Location-aware Services Research Articles

Framework to process vehicles uncertain locations for intelligent transportation

Many factors affect the precision and accuracy of location data. These factors include, but not limited to, environmental obstructions (e.g., high buildings and forests), hardware issues (e.g., malfunctioning and poor calibration), and privacy concerns (e.g., users denying consent to fine-grained location tracking). These factors lead to uncertainty about users’ location which in turn affects the quality of location-aware services. This paper proposes a novel framework called UMove, which stands for uncertain movements, to manage the trajectory of moving objects under location uncertainty. The UMove framework employs the connectivity (i.e., links between edges) and constraints (i.e., travel time and distance) on road network graphs to reduce the uncertainty of the object’s past, present, and projected locations. To accomplish this, UMove incorporates (i) a set-based pruning algorithm to reduce or eliminate uncertainty from imprecise trajectories; and (ii) a wrapper that can extend user-defined probability models designed to predict future locations of moving objects under uncertainty. Intensive experimental evaluations based on real data sets of GPS trajectories collected by Didi Chuxing in China prove the efficiency of the proposed UMove framework. In terms of accuracy, for past exact-location inference, UMove achieves rates from 88% to 97% for uncertain regions with sizes of 75 meters and 25 meters respectively; for future exact-location inference, accuracy rates reach up to 72% and 82% for 75 meters and 25 meters of uncertain regions.

A Deep Learning-Based Hybrid CNN-LSTM Model for Location-Aware Web Service Recommendation

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.

FedGST: An Efficient Federated Graph Neural Network for Spatio-temporal PoI Recommendation

With the proliferation of sensor networks in urban areas, vast amounts of data from location-based social network platforms are now available, thus enabling the stakeholders to develop location-aware services for the end users. PoI (Point of Interests) recommendation is one of the most trending services. PoI recommendation models are developed by considering geographical information and historical records. More recently, with the demand for achieving PoI recommendations on mobile devices, embedded devices, or Internet of Things (IoT) devices, the challenges posed by the limited computational resources, malicious clients, and data silos, the model performance gets immensely affected. To address the above challenges and achieve efficient PoI recommendations, we propose an efficient federated graph learning-based model for mining complex spatiotemporal features to generate recommendations. We implement a GRU-based encoder-decoder to learn the temporal hidden state embeddings. Simultaneously, a 2-layer graph network is used to understand spatial embedding vectors. We also introduce an efficient contribution evaluation method to speed up the training process and improve the recommendation performance. Experiments on the PoI recommendation task based on real-life check-in data validate the effectiveness of our proposed model, and the results indicate that our recommendation model can achieve competitive results with lower computational costs.

LAAKA: Lightweight Anonymous Authentication and Key Agreement Scheme for Secure Fog-Driven IoT Systems

Fog computing is a new computing model with limited resources located near the Internet of Things (IoT) devices to provide low transmission delay, mobility, and location awareness services. However, fog technology not only extends the cloud resources but also inherits its properties and security problems, such as device capture, man-in-the-middle, secret key compromise, identity impersonation, and message replay attacks. These vulnerabilities pose significant risks to the integrity of IoT-fog communications. Nevertheless, the existing mutual authentication approaches either suffer from high computational overheads or fail to provide secure mutual authentication. To address this gap, this paper introduces LAAKA, a Lightweight Anonymous Authentication and Key Agreement scheme. Considering the constrained resources of IoT and fog devices, LAAKA utilizes lightweight operations such as hash function and bitwise XOR. Its main objective is to facilitate mutual authentication and establish secure session keys between IoT devices and fog servers, making it useful for various IoT applications. We validate the robustness of LAAKA against various security threats by conducting comprehensive formal security analysis, including Burrows-Abadi-Needham (BAN) logic and Random Oracle Model (ROM), as well as informal analysis. The efficacy of our scheme is further demonstrated through evaluation with the Scyther tool. Compared to other proposed authentication approaches, the results illustrate the superior performance and efficiency of our approach in enhancing the security features, minimizing the computational cost, and optimizing storage utilization.

Federated Learning-Based Service Caching in Multi-Access Edge Computing System

Multi-access edge computing (MEC) brings computations closer to mobile users, thereby decreasing service latency and providing location-aware services. Nevertheless, given the constrained resources of the MEC server, it is crucial to provide a limited number of services that properly fulfill the demands of users. Several static service caching approaches have been proposed. However, the effectiveness of these strategies is constrained by the dynamic nature of the system states and user demand patterns. To mitigate this problem, several investigations have been conducted on dynamic service caching techniques that can be categorized as centralized and distributed. However, centralized approaches typically require gathering comprehensive data from the entire system. This increases the burden on resources and raises concerns regarding data security and privacy. By contrast, distributed strategies require the formulation of complicated optimization problems without leveraging the inherent characteristics of the data. This paper proposes a distributed service caching strategy based on federated learning (SCFL) that works efficiently in a distributed system with user mobility. An autoencoder model is utilized to extract features regarding the service request distribution of individual MEC servers. The global model is then generated using federated learning, which is utilized to make service-caching decisions. Extensive experiments are conducted to demonstrate that the performance of the proposed method is superior to that of other methods.

Exploiting User Mobility for Time-aware POI Recommendation in Social Networks

Location-Based Social Networks (LBSNs) have recently gained increasing popularity. Timeaware Point-of-Interest(POI) recommendation is one of the most important location-aware services, which can recommend locations to a target user at the specific time based on check-in history. However, current techniques ignore user’s mobility within a region, which plays a vital role in the POI chosen decision. Most of existing algorithms fail to capture the latent relations behind the temporal factors and geographical influence. In this paper, we propose a novel hybrid Time-aware POI Recommendation model based on User Mobility: TPR-UM, which improves the POI recommendation accuracy greatly. More specifically, by introducing the implicit region factor, we capture users’ mobility through collective user actions and geographical properties of locations. We generate the check-in patterns based on different time intervals and different regions to exploit the latent relations between temporal factors and geographical influence. Finally, we conduct comprehensive experiments on two real-world datasets, Gowalla and Foursquare. The experimental results demonstrate that our hybrid method is effective and outperforms other state-of-the-art algorithms in terms of precision, recall and Fβ measurement.

Mobile recognition and positioning for multiple visible light communication cells using a convolutional neural network

The industrial Internet of Things (IIoT) environment involves multiple production items, such as robots and automated guided vehicles (AGVs), among others. The practical industrial scenario requires communication of production items while also considering mobile recognition and positioning. Hence the perception approach requires not only combining communications but also realizing the recognition and positioning of multiple communication cells. This Letter proposes a multi-optical cell recognition and positioning framework based on LED image features. The LED images are obtained by a CMOS image sensor. This framework utilizes convolutional neural networks (CNN) to train LED images for recognition between multiple optical cells and locates precise positions through region recognition within the optical cells. The experimental results show that the mean accuracy of the CNN model for two LED cells is above 99%, and the mean accuracy of region recognition within the optical cell is as high as 100%, which is significantly better than other traditional recognition algorithms. Therefore, the proposed framework can provide location-aware services for visible light communication and has a wide application prospect in IIoT.

Passive Location Estimation via Resonant Beam

High-accuracy indoor positioning is a demanding but challenging technology. In the Internet of Things applications, the existing indoor positioning schemes face the following challenges: 1) for the active positioning schemes, e.g., WiFi, Bluetooth, etc., the targets need power supply to emit signals; and 2) for the passive positioning schemes, e.g., Radar, Lidar, etc., the base station usually needs beam-steering control to aim at the targets for receiving sufficient reflective signals. Here, we propose a passive 3-dimensional monocular positioning scheme, resonant beam based positioning (RBP), which adopts a resonant beam for angle-of-arrival estimation and a time-of-flight module for distance estimation. We present the principle of the proposed scheme and establish analytical models and simulation tools for evaluating the impacting factors on the positioning accuracy including sensing noises, signal power, etc. To verify the RBP performance, we also present a testbed. Both numerical analysis and experimental measurements demonstrate that the positioning accuracy can be less than 1cm over 2m distance in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$15^{\circ }$</tex-math></inline-formula> field of view. With RBP, high-accuracy and monocular without computation and high complexity positioning can be achieved while the target needs no power supply and the base station needs no beam-steering control. RBP is an important candidate for providing location-aware services in the Internet of everything and the proposed simulation models provide guidelines for system analysis and design.

Trajectory personalization privacy preservation method based on multi-sensitivity attribute generalization and local suppression

Fast-developing mobile location-aware services generate an enormous volume of trajectory data while adding value to people’s lives. However, trajectory data contains not only location information, but also sensitive personal information. If the original trajectory data is published directly, it could result in serious privacy leaks. Most of the existing privacy-preserving trajectory publishing methods only protect the location information or set the same privacy preservation levels for all moving objects. To meet the users’ personalized privacy requirements and ensure the utility of trajectory location and sensitive information, we propose a trajectory personalized privacy preservation method based on multi-sensitivity attribute generalization and local suppression. First, we set different security levels for each trajectory by calculating the correlation between sensitive attributes to establish a sensitive attribute classification tree. Second, we generalized sensitive attributes based on privacy preservation levels for each trajectory, the trajectory data still at risk of privacy leakage after generalization was locally suppressed. Finally, an anonymized trajectory dataset was generated. Experimental results on real datasets demonstrated that our method could improve data availability while preserving privacy.

A Blockchain-Based Auction Framework for Location-Aware Services

As a critical factor in ensuring the growth of the electronic auction (e-auction) domain, the privacy and security of the participants (sellers and buyers) must always be guaranteed. Traditionally, auction data, including participant details, are stored in a third party (auctioneer) database. This leads to a high risk of a single point of failure in terms of privacy and security. Toward this end, blockchain technology has emerged as a promising decentralized communication paradigm to address such risks. This paper presents a blockchain-based auction framework as a decentralized e-auctioning framework for location-aware services. In particular, the framework consists of three components: pre-auctioning, main auctioning, and post-auctioning processes with four algorithms. Our primary focus is on location-aware services, such as storage space rental, apartment rental, transport hire, and parking space rental. The trading volumes are expected to be high; hence, simplifying the design is a crucial requirement. In addition to the benefits of eliminating the centralized entity (the auctioneer), fees are redistributed among participants as rewards. Further, we incorporate a service quality monitoring method that ranks the services provided by participants. This ranking allows participants to determine the rank of other participants with whom they wish to trade. We have conducted several experiments to evaluate the proposed framework’s cost feasibility and to ensure the ease of the business flow.

A Low-Cost and Efficient Spatial–Temporal Model for Indoor Localization “H-LSTMF”

The industrial Internet of Things (IIoT) requires location-aware services based on indoor localization. Wi-Fi-based indoor localization is a low-cost and straightforward method. Still, it does not perform satisfactorily because of the fluctuations of the signals due to the multipath effect, the movement of humans, objects, etc. Therefore, an optimized model is required to improve the performance by eliminating received signal strength indicator (RSSI) uncertainties and reducing noise and training time. The hierarchical-density-based spatial clustering of applications with noise (HDBSCAN) algorithm is available for clustering the fingerprint database to eliminate outliers. Furthermore, locally weighted regression (LWR) is available to smoothen the RSSI uncertainty at the input. Finally, the encoder-long short-term memory (LSTM)-encoder model has been proposed that can learn the temporal relationships among the data. A new model is proposed in this article that combines HDBSCAN, LWR, and encoder-LSTM-encoder methods and is named “H-LSTMF. ” The proposed model was evaluated in two distinct datasets. The first dataset was collected in a room of size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10\times $ </tex-math></inline-formula> 5 m (dataset I) with 50% fewer reference data points, and the other dataset was taken from the existing work for a room of size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$35\times16$ </tex-math></inline-formula> m (dataset II). It has been found that the proposed method improved the localization accuracy by 20% and 60% in dataset I and dataset II, respectively, when compared with the state-of-the-art algorithms.

Positioning and Sensing in 6G: Gaps, Challenges, and Opportunities

Among the key differentiators of 6G compared to 5G will be the increased emphasis on radio-based positioning and sensing. These will be utilized not only for conventional location-aware services and for enhancing communication performance but also to support new use case families with extreme performance requirements. This article presents a unified vision from stakeholders across the value chain in terms of both opportunities and challenges for 6G positioning and sensing as well as use cases, performance requirements, and gap analysis. Combined, this motivates the technical advances in 6G and guides system design.

Designing a 6G Testbed for Location: Use Cases, Challenges, Enablers and Requirements

Location will have a central role in Research and Development (R&D) towards 6G networks, both as a service offered by the network (improving the current offering of 5G) and as an input to increasingly location-aware services and network functions. To integrate location into 6G standards, it will be very important to design validation systems such as testbeds, even when the actual technology is not yet commercially available. This paper performs a review of the use cases and their requirements, enabling technologies in 6G, and challenges; and proposes a flexible testbed architecture for performing network location related R&D. This architecture will allow to deploy an evolving infrastructure which will allow early validation of 6G technologies.

Architecture, Protocols, and Algorithms for Location-Aware Services in Beyond 5G Networks

The automotive and railway industries are rapidly transforming with a strong drive toward automation and digitalization, with the goal of increased convenience, safety, efficiency, and sustainability. Since assisted and fully automated automotive and train transport services increasingly rely on vehicle-to-everything communications and high-accuracy real-time positioning, it is necessary to continuously maintain high-accuracy localization, even in occlusion scenes such as tunnels, urban canyons, or areas covered by dense foliage. In this article, we review the 5G positioning framework of the 3rd Generation Partnership Project in terms of methods and architecture and propose enhancements to meet the stringent requirements imposed by the transport industry. In particular, we highlight the benefit of fusing cellular and sensor measurements, and discuss required architecture and protocol support for achieving this at the network side. We also propose a positioning framework to fuse cellular network measurements with measurements by onboard sensors. We illustrate the viability of the proposed fusion-based positioning approach using a numerical example.

Characterization of human mobility based on Information Theory quantifiers

Location-aware services provide valuable information for capturing human mobility patterns. In this context, analyzing the mobility dynamics, such as the means of transportation and their speeds, leads to better solutions by understanding the underlying data generating process and identifying different patterns. Strategies based on extracting Information Theory measures associated with ordinal patterns methods, for example, Complex-Entropy Causality Plane and Fisher–Shannon Causality Plane, have reached relevant advancements in distinguishing different time series dynamics. Thus, they are promising tools to explain those complex behaviors to improve human mobility-based services. In this work, we aim to characterize the users’ means of transportation based on their speed time series derived from the Geolife dataset. Therefore, for each type of transportation, we observe the speed dynamics over time and correlate their associated Information Theory quantifiers with colored noises mapped onto the causal planes. Evaluation results show the potential of our study, allowing us to distinguish motorized and non-motorized means of transportation. Also, based on that mapping, we can estimate the transportation switching.

Dual-grained human mobility learning for location-aware trip recommendation with spatial–temporal graph knowledge fusion

Trip recommendation is a popular and significant location-aware service that can help visitors make more accurate travel plans. Its principal purpose is to provide a sequence of points of interest (POIs) for a visitor who has a specific demand, such as the number of attractions, start location, end location, etc. Previous studies either regard the trip recommendation task as a simplistic orienteering problem or endeavor to maximize the users’ visiting preferences and transitional regularities in a multi-round iteration manner. However, significant contexts such as long-term transitional dependencies and spatial–temporal correlations are under-explored. Although the emerging deep recursive models (e.g., recurrent neural networks) enable us to relieve the above limitations, there still exist three major challenges: (1) most conventional offline-homogeneous knowledge distillation approaches for key entities (e.g, POI and visiting time) could lead to the inadequacy of capturing the inherent heterogeneous interactions among the different entities and even result in the deviation of understanding human real individual preferences; (2) data sparsity in transitional regularity learning impedes the ability to comprehend human diverse mobility patterns; and (3) the lack of considering contextual facts such as attractive bias would degrade the generalization ability of the model. To remedy the above issues, this work presents a novel framework based on spatial–temporal graph representation learning, namely GraphTrip. Specifically, we first introduce a location-aware information fusion after building the spatial–temporal graph (ST-Graph). Then, a dual-grained human mobility learning module is proposed to address the sparsity of periodic regularity. In addition, we fuse the explicit information (e.g., POI popularity) behind the trip data as prior knowledge to facilitate the performance of trip inference. In the end, the experimental results conducted on five real-world trip datasets demonstrate our proposed GraphTrip achieves promising gains against several cutting-edge baselines, e.g., up to 5.75% and 4.45% improvements on Toronto regarding F1 and pairs-F1.

Antifragile and Resilient Geographical Information System Service Delivery in Fog Computing.

The demand for cloud computing has drastically increased recently, but this paradigm has several issues due to its inherent complications, such as non-reliability, latency, lesser mobility support, and location-aware services. Fog computing can resolve these issues to some extent, yet it is still in its infancy. Despite several existing works, these works lack fault-tolerant fog computing, which necessitates further research. Fault tolerance enables the performing and provisioning of services despite failures and maintains anti-fragility and resiliency. Fog computing is highly diverse in terms of failures as compared to cloud computing and requires wide research and investigation. From this perspective, this study primarily focuses on the provision of uninterrupted services through fog computing. A framework has been designed to provide uninterrupted services while maintaining resiliency. The geographical information system (GIS) services have been deployed as a test bed which requires high computation, requires intensive resources in terms of CPU and memory, and requires low latency. Keeping different types of failures at different levels and their impacts on service failure and greater response time in mind, the framework was made anti-fragile and resilient at different levels. Experimental results indicate that during service interruption, the user state remains unaffected.

A graph based approach for mining significant places in trajectory data

Significant place mining in spatiotemporal trajectory data is a key task for mobile pattern mining, useful for supporting location-aware services. State-of-the-art trajectory clustering algorithms utilize a density-based distance measure. However, some major problems with this approach are that (1) results are often inaccurate, especially on data of varying density, (2) the user must fine-tune many thresholds that are unintuitive to set, and (3) boundary points between clusters are often assigned to the wrong locations. Performance is also a major issue as many state-of-the-art algorithms have a very high time complexity. Motivated by these issues, this paper proposes an approach inspired by the data field theory and community detection. It is a graph-based significant place mining algorithm, called GB-SPM, for capturing and characterizing the essence of similarity between nodes. GB-SPM first applies a novel low index neighborhood velocity point filtration method to extract characteristic points. Then, a characteristic point index neighborhood is used to map them to graph nodes. In this way, the original problem is transformed into a community detection problem in complex community networks. Finally, a new edge weight metric is proposed to capture and characterize the nature of similarity between nodes. To evaluate clustering quality, we used the Silhouette (SI) for unannotated data to value inter-cluster separation and intra-cluster homogeneity. To evaluate mining effectiveness, we used Matthew’s correlation coefficient (MCC) for annotated data. Numerous experiments were carried out on real world datasets, and the accuracy and performance of the designed algorithm was compared with the state-of-the-art algorithms. Results show that GB-SPM improves on average SI by 13.9%, MCC by 20.7%, and runtime by 5.15 times.

IoT Based Indoor and Outdoor Localization Framework with WI-FI Fingerprinting Based on Scalable Resnet Models

A scalable indoor localization technique is a vital technology for future large-scale location-aware services covering a complex of multi-story buildings. Our research on the usage of ResNet for scalable building/floor categorization and floor-level position estimation based on Wi-Fi fingerprinting is presented in this publication. Building and floor-level coordinates are estimated using our new ResNet architecture, which utilizes a stacked autoencoder to reduce feature space and a feed-forward classifier to classify multiple labels of building/floor/location. This architecture is the foundation for our multi-building and multi-floor indoor localization system based on Wi-Fi fingerprinting. On the Jaume I University (UJI) campus in Spain, we test the accuracy of building/floor estimation and floor-level coordinates estimation for three buildings with four or five stories each. ResNet-based indoor localization using a single ResNet has been proven to be feasible, with results that are close to the state of the art. One ResNet is all that is needed in order for the proposed indoor localization system based on Wi-Fi fingerprinting to function at levels close to the current state of the art, allowing it to be implemented with less complexity and less energy consumption on mobile devices.

Guest Editorial Special Issue on Integrated Sensing and Communication—Part I

Driving a gradual integration of the physical and digital worlds is perceived to become a reality in the 6G era, from vehicles to drones, from surveillance facilities in cities to agricultural tools in the countryside. Jointly motivated by recent advances in communication and signal processing, radio sensing functionality can be integrated into a 6G radio access network (RAN) in a low-cost and fast manner. That is, future networks have the ability to “see” the physical world through imaging and measuring the surrounding environment, which enables advanced location-aware services, ranging from the physical to application layers. In essence, a radio emission could simultaneously convey communication data from the transmitter to the receiver and deliver environmental information from the scattered echoes. Therefore, sensing and communication (S&C) functionalities are possible to be co-designed to utilize resources efficiently and to assist each other for mutual benefits. This type of research is typically referred to as integrated sensing and communication (ISAC).