Given the continuous technological advances in computing and communication, it seems that we are rapidly heading towards the realization of paradigms commonly described as ubiquitous computing, pervasive computing, ambient intelligence, or, more recently, "everyware". These paradigms envision living environments pervaded by a high number of invisible technological devices affecting and improving all aspects of our lives. Therefore, it is easy to justify the need of knowing the physical location of users. Outdoor location-aware applications are already widespread today, their growing popularity showing that location-awareness is indeed a very useful functionality. Less obvious is how the growing availability of these locations and tracks will be exploited for providing more intelligent "situation-understanding" services that help people. My work is motivated by the fact that, thanks to location-awareness systems, we are more and more aware of the exact positions of the users but unfortunately we are rarely capable of exactly understanding what they are doing. Location awareness should rapidly evolve and become "situation-awareness" otherwise the ubiquitous-computing vision will become impracticable. The goal of this thesis is devising alternative and innovative approaches to the problem of indoor position estimation/assessment and evaluating them in real environments. These approaches are be based on: (i) a low-cost and energy-aware localization infrastructure; (ii) multi-sensor, statistically-based, localization algorithms; (iii) logic-based situation assessment techniques. The algorithms and techniques that are the outcome of this thesis have all been tested by implementing them and measuring (both in a quantitative sense and in a qualitative sense) the performance in the field.

(2010). Tracking with high-density, large-scale wireless sensor networks. (Tesi di dottorato, Università degli Studi di Milano-Bicocca, 2010).

Tracking with high-density, large-scale wireless sensor networks

MERICO, DAVIDE
2010-02-03

Abstract

Given the continuous technological advances in computing and communication, it seems that we are rapidly heading towards the realization of paradigms commonly described as ubiquitous computing, pervasive computing, ambient intelligence, or, more recently, "everyware". These paradigms envision living environments pervaded by a high number of invisible technological devices affecting and improving all aspects of our lives. Therefore, it is easy to justify the need of knowing the physical location of users. Outdoor location-aware applications are already widespread today, their growing popularity showing that location-awareness is indeed a very useful functionality. Less obvious is how the growing availability of these locations and tracks will be exploited for providing more intelligent "situation-understanding" services that help people. My work is motivated by the fact that, thanks to location-awareness systems, we are more and more aware of the exact positions of the users but unfortunately we are rarely capable of exactly understanding what they are doing. Location awareness should rapidly evolve and become "situation-awareness" otherwise the ubiquitous-computing vision will become impracticable. The goal of this thesis is devising alternative and innovative approaches to the problem of indoor position estimation/assessment and evaluating them in real environments. These approaches are be based on: (i) a low-cost and energy-aware localization infrastructure; (ii) multi-sensor, statistically-based, localization algorithms; (iii) logic-based situation assessment techniques. The algorithms and techniques that are the outcome of this thesis have all been tested by implementing them and measuring (both in a quantitative sense and in a qualitative sense) the performance in the field.
BISIANI, ROBERTO
SCHETTINI, RAIMONDO
Wireless Sensor Networks, Ranging, Localization, Tracking, Data Gathering, Bayesian Filters, Particle Filters, Indoor, Reasoning, Agent-based data simulation, Situation Awareness, Situation Understanding
INF/01 - INFORMATICA
English
Scuola di dottorato di Scienze
INFORMATICA - 22R
22
2008/2009
(2010). Tracking with high-density, large-scale wireless sensor networks. (Tesi di dottorato, Università degli Studi di Milano-Bicocca, 2010).
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/7785
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