Crowdsourced Reconstruction of Cellular Networks to Serve Outdoor Positioning: Modeling, Validation and Analysis

Positioning via outdoor fingerprinting, which exploits the radio signals emitted by cellular towers, is fundamental in many applications. In most cases, the localization performance is affected by the availability of information about the emitters, such as their coverage. While several projects aim at collecting cellular network data via crowdsourcing observations, none focuses on information about the structure of the networks, which is paramount to correctly model their topology. The difficulty of such a modeling is exacerbated by the inherent differences among cellular technologies, the strong spatio-temporal nature of positioning, and the continuously evolving configuration of the networks. In this paper, we first show how to synthesize a detailed conceptual schema of cellular networks on the basis of the signal fingerprints collected by devices. We turned it into a logical one, and we exploited that to build a relational spatio-temporal database capable of supporting a crowdsourced collection of data. Next, we populated the database with heterogeneous cellular observations originating from multiple sources. In addition, we illustrate how the developed system allows us to properly deal with the evolution of the network configuration, e.g., by detecting cell renaming phenomena and by making it possible to correct inconsistent measurements coming from mobile devices, fostering positioning tasks. Finally, we provide a wide range of basic, spatial, and temporal analyses about the arrangement of the cellular network and its evolution over time, demonstrating how the developed system can be used to reconstruct and maintain a deep knowledge of the cellular network, possibly starting from crowdsourced information only

From Trajectory Modeling to Social Habits and Behaviors Analysis

In recent years, the widespread of mobile devices has made easier and popular the activities of recording locations visited by users and of inferring their trajectories. The availability of such large amount of spatio-temporal data opens new challenges to automatically extract information and get valuable knowledge. The many aspects of this issue have aroused the interest of researchers in several areas, such as information retrieval, data mining, context-aware computing, security and privacy issues, urban planning, and transport management. Recently, there has been a strong interest in understanding how people move during their common daily activities in order to get information about their behaviors and habits. In this paper we describe considerable recent research works related to the analysis of mobile spatio-temporal data, focusing on the study of social habits and behaviors. We provide a general perspective on studies on human mobility by depicting and comparing methods and algorithms, highlighting some critical issues related to information extraction from spatio-temporal data, and future research directions

A relational encoding of a conceptual model with multiple temporal dimensions

The theoretical interest and the practical relevance of a systematic treatment of multiple temporal dimensions is widely recognized in the database and information system communities. Nevertheless, most relational databases have no temporal support at all. A few of them provide a limited support, in terms of temporal data types and predicates, constructors, and functions for the management of time values (borrowed from the SQL standard). One (resp., two) temporal dimensions are supported by historical and transaction-time (resp., bitemporal) databases only. In this paper, we provide a relational encoding of a conceptual model featuring four temporal dimensions, namely, the classical valid and transaction times, plus the event and availability times. We focus our attention on the distinctive technical features of the proposed temporal extension of the relation model. In the last part of the paper, we briefly show how to implement it in a standard DBMS

Modeling and Validating Spatio-Temporal Conceptual Schemas in XML Schema

In this paper, we describe a translation algorithm that maps spatio-temporal conceptual schemas into XML schemas expressed in the W3C XML Schema Language. Moreover, we extend the standard XML Schema validator with a Java library to check spatio-temporal constraints. The resulting framework allows one to validate XML documents containing spatio-temporal information with respect to spatio-temporal conceptual schemas