User Location Prediction Research Articles

LSTM-Oppurs: Opportunistic user recruitment strategy based on deep learning in mobile crowdsensing system

As the scale of Mobile CrowdSensing (MCS) system expands, effective mobile user allocation and recruitment system design becomes crucial. Mobile users can be divided into opportunistic users and participatory ones. Most of the existing recruitment strategy have neglected some aspects, such as without considering the low-paying opportunistic users, without comprehensively considering users’ attributes and without considering their future location, etc. In this paper, the recruitment scheme is investigated by considering the opportunistic users. Firstly, the User Recruitment problem based on User Comprehensive Capabilities (urUCC) is proposed with the objective of maximizing the total revenue. In addition, this problem is proved to be NP-Hard. Secondly, the Opportunistic Users recruitment strategy based on Deep Learning (OUDL) is designed, which consists of three parts, the user location prediction algorithm based on the Long Short-Term Memory (LSTM), the user evaluation algorithm based on the topsis comprehensive evaluation method and the dynamic user recruitment algorithm. Finally, a large number of simulation experiments are conducted by using real datasets. It is proved that the strategy OUDL can recruit high-quality opportunistic users to participate in the sensing task while guaranteeing the task completion rate. Compared with other strategies, the task coverage of the strategy OUDL increased by more than 5% while the comprehensive quality of users increased by about 10%. Thus, the quality of data can be guaranteed while reducing the cost.

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Many applications in intelligent transportation systems are demanding an accurate web application-based location prediction. In this study, we satisfy this demand by designing an automated mobile user location prediction system based on the well-known traditional Auto-Regressive Integrated Moving Average (ARIMA). To increase the proposed model accuracy, make it dynamic, and reduce its execution time, the traditional ARIMA model has been modified extensively by using different combinations of design options of the model. To perform user location prediction, the proposed model depends the previous recorded user locations to predict the user future locations. To make the proposed model dynamic, it is designed to regenerate all its parameters periodically. To deal with such dynamic environment, only a specified window of the historical data is used. To reduce the regeneration of the model execution time, the model selection process is enhanced and several model selection approaches are proposed. The proposed model and the different design options are evaluated using a realistic user location dataset trace that are recorded using a WIFI embedded, as well as, using traces from a previous study called the Kaggle Dataset. To deal with any imperfection in the data used in generating the model in this study. The results show that the proposed framework can generate ARIMA models that can predict the future user locations of a user accurately and with a reasonable execution time. The results also show that the proposed model can predict the user’s location for several future steps with an acceptable accuracy.

LOCATION PREDICTION ON TWITTER USING MACHINE LEARNING TECHNIQUES

These days, location prediction of users using online social media generates a lot of research. For decades, researchers have looked on automatic location recognition in relation to or referenced in documents. As one of the most popular online social networking sites, Twitter has attracted a large number of users who send millions of tweets on a regular basis. Because of the global reach of its users and the constant flow of messages, location prediction on Twitter has gotten a lot of attention lately. Tweets, brief, noisy, and rich-natured communications, provide numerous study hurdles to researchers. A general picture of location prediction using tweets is investigated in the suggested framework. Tweet location, in particular, is anticipated based on tweet content. It is very important to outline tweet content and situations highlighted how the difficulties are dependent on certain text inputs. Here, we apply machine learning techniques like as naive bayes, Support Vector Machines, and Decision Trees to estimate the user's location from tweet content.

HFUL: a hybrid framework for user account linkage across location-aware social networks

Sources of complementary information are connected when we link user accounts belonging to the same user across different platforms or devices. The expanded information promotes the development of a wide range of applications, such as cross-platform prediction, cross-platform recommendation, and advertisement. Due to the significance of user account linkage and the widespread popularization of GPS-enabled mobile devices, there are increasing research studies on linking user account with spatio-temporal data across location-aware social networks. Being different from most existing studies in this domain that only focus on the effectiveness, we propose a novel framework entitled HFUL (A Hybrid Framework for User Account Linkage across Location-Aware Social Networks), where efficiency, effectiveness, scalability, robustness, and application of user account linkage are considered. Specifically, to improve the efficiency, we develop a comprehensive index structure from the spatio-temporal perspective, and design novel pruning strategies to reduce the search space. To improve the effectiveness, a kernel density estimation-based method has been proposed to alleviate the data sparsity problem in measuring users’ similarities. Additionally, we investigate the application of HFUL in terms of user prediction, time prediction, and location prediction. The extensive experiments conducted on three real-world datasets demonstrate the superiority of HFUL in terms of effectiveness, efficiency, scalability, robustness, and application compared with the state-of-the-art methods.

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.

Pre-training Context and Time Aware Location Embeddings from Spatial-Temporal Trajectories for User Next Location Prediction

Pre-training location embeddings from spatial-temporal trajectories is a fundamental procedure and very beneficial for user next location prediction. In the real world, a location usually has variable functionalities under different contextual environments. If the exact functions of a location in the trajectory can be reflected in its embedding, the accuracy of user next location prediction should be improved. Yet, existing location embeddings pre-trained on trajectories are mostly based on distributed word representations, which mix a location's various functionalities into one latent representation vector. To address this problem, we propose a Context and Time aware Location Embedding (CTLE) model, which calculates a location's representation vector with consideration of its specific contextual neighbors in trajectories. In this way, the multi-functional properties of locations can be properly tackled. Furthermore, in order to incorporate temporal information in trajectories into location embeddings, we propose a subtle temporal encoding module and a novel pre-training objective, which further improve the quality of location embeddings. We evaluate our proposed model on two real-world mobile user trajectory datasets. The experimental results demonstrate that, compared with the existing embedding methods, our CTLE model can pre-train higher quality location embeddings and significantly improve the performance of downstream user location prediction models.

A Markov Chain Position Prediction Model Based on Multidimensional Correction

User location prediction in location-based social networks can predict the density of people flow well in terms of intelligent transportation, which can make corresponding adjustments in time to make traffic smooth, reduce fuel consumption, reduce greenhouse gas emissions, and help build a green cycle low-carbon transportation green system. This paper proposes a Markov chain position prediction model based on multidimensional correction (MDC-MCM). Firstly, extract corresponding information from the user’s historical check-in position sequence as a position-position conversion map. Secondly, the influence of check-in period, space distance, and other factors on the position prediction is linearly weighted and merged with the position prediction of the n-order Markov chain to construct MDC-MCM. Finally, we conduct a comprehensive performance evaluation of MDC-MCM using the dataset collected from Brightkite. Experimental results show that compared with other advanced location prediction technologies, MDC-MCM achieves better location prediction results.

Future Location Prediction for Emergency Vehicles Using Big Data: A Case Study of Healthcare Engineering.

The number of devices equipped with GPS sensors has increased enormously, which generates a massive amount of data. To analyse this huge data for various applications is still challenging. One such application is to predict the future location of an ambulance in the healthcare system based on its previous locations. For example, many smart city applications rely on user movement and location prediction like SnapTrends and Geofeedia. There are many models and algorithms which help predict the future location with high probabilities. However, in terms of efficiency and accuracy, the existing algorithms are still improving. In this study, a novel algorithm, NextSTMove, is proposed according to the available dataset which results in lower latency and higher probability. Apache Spark, a big data platform, was used for reducing the processing time and efficiently managing computing resources. The algorithm achieved 75% to 85% accuracy and in some cases 100% accuracy, where the users do not change their daily routine frequently. After comparing the prediction results of our algorithm, it was experimentally found that it predicts processes up to 300% faster than traditional algorithms. NextSTMove is therefore compared with and without Apache Spark and can help in finding useful knowledge for healthcare medical information systems and other data analytics related solutions especially healthcare engineering.

Deep Learning-Based Edge Caching in Fog Radio Access Networks

In this article, the edge caching policy in fog radio access networks (F-RANs) is optimized via deep learning. Considering that it is hard for fog access points (F-APs) to collect sufficient data of massive content features, our proposed edge caching policy only utilizes the number of requests and user location. In an offline phase, we propose to learn the corresponding popularity prediction model for every content popularity trend class and user location prediction models to make the popularity prediction accurate, adaptive and targeted. Moreover, we develop a loss function to avoid overfitting and increase sensitivity to high popularity for popularity prediction models. In an online phase, we propose a reactive caching scheme to react to user requests. In order to guarantee that classification can improve the popularity prediction accuracy in both phases, deep learning and k-Nearest Neighbor (kNN) are combined to classify popularity trends. Besides, a joint proactive-reactive caching policy is proposed to maximize the cache hit rate. The proposed policy is able to promptly track the various popularity trends with spatial-temporal popularity, trend and user dynamics with a low computational complexity. Extensive performance evaluation results show that the cache hit rate of our proposed policy approaches that of the optimal policy.

K-Medoids Clustering Based Next Location Prediction in Wireless Local Area Network

User mobility prediction in wireless network is being investigated from various angles to improve performance of the network. Access to user’s movement information such as time, direction, speed etc provides an opportunity for wireless networks to effectively manage resources to satisfy user needs. A next location prediction technique is required for transferring the existing connections of user to the next Access Point (AP) beforehand to ensure better Quality of Service (QoS) of the network. There are several techniques for next location prediction of mobile users in Wireless Local Area Network (WLAN) which include Indoor Next Location Prediction with Wi-Fi model, Extended Mobility Markov Chain Model, Hidden Markov Model and Mixed Membership Stochastic Blockmodel. In the Indoor Next Location Prediction with Wi-Fi model the area of prediction is fixed and small which makes this approach inefficient when the number of locations traversed by the mobile user is large. The paper addresses the issue of predicting the next location of mobile users in a Wireless Local Area Network (WLAN) when the area of prediction is vast. In this paper an intelligent clustering technique i.e., the K-Medoids clustering algorithm has been implemented on the indoor next location prediction, which is based on a Markov-chain model, for predicting the next location of a user when the number of locations traversed by the user is vast. The accuracy of prediction of mobile user’s next location by the proposed K-Medoids clustering based next location prediction technique ranges from 67% to 97%.

User Location Prediction Based on Parallel Pattern Mining and Path Matching

User Location Prediction Based on Parallel Pattern Mining and Path Matching

Survey on user location prediction based on geo-social networking data

With the popularity of smart mobile terminals and advances in wireless communication and positioning technologies, Geo-Social Networks (GSNs), which combine location awareness and social service functions, have become increasingly prevalent. The increasing amount of user and location information in GSNs makes the information overload phenomenon more and more serious. Although massive user-generated data brings convenience to users’ social and travel activities, it also causes certain trouble for their daily life. In this context, users are expecting smarter mobile applications, so that the location information can be employed to perceive their surrounding environment intelligently and further mine their behavior patterns in GSNs, which ultimately provides personalized location-based services for users. Therefore, research on user location prediction comes into existence and has received extensive and in-depth attention from researchers. Through systematically analyzing the location data carried by user check-ins and comments, user location prediction can mine various user behavior patterns and personal preferences, thus determining the visiting location of users in the future. Research on user location prediction is still in the ascendant and it has become an important topic of common concern in both academia and industry. This survey takes Geo-social networking data as the focal point to elaborate the recent progress in user location prediction from multiple aspects such as problem categories, data sources, feature extraction, mathematical models and evaluation metrics. Besides, the difficulties to be studied and the future developmental trends of user location prediction are discussed.

Prediction Future Location for Multiple account users Across Social Network

The personal data of people over social networks bring many opportunities for developing a new application. Some users create multiple accounts on social networks. Correlate user information across social networks helps to design algorithms to predict the future location of multiple accounts of a single individual. Most social media site applications need to share their sites with friends and family and this feature has made many people need to predict the site. Prediction is one of the foremost essential issues that need to be investigated for mobility management In mobile computing systems. In this paper, a proposed algorithm for future location prediction of multiple account users based on using frequent pattern mining. First, apply the frequency algorithm. The proposed algorithm experimented using "Gowalla and foursquare datasets" were presented simple method can be used to infer the user location using publicly available attributes and also the geographic information associated with allocatable friends. We find that it is possible to infer the user city with high accuracy. The proposed algorithm prediction achieved accuracy 96% in the Gowalla dataset when applying the support and confidence for each region that visited the user in a specific time period through some years or many months.

Exploiting Meta-Path with Attention Mechanism for Fine-Grained User Location Prediction in LBSNs

Based on the huge volumes of user check-in data in LBSNs, users’ intrinsic mobility patterns can be well explored, which is fundamental for predicting where a user will visit next given his/her historical check-in records. As there are various types of nodes and interactions in LBSNs, they can be treated as Heterogeneous Information Network (HIN) where multiple semantic meta-paths can be extracted. Inspired by the recent success of meta-path context based embedding techniques in HIN, in this paper, we design a deep neural network framework leveraging various meta-path contexts for fine-grained user location prediction. Experimental results based on two real-world LBSN datasets demonstrate the best effectiveness of the proposed approach using various evaluation metrics than others.

Venue2Vec: An Efficient Embedding Model for Fine-Grained User Location Prediction in Geo-Social Networks

Geo-Social Networks (GSN) significantly improve location-aware capability of services by offering geo-located content based on the huge volumes of data generated in the GSN. The problem of user location prediction based on user-generated data in GSN has been extensively studied. However, existing studies are either concerning predicting users’ next check-in location or predicting their future check-in location at a given time with coarse granularity. A unified model that can predict both scenarios with fine granularity is quite rare. Also, due to the heterogeneity of multiple factors associated with both locations and users, how to efficiently incorporate these information still remains challenging. Inspired by the recent success of word embedding in natural language processing, in this paper, we propose a novel embedding model called Venue2Vec which automatically incorporates temporal-spatial context, semantic information, and sequential relations for fine-grained user location prediction. Locations of the same type, and those that are geographically close or often visited successively by users will be situated closer within the embedding space. Based on our proposed Venue2Vec model, we design techniques that allow for predicting a user’s next check-in location, and also their future check-in location at a given time. We conduct experiments on three real-world GSN datasets to verify the performance of the proposed model. Experimental results on both tasks show that Venue2Vec model outperforms several state-of-the-art models on various evaluation metrics. Furthermore, we show how the Venue2Vec model can be more time-efficient due to being parallelizable.

A Novel Multi-Layer Classification Ensemble Approach for Location Prediction of Social Users

Information-disclosure by social-users has increased enormously. Using this information for accurate location-prediction is challenging. Thus, a novel Multi-Layer Ensemble Classification scheme is proposed. It works on un-weighted/weighted majority voting, using novel weight-assignment function. Base learners are selected based on their individual performances for training the model. Main motive is to develop an efficient approach for check-ins-based location-classification of social-users. The proposed model is implemented on Foursquare datasets where a classification accuracy of 94% is achieved, which is higher than other state-of-the-art techniques. Apart from tracking locations of social-users, proposed framework can be useful for detecting malicious users present in various expert and intelligent-system.

Social ties and checkin sites: connections and latent structures in location-based social networks

Location-based social networks integrate location-based facilities with social connectivity for delivering a variety of services, enhancing user experience, emergency/disaster management, and streamlining business processes. A number of recent research efforts have studied relationships between geolocation and social connectivity, social connectivity and preferences, and node attributes and strength of social ties. These efforts have successfully demonstrated prediction of various attributes based on social connectivity, mobility, dynamic checkin information, etc., including prediction of user location as well as future checkin locations. In this paper, we study the relationship between shared checkin locations and the structure and nature of social ties. We argue that typical LSBNs are in fact composed of layers of networks of varying structure and function and that it is possible to deconcolve these networks through effective statistical analysis of shared checkins. In this context, we pose and validate the following hypotheses: (1) A large number of shared checkins imply social connectivity; however, social connectivity does not imply statistically large number of shared checkins; (2) entities in social ties that share a large number of checkins tend to be strongly clustered. We hypothesize that such strong ties (e.g., family ties and friendships) carry higher influence compared to weaker ties (mere acquaintances) in the social network; (3) social ties that have statistically fewer shared checkins (weak ties) tend to be less clustered than the underlying (baseline) network. We hypothesize that such ties (e.g., professional ties, friends of friends, and acquaintances) carry less influence; and (4) social ties that have statistically large number of shared checkins (strong ties) tend to be relatively more dynamic when compared to weak ties over a period of time. We present statistical models and validate our hypotheses on real datasets. Our conclusions can significantly enhance flow of information and influence in the network by suitably leveraging the distinct relationships captured in the deconvolved networks.

User location prediction with energy efficiency model in the Long Term‐Evolution network

SummaryPredicting users' next location/place allows us to anticipate their future movement. It provides additional time to be ready for that movement and react consequently. Furthermore, many industries, including Internet Service Providers, are still requiring low cost and simple location/place prediction methods that can be implemented on mobile device. This paper studies domain‐independent prediction algorithms and spatio‐temporal based prediction method using 20‐day‐long records in Long Term‐Evolution(LTE) network, which captures the mobility patterns of 3474 individuals. After examining the prediction accuracy and resource consumption of domain‐independent prediction algorithms, we find Markov provides the best tradeoff. Furthermore, Active LeZi outperforms Markov if enough consecutive parsed patterns of users' history movement are captured. In addition, we further group users according to their spatio‐temporal entropy profiles in order to predict not only user's future locations but also the place he or she most likely to appear within a specific period. By applying the simple spatio‐temporal based method to each group of user, 83.3% accuracy can be achieved for some users. Yet Markov and Active LeZi algorithms perform better for some other users. This implies that we should consider applying different prediction methods to users with distinct spatio‐temporal characteristics. Copyright © 2015 John Wiley & Sons, Ltd.

Agent-based Movement Analysis and Location Prediction in Cellular Networks

The huge quantity of individual location data recorded by mobile phones or mobile networks inspires research questions regarding new and intelligible services on behalf of mobile users. Creating a system for location prediction and following user habits is a sizeable and challenging task. We present a system for movement analysis and location prediction of individual mobile users. The goal of movement analysis is to determine which locations are significant to the user and to classify locations according to user-specific regularities. The analysis is performed by multi-agent system in which individual agent performs a chosen set of functions. The prediction system consists of two predictors which work in parallel, based on the type of user-specific locations. Prediction system is intended to estimate the future location of a user device and provide it to user-authorized applications and services. The proposed system is evaluated using real movement data recorded as part of the MIT Reality Mining Project.

Extreme learning machine for user location prediction in mobile environment

PurposePrediction accuracies are usually affected by the techniques and devices used as well as the algorithms applied. This work aims to attempt to further devise a better positioning accuracy based on location fingerprinting taking advantage of two important mobile fingerprints, namely signal strength (SS) and signal quality (SQ) and subsequently building a model based on extreme learning machine (ELM), a new learning algorithm for single‐hidden‐layer neural networks.Design/methodology/approachPrediction approach to location determination based on historical data has attracted a lot of attention in recent studies, the reason being that it offers the convenience of using previously accumulated location data to subsequently determine locations using predictive algorithms. There have been various approaches to location positioning to further improve mobile user location determination accuracy. This work examines the location determination techniques by attempting to determine the location of mobile users by taking advantage of SS and SQ history data and modeling the locations using the ELM algorithm. The empirical results show that the proposed model based on the ELM algorithm noticeably outperforms k‐Nearest Neighbor approaches.FindingsWiFi's SS contributes more in accuracy to the prediction of user location than WiFi's SQ. Moreover, the new framework based on ELM has been compared with the k‐Nearest Neighbor and the results have shown that the proposed model based on the extreme learning algorithm outperforms the k‐Nearest Neighbor approach.Originality/valueA new computational intelligence modeling scheme, based on the ELM has been investigated, developed and implemented, as an efficient and more accurate predictive solution for determining position of mobile users based on location fingerprint data (SS and SQ).