Development Of Location-based Services Research Articles

Learning Universal Trajectory Representation via a Siamese Geography-Aware Transformer

With the development of location-based services and data collection equipment, the volume of trajectory data has been growing at a phenomenal rate. Raw trajectory data come in the form of sequences of “coordinate-time-attribute” triplets, which require complicated manual processing before they can be used in data mining algorithms. Current works have started to explore the emerging deep representation learning method, which maps trajectory sequences to vector space and applies them to various downstream applications for boosting accuracy and efficiency. In this work, we propose a universal trajectory representation learning method based on a Siamese geography-aware transformer (TRT for short). Specifically, we first propose a geography-aware encoder to model geographical information of trajectory points. Then, we apply a transformer encoder to embed trajectory sequences and use a Siamese network to facilitate representation learning. Furthermore, a joint training strategy is designed for TRT. One of the training objectives is to predict the masked trajectory point, which makes the trajectory representation robust to low sampling rates and noises. The other is to distinguish the difference between trajectories by means of contrastive learning, which makes the trajectory representation more uniformly distributed over the hypersphere. Last, we design a benchmark containing four typical traffic-related tasks to evaluate the performance of TRT. Comprehensive experiments demonstrate that TRT consistently outperforms the state-of-the-art baselines across all tasks.

Research on Pedestrian Indoor Positioning Based on Two-Step Robust Adaptive Cubature Kalman Filter with Smartphone MEMS Sensors.

With the development of location-based service (LBS), indoor positioning based on pedestrian dead reckoning (PDR) has become a hot research topic. Smartphones are becoming more popular for indoor positioning. This paper proposes a two-step robust-adaptive-cubature Kalman filter (RACKF) algorithm based on smartphone micro-electro-mechanical-system (MEMS) sensor fusion for indoor positioning. To estimate pedestrian heading, a quaternion-based robust-adaptive-cubature Kalman filter algorithm is proposed. Firstly, the model noise parameters are adaptively corrected based on the fading-memory-weighting method and the limited-memory-weighting method. The memory window of the limited-memory-weighting algorithm is modified based on the characteristics of pedestrian walking. Secondly, an adaptive factor is constructed based on the partial state inconsistency to overcome filtering-model deviation and abnormal disturbances. Finally, to identify and control the measurement outliers, the robust factor based on maximum-likelihood estimation is introduced into the filtering to enhance the robustness of heading estimation and support more robust dynamic-position estimation. In addition, based on the accelerometer information, a nonlinear model is constructed and the empirical model is used to estimate the step length. Combining heading and step length, the two-step robust-adaptive-cubature Kalman filter is proposed to improve the pedestrian-dead-reckoning method, which enhances the adaptability and robustness of the algorithm and further improves the accuracy of the plane-position solution. The adaptive factor based on the prediction residual and the robust factor based on the maximum-likelihood estimation are introduced into the filter to improve the adaptability and robustness of the filter, reduce the positioning error, and improve the accuracy of the pedestrian-dead-reckoning method. Three different smartphones are used to validate the proposed algorithm in an indoor environment. Additionally, the experimental results confirm the algorithm's effectiveness. From the results of the three smartphones, the root mean square error (RMSE) of the indoor-positioning results obtained by the proposed method is about 1.3-1.7 m.

BASISMAP: sequence-based similarity search for geomagnetic positioning

Indoor localization has become a popular topic with the development of location-based services (LBS) and indoor navigation systems. Beside these circumstances indoor positioning has been the focus of attention for researchers as the most important component of these applications. Many signals are used as distinguishable features for indoor positioning. RF-based Wi-Fi and BLE systems are the most popular ones and these have been preferred because of their high distinguishable feature. The use of geomagnetism, a natural signal found all over the world, has also been of interest to many researchers. Geomagnetic signals being distorted in the indoor area due to the effect of the structure by using that information takes opportunity to determine the relevant location. In this study, a new method is proposed to convert these unknown signals into location data using a magnetic fingerprint database. The sequential data collected using a dynamic comparison buffer in motion is evaluated with the help of the similarity search method called matrix profile, and position is obtained. The study was compared with other methods in the literature and its prominent and weak points were shared. The performance of the study was evaluated using site-survey by collecting data in an office environment. It has been concluded that the cumulative error is below 2.2 m in the normal operating phase of the system on a 100-m-long path. Compared to the literature, a low complexity and efficient solution is proposed. Furthermore, matrix-profile-based path matching method was used for the first time in magnetic sequence-based localization.

A Blockchain-Based Location Privacy-Preserving Scheme in Location-Based Service

With the rapid popularization and development of location-based services (LBS), location privacy preserving has become a mainstream concern. Existing anonymity-based methods rely mainly on anonymous location selection based on geographic information, which neglects the semantic information on the location, and behavior of self-interested users may raise a privacy vulnerability. A blockchain-based approach for location privacy protection schemes has been proposed to address these issues. In this scheme, the privacy level of the location is improved using private chain-based collaborative anonymous communication with anonymous set construction methods that consider the semantic diversity associated with the related user. Compared with other existing methods, evaluation based on real-world datasets shows that the proposed method prevents location privacy leakage from collaborative users and constructs a semantical anonymity set, thus effectively protecting the user’s location privacy.

Context-Matching Method as a Transformation Paradigm Between Position and Location Domains in Location-Based Services

A two-domain approach involving position and location has been introduced to provide a formal description needed for optimal organization of information processing for utilization in Location-Based Services development and operation. This paper proposes the transformation that connects the two domains (position and location), outlines its formal description, and validates the concept using the Context-Matching method as a paradigm.

Continuous Road Network-Based Skyline Query for Moving Objects

With the development of location-based services and smart terminals, skyline query technique has been used widely in intelligent transportation systems. In skyline queries, the areas and keywords queried by users have a great impact on the quality of the query results and users may only be interested in the closer results of the skyline query. However, current approaches for the continuous skyline query limit the area of the skyline query to a specific area in the road network, which leads to that many useful query results cannot be retrieved. To this end, an innovative continuous skyline query approach in city range is proposed in this paper, where a multi-scale area divisions of the urban road network are provided to find the optimize query scale and area. In our approach, first, the dominant area of each intersection node in the road network is established based on the Voronoi. Then, all Points of Interest (POIs) are divided into the dominant area of each intersection node. After that, the intersection node aggregation algorithm (INAA), link remolding algorithm (LMA) and link fitting algorithm (LFA) are proposed to reduce the number of intersection nodes in the road network, so as to increase the dominant area of the remaining intersection nodes and the number of POIs in these nodes. Finally, a better query scale by considering the efficiency and quality of the query is given through the studies.

Multi-view group representation learning for location-aware group recommendation

With the development of location-based services (LBS), many location-based social sites like Foursquare and Plancast have emerged. People can organize and participate in group activities on those sites. Therefore, recommending venues for group activities is of practical value. However, the group decision making process is complicated, requiring trade-offs among group members. And the data sparsity and cold-start problems make it difficult to make effective group recommendation. In this manuscript, we propose a Multi-view Group Representation Learning (MGPL) framework for location-aware group recommendation. The proposed multi-view group representation learning framework can leverage multiple types of information for deep representation learning of group preferences and incorporate the spatial attributes of locations to further capture the group mobility preferences. Experiments on two real datasets Foursqaure and Plancast show that our method significantly outperforms the-state-of-art approaches.

Indoor Positioning System in Learning Approach Experiments

The positioning system research strongly supports the development of location-based services used by related business organizations. However, location-based services with user experience still have many obstacles to overcome, including how to maintain a high level of position accuracy. From the literature studies reviewed, it is necessary to develop an indoor positioning system using fingerprinting based on Received Signal Strength (RSS). So far, the testing of the indoor positioning system has been carried out with an algorithm. But, in this research, with the proposed parameters, we will conduct experiments with a learning approach. The data tested is the signal service data on the device in the Dinamika Bangsa University building. The test was conducted with a deep learning approach using a deep neural network (DNN) algorithm. The DNN method can estimate the actual space and get better position results, whereas machine learning methods such as the DNN algorithm can handle more effectively large data and produce more accurate data. From the results of comparative testing with the learning approach between DNN, KNN, and SVM, it can be concluded that the evaluation with KNN is slightly better than the use of DNN in a single case. However, the results of KNN have low consistency; this is seen from the fluctuations in the movements of the R2 score and MSE values produced. Meanwhile, DNN gives a consistent value even though it has varied hidden layers. The Support Vector Machine (SVM) gives the worst value of these experiments, although, in the past, SVM was known as one of the favorite methods.

Survey on vehicle map matching techniques

With the development of location-based services and Big data technology, vehicle map matching techniques are growing rapidly, which is the fundamental techniques in the study of exploring global positioning system (GPS) data. The pre-processed GPS data can provide the guarantee of high-quality data for the research of mining passenger's points of interest and urban computing services. The existing surveys mainly focus on map-matching algorithms, but there are few descriptions on the key phases of the acquisition of sampling data, floating car and road data preprocessing in vehicle map matching systems. To address these limitations, the contribution of this survey on map matching techniques lies in the following aspects: (i) the background knowledge, function and system framework of vehicle map matching techniques; (ii) description of floating car data and road network structure to understand the detailed phase of map matching; (iii) data preprocessing rules, specific methodologies, and significance of floating car and road data; (iv) map matching algorithms are classified by the sampling frequency and data information. The authors give the introduction of open-source GPS sampling data sets, and the evaluation measurements of map-matching approaches; (v) the suggestions on data preprocessing and map matching algorithms in the future work.

Privacy Preserving Spatio-Temporal Databases Based on k-Anonymity

The development of location-based services and mobile devices has lead to an increase in the location data. Through the data mining process, some valuable information can be discovered from location data. In the other words, an attacker may also extract some private (sensitive) information of the user and this may make threats against the user privacy. Therefore, location privacy protection becomes an important requirement to the success in the development of location-based services. In this paper, we propose a grid-based approach as well as an algorithm to guarantee k-anonymity, a well-known privacy protection approach, in a location database. The proposed approach considers only the information that has significance for the data mining process while ignoring the un-related information. The experiment results show the effectiveness of the proposed approach in comparison with the literature ones.

ON THE CHALLENGES OF MOBILITY PREDICTION IN SMART CITIES

Abstract. The mass of data generated from people’s mobility in smart cities is constantly increasing, thus making a new business for large companies. These data are often used for mobility prediction in order to improve services or even systems such as the development of location-based services, personalized recommendation systems, and mobile communication systems. In this paper, we identify the mobility prediction issues and challenges serving as guideline for researchers and developers in mobility prediction. To this end, we first identify the key concepts and classifications related to mobility prediction. We then, focus on challenges in mobility prediction from a deep literature study. These classifications and challenges are for serving further understanding, development and enhancement of the mobility prediction vision.

User location privacy protection mechanism for location-based services

With the rapid development of the Internet of Things (IoT), Location-Based Services (LBS) are becoming more and more popular. However, for the users being served, how to protect their location privacy has become a growing concern. This has led to great difficulty in establishing trust between the users and the service providers, hindering the development of LBS for more comprehensive functions. In this paper, we first establish a strong identity verification mechanism to ensure the authentication security of the system and then design a new location privacy protection mechanism based on the privacy proximity test problem. This mechanism not only guarantees the confidentiality of the user’s information during the subsequent information interaction and dynamic data transmission, but also meets the service provider’s requirements for related data.

Survey on vehicle map matching techniques

With the development of location-based services and Big data technology, vehicle map matching techniques are growing rapidly, which is the fundamental techniques in the study of exploring global positioning system (GPS) data. The pre-processed GPS data can provide the guarantee of high-quality data for the research of mining passenger's points of interest and urban computing services. The existing surveys mainly focus on map-matching algorithms, but there are few descriptions on the key phases of the acquisition of sampling data, floating car and road data preprocessing in vehicle map matching systems. To address these limitations, the contribution of this survey on map matching techniques lies in the following aspects: (i) the background knowledge, function and system framework of vehicle map matching techniques; (ii) description of floating car data and road network structure to understand the detailed phase of map matching; (iii) data preprocessing rules, specific methodologies, and significance of floating car and road data; (iv) map matching algorithms are classified by the sampling frequency and data information. The authors give the introduction of open-source GPS sampling data sets, and the evaluation measurements of map-matching approaches; (v) the suggestions on data preprocessing and map matching algorithms in the future work.

A Lightweight Privacy-Preserving Fair Meeting Location Determination Scheme

Equipped with mobile devices, people relied on location-based services (LBSs) can expediently and reasonably organize their activities. But location information may disclose people’s sensitive information, such as interests and health status. Besides, the limited resources of mobile devices restrict the further development of LBSs. In this article, aiming at the fair meeting position determination service, we design a lightweight privacy-preserving solution. In our scheme, mobile users only need to submit service requests. A cloud server and a location services provider are responsible for service response, where the cloud server achieves most of the calculation, and the location services provider determines the fair meeting location based on the computational results of the cloud server and broadcasts it to mobile users. The proposed scheme adopts homomorphic encryptions and random permutation methods to preserve the location privacy of mobile users. The security analyses show that the proposed scheme is privacy preserving under our defined threat models. Besides, the presented solution only needs to calculate $n$ Euclidean distances, and hence, our scheme has linear computation and communication complexity.

Markov and improved particle swarm optimization-based privacy preservation algorithm for user space geographical location

ABSTRACT With the development of location-based service, the problem of personal privacy information security is becoming more and more serious. Users’ personal privacy information is used without authorization, personal privacy information security is facing various challenges, and personal property security is likely to be violated. Based on HMM model, this paper analyses the location and the location transition probability, and then predicts the location probability by AFMO algorithm. Privacy location information can be predicted by AFMO algorithm, which can improve the accuracy of location prediction. Finally, the experimental results show that the HMM-AFMO algorithm is superior to the HMM model in terms of execution time and effect. In the position prediction, the similarity advantage is obvious, which fully reflects the advantages of the algorithm proposed in this paper.

Location Based Services: Research Trends and Open Challenges

Abstract. We are now living in a mobile information era, which is fundamentally changing science and society. Location Based Services (LBS), which deliver information depending on the location of the (mobile) device and user, play a key role in this mobile information era.This presentation will review the state-of-the-art in LBS research, and identify several research trends in recent years. These range from mobile guides and navigation systems to more diverse applications, from outdoor to indoor and mixed outdoor/indoor environments, from location based to context-aware, from maps and audio to more diverse and ‘natural’ interfaces, from technology-oriented to interdisciplinary research, and analysis of big spatial data.To motivate further LBS research and stimulate collective efforts, this presentation will present a series of key research challenges that are essential to advance the development of LBS, setting a research agenda for LBS to ‘positively’ shape the future of our mobile information society. These research challenges (Figure 1) cover issues related to the core of LBS development (e.g. positioning, modelling, and communication), evaluation, and analysis of LBS-generated data, as well as social, ethical, and behavioural issues that rise as LBS enter into people’s daily lives. This research agenda is an outcome of the multi-phase collaborative initiative (https://lbs.icaci.org/research-agenda/), started in May 2016.We hope this research agenda helps to motivate further LBS research and stimulate collective efforts. To bring together the broader community to answer these open research questions, a series of activities (conferences, workshops, and special issues) will be organized in the coming years. By working collaboratively within the LBS community and across cognate communities, we can ensure a better future of our mobile information society.This presentation is based on the following publication: Haosheng Huang, Georg Gartner, Jukka M. Krisp, Martin Raubal & Nico Van de Weghe (2018): Location based services: ongoing evolution and research agenda, Journal of Location Based Services, DOI: 10.1080/17489725.2018.1508763.

Spatiotemporal analysis of trajectory for a new real-time bus routes updated model

With the development of location-based service, the wide range of applications of trajectory data obtained by GPS has made great contributions to the analysis of urban buses information. Affected by the increasing quantity of data, filtering out valid GPS trajectory data to update real-time information of urban buses faces new difficulties. In this study, a prototype model named Real-Time Bus Routes Updated model (RT-BRU) is developed to process and mine the GPS trajectory data collected from each urban bus of each interval. The main purpose of this model is to update real-time information of urban bus routes and provide much more realistic public navigation data with the help of trajectory data and geo-statistical tools. Compared to the regular algorithms and Chinese public navigation map data, the experiment results show that the RT-BRU model proposed here can discover more types of urban bus’s updated routes and play better in perspective of GPS points counts, operation time, calculation results, correct results and accuracy.

Exploiting location-related behaviors without the GPS data on smartphones

The concerns about location privacy has received considerable attention with the development of Location-based Services (LBSs) over the recent years. Most smartphone users ignore the fact that Apps can infer their locations through accessing WiFi list, although they carefully set location-related permissions to preserve their privacy. Therefore, it is crucial to the public to investigate severe such consequence of WiFi list leakage. In this paper, we develop a tracking scheme for Android, called TrackU, which validate that it is possible to obtain user’s location data as well as their location-related behaviors, just by monitoring the WiFi list without any GPS data. Firstly, it periodically scans available Access Points (APs) nearby and queries the geo-location of the device from LBS providers. Secondly, a drift adjusting algorithm proposed obtains the exact locations considering a set of factors, such as historical location information, average signal strength and changing of WiFi list. To preserve the battery life, an optimization is made to dynamically adjust the positioning interval. Based on the obtained data, we design an activity detection algorithm precisely to infer users’ daily activities, and identify their travel modes, i.e., hovering, walking, and vehicles. Finally, we implement TrackU and carry out a series of experiments with 39 volunteers from seven cities of China. The experiment results show that our design can detect 91.6% of activities by monitoring the WiFi list, and accurately recognize 94.6% of user’s travel mode.

An Efficient Approach for LBS Privacy Preservation in Mobile Social Networks

With the rapid development of smart handheld devices, wireless communication, and positioning technologies, location-based service (LBS) has been gaining tremendous popularity in mobile social networks (MSN). Users’ daily lives are facilitated by the applications of LBS, but users’ privacy leaking hinders the further development of LBS. In order to solve this problem, techniques such as k-anonymity and l-diversity have been widely adopted. However, most papers that combine with k-anonymity and l-diversity focus on the security of users’ privacy with little consideration of service efficiency. In this paper, we firstly treat the relationship between k-anonymity and l-diversity in the clustering process from a dynamic and global perspective. Then a service category table based algorithm (SCTB) is designed to identify and calculate l-diversity securely and efficiently, which promotes the cooperative efficiency of users in LBS query, especially when the preference privacy that users request in the clustering process have similarities. Finally, theoretical performance analysis and extensive experimental studies are performed to validate the effectiveness of our SCTB algorithm.

MPTR: A Maximal-Marginal-Relevance-Based Personalized Trip Recommendation Method

Personalized trip recommendation has drawn much attention recently with the development of location-based services. How to utilize the data in the location-based social network to recommend a single Point of Interest (POI) or a sequence of POIs for users is an important question to answer. Recommending the latter is called trip recommendation that is a challenging study because of the diversity of trips and complexity of involved computation. This work proposes a maximal-marginal-relevance-based personalized trip recommendation method that considers both relevance and diversity of trips in trip planning. An ant-colony-optimization-based trip planning algorithm is developed to efficiently plan a trip. Finally, case studies and experiments illustrate the effectiveness of our method.