Types Of Location-based Services Research Articles

On-the-Fly Privacy for Location Histograms

An important motivation for research in location privacy has been to protect against user profiling, i.e., inferring a user’s political affiliation, wealth level, sexual preferences, religious beliefs, and other sensitive attributes. Existing approaches focus on distorting or suppressing individual locations, but we argue that, for directly protecting against profiling, it is more appropriate to focus on the frequency with which various locations are visited – in other words, the histogram of a user’s locations. We introduce and explore a new privacy notion, namely, on-the-fly privacy for location histograms, in which a mobile user repeatedly submits obfuscated locations to a Location-Based Service aiming for the resulting histogram to resemble a target profile or differ from it. For example, she may want to avoid looking wealthy or to resemble a health-conscious person. We describe how to design concrete privacy mechanisms that operate under different assumptions on, e.g., the user’s mobility, including provably optimal mechanisms. We use a mobility dataset with 1083 users to illustrate how these mechanisms achieve privacy while minimizing the quality loss caused by the location obfuscation, in the context of two types of Location-Based Services: nearest-PoI, and geofence.

A Framework for Estimating Gaze Point Information for Location-Based Services

In this study, a novel framework for estimating a user's gaze point is proposed. If it is possible to detect what a user is looking at, appropriate services can be provided accordingly. Most existing methods for gaze estimation using image processing are classified into two types: those using the third-person viewpoint and those using the first-person viewpoint. However, the former approach lacks accurate estimation and the latter approach can cause privacy issues. In the proposed framework, sensor information from acceleration and gyro sensors installed in mobile devices is utilized instead of the first-person camera. From the images obtained from the third-person camera, a heatmap showing the possibility of objects that the user is looking at is estimated using machine learning techniques. This information is combined with the position of the user's head, which is obtained from the sensor information, to estimate the location of the user's gaze point. Experimental results show that the proposed method achieves a much higher accuracy than existing techniques. Obtaining user gaze information is very helpful in providing advanced location-based services (LBSs). The proposed framework can increase the added value of various types of LBSs.

SparkNN: A Distributed In-Memory Data Partitioning for KNN Queries on Big Spatial Data

The increase in GPS-enabled devices and proliferation of location-based applications have resulted in an abundance of geotagged (spatial) data. As a consequence, numerous applications have emerged that utilize the spatial data to provide different types of location-based services. However, the huge amount of available spatial data presents a challenge to the efficiency of these location-based services. Although the advent of big data frameworks like Apache Spark has enabled the processing of large amounts of data efficiently, they are designed for general (non-spatial) data. That is due to the build-in data partitioning mechanism that does not take into account the spatial proximity of the data. Therefore, these big data frameworks cannot be readily used for spatial analytics such as efficiently answering spatial queries. To fill this gap, this paper proposes SparkNN, an in-memory partitioning and indexing system for answering spatial queries, such as K-nearest neighbor, on big spatial data. SparkNN is implemented on top of Apache Spark and consists of three layers to facilitate efficient spatial queries. The first layer is a spatial-aware partitioning layer, which partitions the spatial data into several partitions ensuring that the load of the partitions is balanced and data objects with close proximity are placed in the same, or neighboring, partitions. The second layer is a local indexing layer, which provides a spatial index inside each partition to speed up the data search within the partition. The third layer is a global index, which is placed in the master node of Spark to route spatial queries to the relevant partitions. The efficiency of SparkNN was evaluated by extensive experiments with big spatial datasets. The results show SparkNN significantly outperforms the state-of-the-art Spark system when evaluated on the same set of queries.

An RSS-based regression model for user equipment location in cellular networks using machine learning

Dissemination of wireless networks and mobile devices, such as smartphones, has motivated the appearance of several types of location-based services. Consequently, the interest in low-complexity cost-effective mobile positioning techniques against the traditional method based on global positioning system has emerged. An interesting alternative is to utilize radio frequency (RF) signals to estimate the location of mobile terminals, as in multilateration and RF fingerprinting techniques. In this context, the objective of this work is to propose a new radio strength signal-based user equipment location method using a machine learning regression-based model to find directly the geographical coordinates of the mobile user in cellular networks. Numerical results show that, in most cases, the proposed method can meet the location accuracy requirements established by the Federal Communications Commission for network-based localization methods.

Privacy Preservation in Location-Based Services

Based on spatial and temporal big data, location- based services (LBSs) obtain considerable attention due to their potential to make cities smarter. However, LBS provides the functionality by extracting personal data from users, which may incur privacy leakage risk, such as intrusion, theft, and unauthorized sale of sensitive information. Hence, the mass market share of LBS depends on how well the privacy of citizens can be protected. In this article, we specifically study the privacy issues in LBSs. In detail, we start with classifying different types of LBSs and analyzing the privacy leakage presented in each type. After summarizing the state-of-the-art work on location privacy and query privacy, we propose a query content preservation approach with the aim of providing accurate LBS answer with zero server knowledge on query content. Finally, we present some open problems that may foster future research on LBS privacy preservation.

A hybrid method for user location anonymisation based on road mobility model

Highly accurate positioning devices provide various types of location-based services (LBS). Since location information may reveal private information, preserving location privacy has become a significant issue in LBS. Many different techniques for securing location privacy have been proposed, for example, the concepts of silent period, dummy node, and cloaking region. However, much of this research has not solved the problem that the quality of service (QoS) of LBS decreases when anonymity is improved, and anonymity falls when QoS is improved. In this paper, we present a node density-based location anonymisation scheme that can provide location privacy by a hybrid method using the dummy node and the cloaking region. Especially, we propose a method to keep the anonymity without reducing the QoS of the LBS by setting the cloaking region based on the road mobility model. Simulations are conducted to evaluate our proposed method from the viewpoint of location error in LBS and the probability of tracking a target node. Simulation results show that the probability of tracking a target node by an adversary is reduced and the QoS of LBS is improved.

Trajectory privacy in location-based services and data publication

The ubiquity of mobile devices with global positioning functionality (e.g., GPS and AGPS) and Internet connectivity (e.g., 3G andWi-Fi) has resulted in widespread development of location-based services (LBS). Typical examples of LBS include local business search, e-marketing, social networking, and automotive traffic monitoring. Although LBS provide valuable services for mobile users, revealing their private locations to potentially untrusted LBS service providers pose privacy concerns. In general, there are two types of LBS, namely, snapshot and continuous LBS. For snapshot LBS, a mobile user only needs to report its current location to a service provider once to get its desired information. On the other hand, a mobile user has to report its location to a service provider in a periodic or on-demand manner to obtain its desired continuous LBS. Protecting user location privacy for continuous LBS is more challenging than snapshot LBS because adversaries may use the spatial and temporal correlations in the user's location samples to infer the user's location information with higher certainty. Such user location trajectories are also very important for many applications, e.g., business analysis, city planning, and intelligent transportation. However, publishing such location trajectories to the public or a third party for data analysis could pose serious privacy concerns. Privacy protection in continuous LBS and trajectory data publication has increasingly drawn attention from the research community and industry. In this survey, we give an overview of the state-of-the-art privacy-preserving techniques in these two problems.

RoadTrack

Mobile commerce and location based services (LBS) are some of the fastest growing IT industries in the last five years. Location update of mobile clients is a fundamental capability in mobile commerce and all types of LBS. Higher update frequency leads to higher accuracy, but incurs unacceptably high cost of location management at the location servers. We propose RoadTrack -- a road-network based, query-aware location update framework with two unique features. First, we introduce the concept of precincts to control the granularity of location update resolution for mobile clients that are not of interest to any active location query services. Second, we define query encounter points for mobile objects that are targets of active location query services, and utilize these encounter points to define the adequate location update schedule for each mobile. The RoadTrack framework offers three unique advantages. First, encounter points as a fundamental query awareness mechanism enable us to control and differentiate location update strategies for mobile clients in the vicinity of active location queries, while meeting the needs of location query evaluation. Second, we employ system-defined precincts to manage the desired spatial resolution of location updates for different mobile clients and to control the scope of query awareness to be capitalized by a location update strategy. Third, our road-network based check-free interval optimization further enhances the effectiveness of the Road-Track query-aware location update scheduling algorithm. This optimization provides significant cost reduction for location update management at both mobile clients and location servers. We evaluate the RoadTrack location update approach using a real world road-network based mobility simulator. Our experimental results demonstrate that the RoadTrack query aware location update approach outperforms existing representative location update strategies in terms of both client energy efficiency and server processing load.

Location-based service use: a metaverse investigation

The current trend in location-based services (LBSs) research is to focus on the development and deployment of LBSs. This research examines the different uses of LBSs based on a virtual environment. It is argued that using the real world method of developing and deploying LBS restricts the types of uses being developed and that an alternative approach might yield insight into what types of uses are possible and how it might change the thinking of LBSs developers. This research surveyed the Second Life metaverse to identify and categorise the types of LBS used and deployed in the digital world. The results of the survey found that many of the LBSs in SL provided intelligence to the avatar so that they could perform an action which they could not do without the LBS. It is clear that the topic of navigation is biased based on the digital nature of the SL metaverse and its use of the teleportation concept. But the results show that LBSs are ubiquitous throughout metaverse and the types of services go beyond those found in the real world.

A Border Line-Based Pruning Scheme for Shortest Path Computations

With the progress of IT and mobile positioning technologies, various types of location-based services (LBS) have been proposed and implemented. Finding a shortest path between two nodes is one of the most fundamental tasks in many LBS related applications. So far, there have been many research efforts on the shortest path finding problem. For instance, A * algorithm estimates neighboring nodes using a heuristic function and selects minimum cost node as the closest one to the destination. Pruning method, which is known to outperform the A * algorithm, improves its routing performance by avoiding unnecessary exploration in the search space. For pruning, shortest paths for all node pairs in a map need to be pre-computed, from which a shortest path container is generated for each edge. The container for an edge consists of all the destination nodes whose shortest path passes through the edge and possibly some unnecessary nodes. These containers are used during routing to prune unnecessary node visits. However, this method shows poor performance as the number of unnecessary nodes included in the container increases. In this paper, we focus on this problem and propose a new border line-based pruning scheme for path routing which can reduce the number of unnecessary node visits significantly. Through extensive experiments on randomly-generated, various complexity of maps, we empirically find out optimal number of border lines for clipping containers and compare its performance with other methods.

이동 객체 데이터베이스에서 빈발 시퀀스 패턴 탐색

위치 기반 장치의 발전과, GIS 기능의 확장 그리고 위치 정보기술들의 정확성과 가용성이 증가함에 따라서 위치 기반 서비스들의 새로운 영역에 대한 새로운 가능성이 나타나게 되었다. 데이터의 시간과 공간 형태에 따라서 정의되는 Relationship에 기인하여 시공간 데이터 마이닝 영역에서 공간에 대한 지식 검색이 증가할 경우 매우 큰 문제에 직면한다. 이 논문에서는 모바일 환경에서 시공간 패턴 마이닝을 위한 알고리즘들을 제안한다. 이동 패턴들은 All_MOP와 Max_MOP 두 개의 알고리즘을 활용하여 생성된다. 이 알고리즘들은 먼저 모든 빈발 패턴들을 탐사한 후 오직 최대의 빈발 패턴만을 탐사한다. 아울러, 제안한 기법과 기존의 DFS_MINE 기법의 수행 시간 비교를 통하여 제안한 기법이 수행시간에서 다소 우수한 것을 나타낸다. 이러한 제안접근법은 관광 서비스, 교통 서비스 등과 같은 위치 기반 서비스 등에 활용할 수 있다. The converge of location-aware devices, GIS functionalities and the increasing accuracy and availability of positioning technologies pave the way to a range of new types of location-based services. The field of spatiotemporal data mining where relationships are defined by spatial and temporal aspect of data is encountering big challenges since the increased search space of knowledge. Therefore, we aim to propose algorithms for mining spatiotemporal patterns in mobile environment in this paper. Moving patterns are generated utilizing two algorithms called All_MOP and Max_MOP. The first one mines all frequent patterns and the other discovers only maximal frequent patterns. Our proposed approach is able to reduce consuming time through comparison with DFS_MINE algorithm. In addition, our approach is applicable to location-based services such as tourist service, traffic service, and so on.