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Tecniche di Data Management per Applicazioni Active RFID
Full text linkNegli ultimi anni, la tecnologia RFID sta guadagnando una certa popolarità grazie alla
sua capacità di rilevare oggetti e persone dotati di piccoli tag in un ambiente
attrezzato di antenne e lettori RFID. Le applicazioni RFID spesso si basano sull'omonima tecnologia
per gestire eventi di alto livello, come il tracciamento della posizione di prodotti in una suply-chain, il monitoraggio della posizione e lo stato dei pazienti in ambiente ospedaliero, servizi di localizzazione di intrusi, e così via. Una
relazione fondamentale per questi scopi è la posizione di persone e oggetti nel
tempo. Tuttavia, il flusso di dati RFID è per natura rumoroso, ridondante e inaffidabile
e quindi questo flusso di basso livello deve essere trasformato in una sequenza temporale di istanze di posizione. Inoltre, le applicazioni RFID di solito producono enormi quantità di dati che possono raggiungere in casi pratici la dimensione di un gigabyte in un giorno.
Questa tesi presenta la progettazione, realizzazione e valutazione sperimentale di un sistema in tempo reale che risolve i problemi di gestione dei dati RFID
suddetti.
Il sistema è dotato di un modulo per l'acquisizione dati e la gestione dell'incertezza basato
su modelli probabilistici che è in grado di trasformare il flusso di dati grezzi provenienti da tag RFID in informazioni probabilistiche, che risultano significative per le applicazioni o dalle quali è possibile estrarre informazioni di interesse.
Inoltre, per gestire i grandi volumi di dati generati da applicazioni RFID,
questa tesi propone un semplice meccanismo on-line che è in grado di riassumere quantità massicce di dati in streaming pur
preservando la significatività delle informazioni trasmesse.
Infine in questa tesi abbiamo anche progettato e realizzato un programma per la localizzazione di intrusi. Si tratta della prima proposta di utilizzo congiunto di telecamere
e RFID in tempo reale su vaste aree aperte, rumorose e complesse.
A questo scopo nella tesi viene proposta una nuova architettura e vengono sviluppati algoritmi specifici che sono stati testati su casi reali.In the last several years, RFID technology has gained significant popularity due to
its ability of detecting objects and people carrying small RFID tags in an environment
equipped with RFID readers. RFID applications usually rely on RFID deployments
to manage high-level events such as tracking the location that products
visit for supply-chain management, monitoring the location and status of patients
in hospital environment, localizing intruders for alerting services, and so on. A
fundamental relation for these purposes is the location of people and objects over
time. However, the nature of RFID data stream is noisy, redundant and unreliable
and thus streams of low-level tag-reads must be transformed into meaningful
relation instances. Nevertheless, the management of RFID data in transforming
low-level streams in to high-level events poses a number of challenges. Moreover,
RFID deployments usually produce huge volumes of data that can reach
in practical cases to the size of gigabytes in a day.
This thesis presents the design, implementation and experimental evaluation of realtime
system that addresses the above mentioned data management issues related to RFID tracking
systems.
In this thesis, a Data Acquisition and Uncertainty Management Module based
on probabilistic models and techniques is developed that operate on unreliable
and imprecise real-time data stream in order to transform them into probabilistic data streams that can be meaningful to the applications or by which we can extract information of interest.
Moreover, to handle the huge data volumes generated by RFID deployments,
this thesis proposes a simple on-line summarization mechanism by developing a
Data Aggregation and Query Processing Module, which is able to provide small
space representation for massive RFID probabilistic real-time data streams while
preserving the meaningfulness of the information streams convey.
In this thesis, we have also designed and implemented an application for intruder localization. This is the first proposal of joint use of cameras and RFIDs in real noisy and complex wide open areas.
We propose a new architecture and developed specific algorithms that have been finally tested on real-cases
Online filtering and uncertainty management techniques for rfid data processing
Full text linkRFID is one of the emerging technologies for a wide-range of applications, including supply chain and asset management, healthcare and intruder localization. However, the nature of an RFID data stream is noisy, redundant and unreliable, making it unsuitable for direct use in applications. In this paper, we propose specific RFID Online Filtering and Uncertainty Management techniques that operate on unreliable and imprecise data streams in order to transform them into reliable probabilistic data that can be meaningful to the applications. Our proposal makes use of an Hidden Markov Model (HMM) that continuously infers hidden variables (locations, in case of above example) based on sensor readings. The resulting data can be directly stored in a probabilistic database table for further analysis. All the techniques presented in this paper are implemented in a complete framework and succesfully evaluated in real-world object tracking scenarios
Effective Aggregation and Querying of Probabilistic RFID Data in a Location Tracking Context
No full textRFID applications usually rely on RFID deployments to manage high-level events such as tracking the location that products visit for supply-chain management, localizing intruders for alerting services, and so on. However, transforming low-level streams into high-level events poses a number of challenges. In this paper, we deal with the well known issues of data redundancy and data-information mismatch: we propose an on-line summarization mechanism that is able to provide small space representation for massive RFID probabilistic data streams while preserving the meaningfulness of the information. We also show that common information needs, i.e. detecting complex events meaningful to applications, can be effectively answered by executing temporal probabilistic SQL queries directly on the summarized data. All the techniques presented in this paper are implemented in a complete framework and successfully evaluated in real-world location tracking scenarios
Data management techniques for active RFID applications
No full textIn the last several years, RFID technology has gained significant popularity due to its ability of de- tecting objects and people carrying small RFID tags in an environment equipped with RFID readers. This research involved the design, implementation and experimental evaluation of a realtime system that ad- dresses the above mentioned data management issues in the context of RFID location tracking systems
Toward a Flexible Data Management Middleware for Wireless Sensor Networks
No full textIn this paper we present the research activity we are carrying out in the "Mobile Semantic Self-Organizing Wireless Sensor Networks" Project at the Department of Information Engineering of the University of Modena and Reggio Emilia. In this context, the main aim of our research is to study solutions for the flexible querying of distributed data collected by heterogeneous devices providing measurement readings. To this end, we propose a middleware for wireless sensor networks which is able to autonomously configure the communication and the operations required to each device in order to reduce energy and temporal costs
Identification of Intruders in Groups of People using Cameras and RFIDs
No full textThe identification of intruders in groups of people moving in wide open areas represents a challenging scenario where coordination between cameras can be certainly used but this solution is not enough. In this paper, we propose to go beyond pure vision-based approaches by integrating the use of distributed cameras with the RFID technology. To this end, we introduce a system that “maps” RFID tags to people detected by cameras by using sophisticated techniques to filter the singular modalities and an evidential fusion architecture, based on Transferable Belief Model, to combine the two sources of information and manage conflict between them. The conducted experimental evaluation shows very promising results, especially in treating groups of people
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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