Indexing of moving objects for location-based services

2003 ◽  
Author(s):  
S. Saltenis ◽  
C.S. Jensen
Keyword(s):  
Moving Objects ◽  
Location Based Services

scholarly journals Extracting Stops from Spatio-Temporal Trajectories within Dynamic Contextual Features

2021 ◽  
Vol 13 (2) ◽  
pp. 690
Author(s):  
Tao Wu ◽  
Huiqing Shen ◽  
Jianxin Qin ◽  
Longgang Xiang
Keyword(s):  
Moving Objects ◽  
Major Effect ◽  
Location Based Services ◽  
Trajectory Data ◽  
The Road ◽  
Contextual Features ◽  
Novel Approach ◽  
Gps Trajectories ◽  
Time Distance ◽  
Spatio Temporal

Identifying stops from GPS trajectories is one of the main concerns in the study of moving objects and has a major effect on a wide variety of location-based services and applications. Although the spatial and non-spatial characteristics of trajectories have been widely investigated for the identification of stops, few studies have concentrated on the impacts of the contextual features, which are also connected to the road network and nearby Points of Interest (POIs). In order to obtain more precise stop information from moving objects, this paper proposes and implements a novel approach that represents a spatio-temproal dynamics relationship between stopping behaviors and geospatial elements to detect stops. The relationship between the candidate stops based on the standard time–distance threshold approach and the surrounding environmental elements are integrated in a complex way (the mobility context cube) to extract stop features and precisely derive stops using the classifier classification. The methodology presented is designed to reduce the error rate of detection of stops in the work of trajectory data mining. It turns out that 26 features can contribute to recognizing stop behaviors from trajectory data. Additionally, experiments on a real-world trajectory dataset further demonstrate the effectiveness of the proposed approach in improving the accuracy of identifying stops from trajectories.

Database Queries in Mobile Environments

2011 ◽  
pp. 267-284
Author(s):  
N. Marsit
Keyword(s):  
Scientific Community ◽  
Moving Objects ◽  
Research Work ◽  
Location Based Services ◽  
Technological Evolution ◽  
Mobile Terminals ◽  
Database Queries ◽  
Positioning Systems ◽  
Work Related ◽  
Comprehensive Survey

The technological evolution of networks together with the development of positioning systems has contributed to the emergence of numerous location-based services. Services related to this expanding area will become of major technical as well as economical interest in the coming few years. This aroused a great deal of interest from the scientific community at large and specifically from those studying these services and their diverse requirements and constraints. One of the direct consequences in the database field is the appearance of new types of queries (mobile queries issued from mobile terminals and/or requesting information associated with moving objects such as vehicles). Our objective in this chapter is to present a comprehensive survey of the field of research work related to mobile queries, with particular attention to the location issue.

scholarly journals Applications of Moving Objects Databases

2011 ◽  
pp. 186-203 ◽  
Author(s):  
Ouri Wolfson ◽  
Eduardo Mena
Keyword(s):  
Traffic Control ◽  
Moving Objects ◽  
Weather Forecasting ◽  
Location Management ◽  
Resource Discovery ◽  
Location Based Services ◽  
Sensor Technology ◽  
Moving Objects Databases ◽  
New Applications ◽  
Target Readership

Miniaturization of computing devices and advances in wireless communication and sensor technology are some of the forces propagating computing from the stationary desktop to the mobile outdoors. Some important classes of new applications that will be enabled by this revolutionary development include location-based services, tourist services, mobile electronic commerce and digital battlefield. Some existing application classes that will benefit from the development include transportation and air traffic control, weather forecasting, emergency response, mobile resource management and mobile workforce. Location management, that is, the management of transient location information, is an enabling technology for all these applications. Location management is also a fundamental component of other technologies, such as fly-through visualization, context awareness, augmented reality, cellular communication and dynamic resource discovery. Moving Objects Databases (MODs) store and manage the location as well as other dynamic information about moving objects. In this chapter we will present the applications of MODs and their functionality. The target readership is researchers and engineers working in databases and mobile computing.

scholarly journals Privacy preserving semantic trajectory data publishing for mobile location-based services

2019 ◽  
Vol 26 (8) ◽  
pp. 5551-5560 ◽  
Author(s):  
Rong Tan ◽  
Yuan Tao ◽  
Wen Si ◽  
Yuan-Yuan Zhang
Keyword(s):  
Moving Objects ◽  
Information Source ◽  
Location Based Services ◽  
Data Publishing ◽  
Trajectory Data ◽  
Similarity Comparison ◽  
Mobile Location ◽  
Motion Modes ◽  
Network Topologies ◽  
Rich Information

Abstract The development of wireless technologies and the popularity of mobile devices is responsible for generating large amounts of trajectory data for moving objects. Trajectory datasets have spatiotemporal features and are a rich information source. The mining of trajectory data can reveal interesting patterns of human activities and behaviors. However, trajectory data can also be exploited to disclose users’ privacy information, e.g., the places they live and work, which could be abused by a malicious user. Therefore, it is very important to protect the users’ privacy before publishing any trajectory data. While most previous research on this subject has only considered the privacy protection of stay points, this paper distinguishes itself by modeling and processing semantic trajectories, which not only contain spatiotemporal data but also involve POI information and the users’ motion modes such as walking, running, driving, etc. Accordingly, in this research, semantic trajectory anonymizing based on the k-anonymity model is proposed that can form sensitive areas that contain k − 1 POI points that are similar to the sensitive points. Then, trajectory ambiguity is executed based on the motion modes, road network topologies and road weights in the sensitive area. Finally, a similarity comparison is performed to obtain the recordable and releasable anonymity trajectory sets. Experimental results show that this method performs efficiently and provides high privacy levels.

scholarly journals Improving Room-Level Location for Indoor Trajectory Tracking with Low IPS Accuracy

2021 ◽  
Vol 10 (9) ◽  
pp. 620
Author(s):  
Taehoon Kim ◽  
Kyoung-Sook Kim ◽  
Ki-Joune Li
Keyword(s):  
Moving Objects ◽  
Three Dimensional ◽  
Synthetic Data ◽  
Geographic Location ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Ultra Wideband ◽  
Map Matching ◽  
Unit Space ◽  
Indoor Spaces

With the development of indoor positioning methods, such as Wi-Fi positioning, geomagnetic sensor positioning, Ultra-Wideband positioning, and pedestrian dead reckoning, the area of location-based services (LBS) is expanding from outdoor to indoor spaces. LBS refers to the geographic location information of moving objects to provide the desired services. Most Wi-Fi-based indoor positioning methods provide two-dimensional (2D) or three-dimensional (3D) coordinates in 1–5 m of accuracy on average approximately. However, many applications of indoor LBS are targeted to specific spaces such as rooms, corridors, stairs, etc. Thus, they require determining a service space from a coordinate in indoor spaces. In this paper, we propose a map matching method to assign an indoor position to a unit space a subdivision of an indoor space, called USMM (Unit Space Map Matching). Map matching is a commonly used localization improvement method that utilizes spatial constraints. We consider the topological information between unit spaces and moving objects’ probabilistic properties, compared to existing room-level mappings based on sensor signals, especially received signal strength-based fingerprinting. The proposed method has the advantage of calculating the probability even if there is only one input trajectory. Last, we analyze the accuracy and performance of the proposed USMM methods by extensive experiments in real and synthetic environments. The experimental results show that our methods bring a significant improvement when the accuracy level of indoor positioning is low. In experiments, the room-level location accuracy improves by almost 30% and 23% with real and synthetic data, respectively. We conclude that USMM methods are helpful to correct valid room-level locations from given positioning locations.

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