User Location Information Research Articles

Inter-User Distance Estimation Based on a New Type of Fingerprint in Massive MIMO System for COVID-19 Contact Detection.

In this paper, we address the challenging task of estimating the distance between different users in a Millimeter Wave (mmWave) massive Multiple-Input Multiple-Output (mMIMO) system. The conventional Time of Arrival (ToA) and Angle of Arrival (AoA) based methods need users under the Line-of-Sight (LoS) scenario. Under the Non-LoS (NLoS) scenario, the fingerprint-based method can extract the fingerprint that includes the location information of users from the channel state information (CSI). However, high accuracy CSI estimation involves a huge overhead and high computational complexity. Thus, we design a new type of fingerprint generated by beam sweeping. In other words, we do not have to know the CSI to generate fingerprint. In general, each user can record the Received Signal Strength Indicator (RSSI) of the received beams by performing beam sweeping. Such measured RSSI values, formatted in a matrix, could be seen as beam energy image containing the angle and location information. However, we do not use the beam energy image as the fingerprint directly. Instead, we use the difference between two beam energy images as the fingerprint to train a Deep Neural Network (DNN) that learns the relationship between the fingerprints and the distance between these two users. Because the proposed fingerprint is rich in terms of the users’ location information, the DNN can easily learn the relationship between the difference between two beam energy images and the distance between those two users. We term it as the DNN-based inter-user distance (IUD) estimation method. Nonetheless, we investigate the possibility of using a super-resolution network to reduce the involved beam sweeping overhead. Using super-resolution to increase the resolution of low-resolution beam energy images obtained by the wide beam sweeping for IUD estimation can facilitate considerate improvement in accuracy performance. We evaluate the proposed DNN-based IUD estimation method by using original images of resolution 4 × 4, 8 × 8, and 16 × 16. Simulation results show that our method can achieve an average distance estimation error equal to 0.13 m for a coverage area of 60 × 30 m2. Moreover, our method outperforms the state-of-the-art IUD estimation methods that rely on users’ location information.

Influence Spread in Location-Based Social Network: An Efficient Algorithm of Epidemic Controlling

Much work has already been studied on the interrelation between the epidemic spreading and awareness spreading to prevent infections in a social network. By selecting seed users to spread awareness, we can control epidemic spreading. However, selecting seed users with the maximum influential users may not be the best solution in location-based social networks. Therefore, it is challenging to determine users to spread the information (the awareness of prevention) in these networks. The minimized epidemic infection ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MEI</i> ) problem aims to find a seed set with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> seed users such that the infection users can be minimized. In this article, we propose a piecewise function to measure the probability of each user being infected, which considers the distance and time. Then, we propose an algorithm called location-infected-greedy (LIG) to solve the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MEI</i> problem by finding the seed nodes that consider the probability of infection, time of check-in, location information, and influence of users. In the meantime, LIG can obtain an upper bound of the data-dependent approximate ratio, and it runs in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(kn^{2})$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> is the total number of nodes and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> is the number of seed nodes. Finally, extensive contrast experiments on real-world location-based social networks show that our algorithm is efficient and effective.

Joint Hybrid 3D Beamforming Relying on Sensor-Based Training for Reconfigurable Intelligent Surface Aided TeraHertz-Based Multiuser Massive MIMO Systems

Terahertz (THz) systems have the benefit of high bandwidth and hence are capable of supporting ultra-high data rates, albeit at the cost of high pathloss. Hence they tend to harness high-gain beamforming. Therefore a joint hybrid 3D beamformer relying on sophisticated sensor-based beam training and channel estimation is proposed for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Reconfigurable Intelligent Surface</i> (RIS) aided THz Multi-user Massive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Multiple Input Multiple Output</i> (MIMO) systems. A novel joint subarray based THz base station (BS) architecture and the corresponding sub-RIS is proposed. The BS, RIS and receiver antenna arrays of the users are all <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Uniform Planar Arrays</i> (UPAs). Moreover, the conditions of maintaining the orthogonality of the proposed joint architecture are derived in support of spatial multiplexing. The closed-form expressions of the near-field and far-field path-loss are also derived. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ultra-wideband</i> (UWB) sensors are integrated into the RIS and the user location information obtained by the UWB sensors is exploited for channel estimation. The optimal active and passive beamforming schemes are also derived. Moreover, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Precise Beamforming Algorithm</i> (PBA) for joint RIS phase shift and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">user equipment</i> (UE) receiver beamforming is proposed, which further improves the beamforming accuracy by circumventing the performance limitations imposed by positioning errors. Our simulation results show that the proposed system significantly improves the spectral efficiency, despite its low complexity. Compared to the scheme operating without PBA, our proposed scheme increases the spectral efficiency on average by 10.41%, 10.17%, and 5.19% for the three farfield configurations, and by 5.05% and 3.95% for the two nearfield configurations, respectively. This makes our solution eminently suitable for delay-sensitive applications.

Mathematical Solution of ULTE Based Salt Generation Password Based Key Derivation Function (PBKDF)

In modern life, users use the cloud spaces for storing important data and valuable information. To avoid security problems, every cloud storage provider requires help from several security mechanisms. Every mechanism has its own security scheme. Most of them use the PBKDF (Password-Based Key Derivation Function) and Salt for generating encryption keys. By analyzing, we found that unique and random Salt plays a vital role for generating keys. But to create powerful keys, we must avoid pseudorandom generators for making salt because hackers can easily crack the key by using it. We have to take inputs from beyond expected sources. We proposed an algorithm by which we can generate Salt by using the client’s real-time environmental information known as Location Information of user, User Time Information with Entropy Data at key generation time. In this paper, we focus on simulation result and compare each algorithm with the proposed algorithm ULTE. Simulation result represents the whole statistical distribution as stated in the algorithm. The result shows that ULTE has provided excellent performance than other algorithms.

Indoor Smart Design Algorithm Based on Smart Home Sensor

Modern home furnishings have solved the most basic housing problems, but how to make homes more informatized and modern has become the focus of people’s growing concern. With the rapid development of information technology, improving the intelligent level of family life and modern lifestyle is bound to be the trend of future development. This paper studies a smart home control system based on wireless sensor network positioning, which can perceive the home environment through the sensor module, and the control module can automatically control common electrical appliances to achieve real-time data monitoring and alarm functions. Specifically, it includes a positioning module, a communication module, and a server: the positioning module is connected to the server through the communication module, and the positioning module is used to obtain user location information in real time and report user location information to the server at intervals. The server receives the user reported at intervals through the communication module. Location information and remotely control smart home devices based on user location information. The research results show that the method proposed in this paper can separately monitor each part of the home and send it to the home appliance control module through the server to control the home appliance, optimize the living environment, and realize the remote control and monitoring of the smart home.

Protecting user privacy in location-based services over road networks

ABSTRACT In location-based services (LBS), the users with mobile devices share information such as their locations and queries to obtain desired services. Additionally, LBS providers are often able to track or record the movements of their users with great spatial and temporal accuracy. However, user location information can be misused by LBS providers or attackers, which creates privacy problems for users. This raises concerns about privacy breaches; therefore, many approaches have been proposed to protect user privacy. Thus, in previous works, the authors have proposed approaches to maintain the privacy of mobile users in the Euclidean space. However, it is difficult to apply current methods of Euclidean space to the environment of the road networks due to the topological characteristics of the road networks. In this paper, we propose a new approach to preserve user privacy in LBS over road networks. Additionally, in this paper, we provide new methods for selecting segments and users on the road network to face the attacks. Moreover, to evaluate our work, we compared it with other works such as MCC, BATCH, V-DCA, D-TC, and GCA approaches. After comparing these works, our work offers a good balance between quality of service and privacy.

All-dummy k-anonymous privacy protection algorithm based on location offset

While k-anonymous algorithms can effectively protect users’ private location information, the problem of selecting an appropriate location in the anonymous area to construct the k-anonymous area remains a significant one. When selecting real users from the surrounding area to co-construct anonymous regions, it is easy to cause the leakage of user location information. Moreover, using false addresses to construct a region requires calculating the probability of location queries, which increases the computational complexity. In this paper, an all-dummy k-anonymous algorithm based on location offset is proposed to construct anonymous regions. This algorithm randomly selects k−1 locations and real users in the selected anonymous compose an anonymous group at first. Subsequently, these coordinates are centered on migration, generating multiple dummy addresses of each location migration, such that the dummy address distance is greater than the radius of the user's query, with the dummy address location information used for the location server queries. Through experimental verification, compared with the circle-based dummy address generation algorithm and the random k-anonymous algorithm, the all-dummy k-anonymous algorithm is found to achieve an entropy value and tracking success rate closer to the optimal k-anonymous algorithm without increasing the communication cost.

On the privacy protection of indoor location dataset using anonymization

Indoor positioning is becoming more popular with increasing user demands on Location-based Services (LBS) and Social Networking Services (SNS). Location fingerprinting is widely deployed in indoor localization, and extensive daily use of different kinds of mobile devices in indoor environments results in generating a huge amount of location data of people. However, publishing this location based dataset by Location Service Provider (LSP) to any third party, such as researchers or commercial organizations, potentially threatens users’ privacy. Several studies have been done to anonymize the user location data, but protecting the users’ information against various attacks in an indoor environment is still a challenging issue. This paper proposes a novel framework for publishing privacy preserving location data employing five anonymization techniques: k-anonymity, ℓ-diversity, t-closeness, (α,k)-anonymity, and δ-presence. In the proposed framework, although the LSP cannot find the exact location of the users, it can provide them online location services at the same time and publish the anonymized privacy protected dataset afterward. The practical feasibility of applying the proposed framework is verified on both simulated and two real-world datasets. The results indicate that the published location dataset can protect the identity and location information of users while providing location information for third parties for further purposes.

Secure Transmission Scheme Based on Fingerprint Positioning in Cell-Free Massive MIMO Systems

In this paper, we mainly study how to improve the secure transmission performance of cell-free massive multiple-input multiple-output (MIMO) systems by using location technology. In cell-free massive MIMO systems, active pilot attacks will contaminate the uplink channel estimation and affect the downlink precoding of access points (APs). In order to effectively reduce the impact of active pilot attacks on user transmission, this paper respectively proposes an user location estimation method based on fingerprint positioning and a channel estimation algorithm based on location information. Firstly, under the imperfect channel state information, the location information of users and eavesdropper is obtained by using fingerprint positioning method and K-means clustering algorithm. Then, combined with location information, AP selection strategy and channel estimation method based on non-overlapping angle of arrival (AOA) criterion are proposed respectively. Based on the location information of users and eavesdropper, we use discrete Fourier transform (DFT) to distinguish the uplink channels of legitimate user and eavesdropper from the angle domain, thus eliminating the pilot contamination caused by active pilot attacks. The results show that compared with the traditional transmission method, the proposed secure transmission strategy can increase the secrecy rate of up to 2 b/s/Hz, which effectively enhances the secure transmission performance of cell-free massive MIMO in strong interference environment.

Generating Synthetic Trajectory Data Using GRU

With the rise of mobile network, user location information plays an increasingly important role in various mobile services. The analysis of mobile users’ trajectories can help develop many novel services or applications, such as targeted advertising recommendations, location-based social networks, and intelligent navigation. However, privacy issues limit the sharing of such data. The release of location data resulted in disclosing users’ privacy, such as home addresses, medical records, and other living habits. That promotes the development of trajectory generators, which create synthetic trajectory data by simulating moving objects. At current, there are some disadvantages in the process of generation. The prediction of the following position in the trajectory generation is very dependent on the historical location data, but the relationship between trajectory positions tends to be ignored. Most commonly used methods only adopt the probability distribution of users’ positions to generate synthetic data. On the one hand, this type of statistical method is too rough, and on the other hand, it cannot bring more benefits in availability by increasing data volume. We propose a new trajectory generation method in this paper–Trajectory Generation Model with RNNs(TGMRNN), to address the deficiencies above. It adopts the RNN model to replace the traditional Markov model to generate trajectory data with higher availability. Meanwhile, it solves the problem that RNNs are unsuitable for continuous location data by representing trajectories as discretized data with the grid method. We have conducted experiments in a real data set. Compared with the Markov model, the results of TGMRNN demonstrate that it is superior to some existing methods.

Deep Learning Based Channel Covariance Matrix Estimation With User Location and Scene Images

Channel covariance matrix (CCM) is one critical parameter for designing the communications systems. In this paper, a novel framework of the deep learning (DL) based CCM estimation is proposed that exploits the perception of the transmission environment without any channel sample or the pilot signals. Specifically, as CCM is affected by the user's movement, we design a deep neural network (DNN) to predict CCM from user location and user speed, and the corresponding estimation method is named as ULCCME. A location denoising method is further developed to reduce the positioning error and improve the robustness of ULCCME. For cases when user location information is not available, we propose an interesting way that uses the environmental 3D images to predict the CCM, and the corresponding estimation method is named as SICCME. Simulation results show that both the proposed methods are effective and will benefit the subsequent channel estimation.

Identifying user geolocation with Hierarchical Graph Neural Networks and explainable fusion

Determining user geolocation from social media data is essential in various location-based applications — from improved transportation/supply management, through providing personalized services and targeted marketing, to better overall user experiences. Previous methods rely on the similarity of user posting content and neighboring nodes for user geolocation, which suffer the problems of: (1) position-agnostic of network representation learning, which impedes the performance of their prediction accuracy; and (2) noisy and unstable user relation fusion due to the flat graph embedding methods employed. This work presents Hierarchical Graph Neural Networks (HGNN) – a novel methodology for location-aware collaborative user-aspect data fusion and location prediction. It incorporates geographical location information of users and clustering effect of regions and can capture topological relations while preserving their relative positions. By encoding the structure and features of regions with hierarchical graph learning, HGNN can primarily alleviate the problem of noisy and unstable signal fusion. We further design a relation mechanism to bridge connections between individual users and clusters, which not only leverages the information of isolated nodes that are useless in previous methods but also captures the relations between unlabeled nodes and labeled subgraphs. Furthermore, we introduce a robust statistics method to interpret the behavior of our model by identifying the importance of data samples when predicting the locations of the users. It provides meaningful explanations on the model behaviors and outputs, overcoming the drawbacks of previous approaches that treat user geolocation as “black-box” modeling and lacking interpretability. Comprehensive evaluations on real-world Twitter datasets verify the proposed model’s superior performance and its ability to interpret the user geolocation results.

Proactive UAV Network Slicing for URLLC and Mobile Broadband Service Multiplexing

The unmanned aerial vehicle (UAV) network that is convinced as a significant component of 5G and emerging 6G wireless networks is desired to accommodate multiple types of service requirements simultaneously. However, how to converge different types of services onto a common UAV network without deploying an individual network solution for each type of service is challenging. We tackle this challenge in this paper through slicing the UAV network, i.e., creating logical UAV networks customized for specific requirements. To this end, we formulate the UAV network slicing problem as a sequential decision problem to provide mobile broadband (MBB) services for ground mobile users while satisfying ultra-reliable and low-latency requirements of UAV control and non-payload signal delivery. This problem, however, is difficult to be directly solved mainly due to the sequence-dependent characteristic and the lack of accurate location information of mobile users and accurate and tractable channel gain models in practice. To overcome these difficulties, we propose a novel solution approach based on learning and optimization methods. Particularly, we develop a distributed learning method to predict mobile users’ locations, where partial user location information stored on each UAV is utilized to train user location prediction networks. To achieve accurate channel gain models, we design deep neural networks (DNNs) that are trained by signal measurements at each UAV. To cope with the challenging sequence-dependent characteristic of the problem, we develop a Lyapunov-based optimization framework with provable performance guarantees to decompose the original problem into a sequence of separate optimization subproblems based on the learned results. Finally, an iterative optimization scheme joint with a successive convex approximation technique is exploited to solve these subproblems. Simulation results demonstrate the accuracy of the learning methods as well as the effectiveness of the Lyapunov-based optimization framework.

Triplet-Based Wireless Channel Charting: Architecture and Experiments

Channel charting is a data-driven baseband processing technique consisting in applying self-supervised machine learning techniques to channel state information (CSI), with the objective of reducing the dimension of the data and extracting the fundamental parameters governing its distribution. We introduce a novel channel charting approach based on triplets of samples. The proposed algorithm learns a meaningful similarity metric between CSI samples on the basis of proximity in their respective acquisition times, and simultaneously performs dimensionality reduction. We present an extensive experimental validation of the proposed approach on data obtained from a commercial Massive MIMO system; in particular, we evaluate to which extent the obtained channel chart is similar to the user location information, although it is not supervised by any geographical data. Finally, we propose and evaluate variations in the channel charting process, including the partially supervised case where some labels are available for part of the dataset.

Location-Privacy Leakage and Integrated Solutions for 5G Cellular Networks and Beyond.

The fifth generation (5G) of cellular networks improves the precision of user localization and provides the means to disclose location information to over-the-top (OTT) service providers. The network data analytics function (NWDAF) can further elaborate this information at an aggregated level using artificial intelligence techniques. These powerful features may lead to the improper use of user location information by mobile network operators (MNOs) and OTT service providers. Moreover, vulnerabilities at various layers may also leak user location information to eavesdroppers. Hence, the privacy of users is likely at risk, as location is part of their sensitive data. In this paper, we first go through the evolution of localization in cellular networks and investigate their effects on location privacy. Then, we propose a location-privacy-preserving integrated solution comprising virtual private mobile networks, an independent authentication and billing authority, and functions to protect wireless signals against location information leakage. Moreover, we advocate the continuous and detailed control of localization services by the user.

POI recommendation method using LSTM-attention in LBSN considering privacy protection

Aiming at the problems of traditional point of interest (POI), such as sparse data, lack of negative feedback, and dynamic and periodic changes of user preferences, a POI recommendation method using deep learning in location-based social networks (LBSN) considering privacy protection is proposed. First, the idea of Embedding is used to quantify the user information, friend relationship, POI information, and so on, so as to obtain the internal relationship of the location. Then, based on the user's history and current POI check-in sequence set, the long- and short-term attention mechanism (LSA) is constructed, and the quantified information is used as the input of LSA to better capture the user's long-term and short-term preferences. Finally, the social network information and semantic information are fitted in different input layers, and the time and geographical location information of user's historical behavior are used to recommend the next POI for users. Gowalla and Brightkite datasets are used to demonstrate the proposed method. The results show that the performance of the proposed method is better than other comparison methods under different sparsity, location sequence length, and embedding length. When the number of iterations is 500, the recommended method tends to be stable, and the accuracy is 0.27. Moreover, the recommendation time of the proposed method is less than 130 ms, which is better than other comparative deep learning methods.

Restaurant Recommendation System Using User Based Collaborative Filtering

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

Mobility Prediction Using a Weighted Markov Model Based on Mobile User Classification.

The vast amounts of mobile communication data collected by mobile operators can provide important insights regarding epidemic transmission or traffic patterns. By analyzing historical data and extracting user location information, various methods can be used to predict the mobility of mobile users. However, existing prediction algorithms are mainly based on the historical data of all users at an aggregated level and ignore the heterogeneity of individual behavior patterns. To improve prediction accuracy, this paper proposes a weighted Markov prediction model based on mobile user classification. The trajectory information of a user is extracted first by analyzing real mobile communication data, where the complexity of a user’s trajectory is measured using the mobile trajectory entropy. Second, classification criteria are proposed based on different user behavior patterns, and all users are classified with machine learning algorithms. Finally, according to the characteristics of each user classification, the step threshold and the weighting coefficients of the weighted Markov prediction model are optimized, and mobility prediction is performed for each user classification. Our results show that the optimized weighting coefficients can improve the performance of the weighted Markov prediction model.

BeepTrace: Blockchain-Enabled Privacy-Preserving Contact Tracing for COVID-19 Pandemic and Beyond.

The outbreak of the coronavirus disease 2019 (COVID-19) pandemic has exposed an urgent need for effective contact tracing solutions through mobile phone applications to prevent the infection from spreading further. However, due to the nature of contact tracing, public concern on privacy issues has been a bottleneck to the existing solutions, which is significantly affecting the uptake of contact tracing applications across the globe. In this article, we present a blockchain-enabled privacy-preserving contact tracing scheme: BeepTrace, where we propose to adopt blockchain bridging the user/patient and the authorized solvers to desensitize the user ID and location information. Compared with recently proposed contact tracing solutions, our approach shows higher security and privacy with the additional advantages of being battery friendly and globally accessible. Results show viability in terms of the required resource at both server and mobile phone perspectives. Through breaking the privacy concerns of the public, the proposed BeepTrace solution can provide a timely framework for authorities, companies, software developers, and researchers to fast develop and deploy effective digital contact tracing applications, to conquer the COVID-19 pandemic soon. Meanwhile, the open initiative of BeepTrace allows worldwide collaborations, integrate existing tracing and positioning solutions with the help of blockchain technology.

Research on Information Security Encryption Method Based on Blockchain

With the rapid maturity and development of positioning technology, location-based service (LBS) has become an indispensable part of many industries in social life and national economy, including transportation, social platforms, medical fields, military National defense, etc. With the rapid promotion of wireless network technology and the continuous enrichment of related application fields, LBS has become more complete and convenient, and has a wide range of application prospects. LBS refers to providing location-related services to users through location sharing. In the traditional centralized management mode, problems such as location privacy leakage and location information tampering caused by third parties have brought huge security risks to users. Taking into account the distributed data storage method of the blockchain and the characteristics of openness and autonomy, this paper uses the blockchain to realize the decentralized storage of location information, and on this basis, research and design the location of multi-level privacy protection. The sharing scheme eliminates the security problems caused by third parties and realizes the security of user location information shared.