Rong Tan

SOCOM: Multi-sensor Oriented Context Model Based on Ontologies

Sensor plays an important role in context-aware computing. While sensor modeling is usually isolated from former researches on context modeling and sensor type is always restricted to physical ones, this paper aims to provide a more comprehensive insight into the relationships between sensor and context. Based on a more generic definition of sensor and a corresponding sensor category in context-aware computing, main characteristics of sensor and the relations between sensor and context are analyzed in details. In particular, a multi-sensor oriented context model based on ontology approach is proposed. The model which has been applied to a middleware supporting for rapid context-aware applications development is proved to be effective.

Link Prediction Using Protected Location History

This paper investigates the link prediction problem in location-based social networking services (LBSNS) with protected location history. While former approaches mainly utilize the accurate locations, the relevant data we analyzed are modeled by a location privacy protection model called k-anonymous spatial-temporal cloaking model (KSTCM) which perturbs the location-related records on both temporal and spatial dimensions. We also propose a KSTCM-based co-located relationship model based on which various contextual features are extracted to help building the prediction model. Furthermore, we study the extent to which link between two users can be inferred from the co-located situation in space, time and degree of privacy protection. The experimental results show that our link prediction model can obtain a very high accuracy.

Metadata management of context resources in context-aware middleware system

Metadata is the data about data. With the development of information technologies, it is realized that metadata is of great importance in discovery, identification, localization and access of resources. For the context-aware middleware, it is necessary to provide the metadata of context resources connected to it for the context consumers. However, the complexity of context-aware computing results in the diversity of context resources which makes it difficult in explicitly describing the metadata. In this paper, an XML-based markup language called VirtualSensorML is proposed for the purpose of providing model and schema to describe the metadata of context resources in an understandable format. It defines fundamental elements and various sub elements to describe the common and special properties of different context resources. The metadata management of context resources is currently implemented in a multi-agent based context-aware middleware. The evaluation demonstrates that it is efficient to improve the discovery of context resources.

Region of Interest Discovery in Location-Based Social Networking Services with Protected Locations

Region of Interest (ROI) discovery is one of the most common interests in Location-based social networking services (LBSNS). While former researches mainly utilize the accurate location history, this paper explores the methods to extract those regions with protected locations. A spatial-temporal cloaking check-in model following k-anonymity principle is introduced. And methods to extract two kinds of ROIs, popular regions and personal regions, are proposed respectively. Experimental results illustrate that by analyzing the characteristics of those protected locations, ROIs are able to be discovered as well. Furthermore, our work shows that privacy protection and personalized services can be both achieved in LBSNS.

A Hybrid Spatial-Temporal Index for location based service

Nowadays, numerous applications involving spatial data are emerging. While efficient management of tremendous spatial-temporal objects is a challenge due to the highly dynamic nature of the data, the demand for fast getting query results and minimum update cost. However, the query performance optimizations and the update cost minimizations cannot be achieved at the same time. In this paper, a novel approach is presented to get a tradeoff between those two. A spatial-temporal index mechanism named as Hybrid Spatial-Temporal Index (HSTI), which satisfies the time slice, time interval, event, and trajectory queries is introduced. The effectiveness and efficiency of the proposed index is validated through extensive experiments.