic in early 2020 but largely bounced back by late 2021. The pandemic caused dramatic changes to working, schooling, shopping, and leisure activities, and to the travel associated with them. Several of these changes have so far proven enduring. So, while overall vehicle travel had largely returned to pre-pandemic levels by late 2021, the underlying drivers of this travel have likely changed.To examine one element of this issue, we analyzed whether patterns of daily trip-making shifted temporally between the fall of 2019 and 2021 in the Greater Los Angeles megaregion. We used location-based service data to examine vehicle trip originations for each hour of the day at the U.S. census block group level in October 2019 and October 2021. We observed notable shifts in the timing of post-pandemic PM peak travel, so we examined changes in the ratio of mid-week trips originating in the early afternoon (12–3:59 PM) and the late afternoon/early evening (4–7:59 PM).We found a clear shift in the temporal distribution of PM trip-making, with relatively more late PM peak period trip-making prior to the pandemic, and more early PM peak trip-making in 2021. The peak afternoon/evening trip-making hour shifted from 5–5:59 PM to 3–3:59 PM. We also found that afternoon/evening trip-making in each year is largely explained by three workplace-area/school-area factors: (1) the number of schoolchildren in a block group (earlier); (2) block groups with large shares of potential remote workers (earlier), and (3) block groups with large shares of low-wage jobs and workers of color (later, except for Black workers in 2021). We found the earlier shift in PM peak travel between pre- and late-pandemic periods to be explained most by (1) higher shares of potential remote workers and (2) higher shares of low-wage jobs and workers of color. These findings suggest that the rise of working from home has likely led to a shift in PM peak travel earlier in the afternoon when school chauffeuring trips are most common. This is especially true for low-income workers and workers of color.

Trustworthy Localization in IoT Networks: A Survey of Localization Techniques, Threats, and Mitigation.

The Internet of Things (IoT) has revolutionized the world, connecting billions of devices that offer assistance in various aspects of users' daily lives. Context-aware IoT applications exploit real-time environmental, user-specific, or situational data to dynamically adapt to users' needs, offering tailored experiences. In particular, Location-Based Services (LBS) exploit geographical information to adapt to environmental settings or provide recommendations based on users' and nodes' positions, thus delivering efficient and personalized services. To this end, there is growing interest in developing IoT localization systems within the scientific community. In addition, due to the sensitivity and privacy inherent to precise location information, LBS introduce new security challenges. To ensure a more secure and trustworthy system, researchers are studying how to prevent vulnerabilities and mitigate risks from the early design stages of LBS-empowered IoT applications. The goal of this study is to carry out an in-depth examination of localization techniques for IoT, with an emphasis on both the signal-processing design and security aspects. The investigation focuses primarily on active radio localization techniques, classifying them into range-based and range-free algorithms, while also exploring hybrid approaches. Next, security considerations are explored in depth, examining the main attacks for each localization technique and linking them to the most interesting solutions proposed in the literature. By highlighting advances, analyzing challenges, and providing solutions, the survey aims to guide researchers in navigating the complex IoT localization landscape.

Privacy-preserving data integration and sharing in multi-party IoT environments: An entity embedding perspective

The increasing prevalence of IoT applications highlights the urgency for insightful data fusion and information acquisition, boosting data integration and sharing needs. However, challenges arise in multi-party data sharing due to inherent data heterogeneity and privacy concerns. To address these issues, this paper discusses the feasibility of using embedding vectors as the semantic representation, aiming to enhance interoperability across diverse data sources and lay the foundation for natural language-based data querying. At the specific method level, this paper proposes an improved entity tree embedding algorithm to reduce information loss and ameliorate the representation of entity semantics. Additionally, a privacy preservation mechanism based on the entity embedding approach is introduced to provide privacy protection for text-based data. Experimental results on address data demonstrate the mechanism’s efficacy in achieving privacy protection comparable to the widely adopted 2D Laplace plane noise method. Furthermore, incorporating the entity tree embedding into the privacy mechanism could yield more robust and reasonable results regarding location privacy and service quality, signifying the validity of the entity embedding results.

Model-Driven Deep Neural Network for Enhanced AoA Estimation Using 5G gNB

High-accuracy positioning has become a fundamental enabler for intelligent connected devices. Nevertheless, the present wireless networks still rely on model-driven approaches to achieve positioning functionality, which are susceptible to performance degradation in practical scenarios, primarily due to hardware impairments. Integrating artificial intelligence into the positioning framework presents a promising solution to revolutionize the accuracy and robustness of location-based services. In this study, we address this challenge by reformulating the problem of angle-of-arrival (AoA) estimation into image reconstruction of spatial spectrum. To this end, we design a model-driven deep neural network (MoD-DNN), which can automatically calibrate the angular-dependent phase error. The proposed MoD-DNN approach employs an iterative optimization scheme between a convolutional neural network and a sparse conjugate gradient algorithm. Simulation and experimental results are presented to demonstrate the effectiveness of the proposed method in enhancing spectrum calibration and AoA estimation.

MCN4Rec: Multi-level Collaborative Neural Network for Next Location Recommendation

Next location recommendation plays an important role in various location-based services, yielding great value for both users and service providers. Existing methods usually model temporal dependencies with explicit time intervals or learn representation from customized point of interest (POI) graphs with rich context information to capture the sequential patterns among POIs. However, this problem is perceptibly complex, because various factors, e.g., users’ preferences, spatial locations, time contexts, activity category semantics, and temporal relations, need to be considered together, while most studies lack sufficient consideration of the collaborative signals. Toward this goal, we propose a novel M ulti-Level C ollaborative Neural N etwork for next location Rec ommendation (MCN4Rec). Specifically, we design a multi-level view representation learning with level-wise contrastive learning to collaboratively learn representation from local and global perspectives to capture complex heterogeneous relationships among user, POI, time, and activity categories. Then, a causal encoder-decoder is applied to the learned representations of check-in sequences to recommend the next location. Extensive experiments on four real-world check-in mobility datasets demonstrate that our model significantly outperforms the existing state-of-the-art baselines for the next location recommendation. Ablation study further validates the benefits of the collaboration of the designed sub-modules. The source code is available at https://github.com/quai-mengxiang/MCN4Rec .

Exploring factors and customer perceptions of airport services: A quantitative textual analysis

<span lang="EN-GB">This study conducted an analysis of 1500 user generated content from Google Travel to examine the factors influencing airport services and customer perceptions. Quantitative textual analysis is employed to extract meaningful insights. Our findings highlighted the most frequently used words in airport user generated content, reflecting critical aspects of airport experiences such as the airport itself, the quality of service, and international travel. A cluster analysis revealed five distinct clusters, representing flight operations, location, views, customer feelings, and intangible services. A co-occurrence network analysis showed strong correlations among keywords associated with positive experiences, underscoring the importance of service quality and infrastructure in customer satisfaction. Furthermore, through topic modeling, we categorized words into five distinct groups: airport flights, ground services, international services, customer experience, and location. The practical implications of this study are substantial. The insights can help airport management identify strengths and areas needing improvement, ultimately enhancing customer satisfaction and the overall airport experience.</span>

An Efficient Convolutional Denoising Autoencoder-Based BDS NLOS Detection Method in Urban Forest Environments.

The BeiDou Navigation Satellite System (BDS) provides real-time absolute location services to users around the world and plays a key role in the rapidly evolving field of autonomous driving. In complex urban environments, the positioning accuracy of BDS often suffers from large deviations due to non-line-of-sight (NLOS) signals. Deep learning (DL) methods have shown strong capabilities in detecting complex and variable NLOS signals. However, these methods still suffer from the following limitations. On the one hand, supervised learning methods require labeled samples for learning, which inevitably encounters the bottleneck of difficulty in constructing databases with a large number of labels. On the other hand, the collected data tend to have varying degrees of noise, leading to low accuracy and poor generalization performance of the detection model, especially when the environment around the receiver changes. In this article, we propose a novel deep neural architecture named convolutional denoising autoencoder network (CDAENet) to detect NLOS in urban forest environments. Specifically, we first design a denoising autoencoder based on unsupervised DL to reduce the long time series signal dimension and extract the deep features of the data. Meanwhile, denoising autoencoders improve the model's robustness in identifying noisy data by introducing a certain amount of noise into the input data. Then, an MLP algorithm is used to identify the non-linearity of the BDS signal. Finally, the performance of the proposed CDAENet model is validated on a real urban forest dataset. The experimental results show that the satellite detection accuracy of our proposed algorithm is more than 95%, which is about an 8% improvement over existing machine-learning-based methods and about 3% improvement over deep-learning-based approaches.