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Research Activity - Riccardo Masiero @ DEI |
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In this page it is possible to find
and downloading matherial related to my academical career. Please, note that the downloading of my work is for free, for personal use only and provided prior read of the IEEE copyright statement in this page.
If you wish to contact me, my e-mail address is |
| Home | |||||
| Curriculum Vitae | The link aside left is to download my Curriculum Vitae in pdf | |||||||||||||||||||||||||||||||
| MS's Thesis | The link aside left is to download my MS's Thesis in pdf | |||||||||||||||||||||||||||||||
| MS's Thesis Presentation | The link aside left is to download my MS's Thesis presentation in ppt | |||||||||||||||||||||||||||||||
| PhD Thesis | The link aside left is to download my PhD Thesis in pdf | |||||||||||||||||||||||||||||||
| PhD Thesis Presentation | The link aside left is to download my PhD Thesis presentation in pdf | |||||||||||||||||||||||||||||||
| OCEANS 2012 (Spring) | The link aside left is to download my paper presented at the MTS/IEEE OCEANS 2012 conference (Spring)
DESERT Underwater: an NS-Miracle-based framework to DEsign, Simulate, Emulate and Realize Test-beds for Underwater network protocols
Authors: Riccardo Masiero, Saiful Azad, Federico Favaro, Matteo Petrani, Giovanni Toso, Federico Guerra, Paolo Casari, Michele Zorzi
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| OCEANS 2011 (Fall) | The link aside left is to download my paper presented at the MTS/IEEE OCEANS 2011 conference (Fall)
The NAUTILUS project: Physical Parameters,Architectures and Network Scenarios
Authors: Riccardo Masiero, Paolo Casari, Michele Zorzi
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| INFOCOM 2011 | The link aside left is to download my paper presented at the 2011 INFOCOM Mini-Conference
Distributed Subgradient Methods for Dealy Tolerant Networks
Authors: Riccardo Masiero, Giovanni Neglia
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| SenseApp 2010 | The link aside left is to download my paper presented at the 2010 SenseApp workshop
WSN-Control: Signal Reconstruction through
Compressive Sensing in Wireless Sensor Networks
Authors: Giorgio Quer, Davide Zordan, Riccardo Masiero, Michele Zorzi, Michele Rossi
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| GLOBECOM 2009 | The link aside left is to download my paper presented at the 2009 GLOBECOM conference
Data Acquisition through joint Compressive Sensing
and Principal Component Analysis
Authors: Riccardo Masiero, Giorgio Quer, Daniele Munaretto, Michele Rossi, Joerg Widmer, Michele Zorzi
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| SASN 2009 | The link aside left is to download my paper presented at the 2009 SASN workshop
A Bayesian Analysis of Compressive Sensing Data Recovery in Wireless Sensor Networks
Authors: Riccardo Masiero, Giorgio Quer, Michele Rossi, Michele Zorzi
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| VTC Spring 2009 | The link aside left is to download my paper presented at the 2009 VTC Spring conference
A Note on the Buffer Overlap Among Nodes Performing Random Network Coding in Wireless Ad Hoc Networks
Authors: Riccardo Masiero, Daniele Munaretto, Michele Rossi, Joerg Widmer, Michele Zorzi
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| ITA 2009 | The link aside left is to download my paper presented at the 2009 ITA workshop
On the Interplay Between Routing and Signal Representation for Compressive Sensing in Wireless Sensor Networks
Authors: Giorgio Quer, Riccardo Masiero, Daniele Munaretto, Michele Rossi, Joerg Widmer, Michele Zorzi
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| RR-7345 INRIA Report | The link aside left is to download the research report written during my research period at INRIA, Sophia Antipolis, France
Distributed Sub-gradient Method for Delay Tolerant Networks
Authors: Riccardo Masiero, Giovanni Neglia
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| On the Interplay Between Routing and Signal Representation for Compressive Sensing in Wireless Sensor Networks
Authors: Giorgio Quer, Riccardo Masiero, Daniele Munaretto, Michele Rossi, Joerg Widmer, Michele Zorzi
Abstract Compressive Sensing (CS) shows high promise for fully distributed compression in wireless sensor networks (WSNs). In theory, CS allows the approximation of the readings from a sensor field with excellent accuracy, while collecting only a small fraction of them at a data gathering point. However, the conditions under which CS performs well are not necessarily met in practice. CS requires a suitable transformation that makes the signal sparse in its domain. Also, the transformation of the data given by the routing protocol and network topology and the sparse representation of the signal have to be incoherent, which is not straightforward to achieve in real networks. In this work we address the data gathering problem in WSNs, where routing is used in conjunction with CS to transport random projections of the data. We analyze synthetic and real data sets and compare the results against those of random sampling. In doing so, we consider a number of popular transformations and we find that, with real data sets, none of them are able to sparsify the data while being at the same time incoherent with respect to the routing matrix. The obtained performance is thus not as good as expected and finding a suitable transformation with good sparsification and incoherence properties remains an open problem for data gathering in static WSNs. Download this paper. Up to Conference Papers. | |||||
| A Note on the Buffer Overlap Among Nodes Performing Random Network Coding in Wireless Ad Hoc Networks
Authors: Riccardo Masiero, Daniele Munaretto, Michele Rossi, Joerg Widmer, Michele Zorzi
Abstract Network coding is a technique which is particularly suitable for the
dissemination of data in distributed ad hoc networks. The definition of a mathematical model that describes the interactions among nodes and, in particular, their relationship in terms of buffer subspaces is still an open and challenging problem.The contribution of this paper is an analysis of the relationship between the network topology and the subspace overlap among nodes. This analysis can be used to establish criteria for the design of packet combination policies in diverse networking scenarios. Differently from previous studies, we will explicitly take the overlap among subspaces into account through a framework comprising networks with fixed as well as mobile nodes. Download this paper. Up to Conference Papers. | |||||
| A Bayesian Analysis of Compressive Sensing Data Recovery in Wireless Sensor Networks
Authors: Riccardo Masiero, Giorgio Quer, Michele Rossi, Michele Zorzi
Abstract In this paper we address the task of accurately reconstructing a distributed signal through the collection of a small number of samples at a data gathering point using Compressive Sensing (CS) in conjunction with Principal Component Analysis (PCA). Our scheme compresses in a distributed way real world non-stationary signals, recovering them at the data collection point through the online estimation of their spatial/temporal correlation structures. The proposed technique is hereby characterized under the framework of Bayesian estimation, showing under which assumptions it is equivalent to optimal maximum a posteriori (MAP) recovery. As the main contribution of this paper, we proceed with the analysis of data collected by our indoor wireless sensor network (WSN) testbed, proving that these assumptions hold with good accuracy in the considered real world scenarios. This provides empirical evidence of the effectiveness of our approach and proves that CS is a legitimate tool for the recovery of real-world signals in WSNs. Download this paper. Up to Conference Papers. | |||||
| Data Acquisition through joint Compressive Sensing and Principal Component Analysis
Authors: Riccardo Masiero, Giorgio Quer, Daniele Munaretto, Michele Rossi, Joerg Widmer, Michele Zorzi
Abstract In this paper we look at the problem of accurately reconstructing distributed signals through the collection of a small number of samples at a data gathering point. The techniques that we exploit to do so are Compressive Sensing (CS) and Principal Component Analysis (PCA). PCA is used to find transformations that sparsify the signal, which are required for CS to retrieve, with good approximation, the original signal from a small number of samples. Our approach dynamically adapts to non-stationary real world signals through the online estimation of their correlation properties in space and time; these are then exploited by PCA to derive the transformations for CS. The approach is tunable and robust, independent of the specific routing protocol in use and able to substantially outperform standard data collection schemes. The effectiveness of our recovery algorithm, in terms of number of transmissions in the network {\it vs} reconstruction error, is demonstrated for synthetic as well as for real world signals which we gathered from an actual wireless sensor network (WSN) deployment. We stress that our solution is not limited to WSNs, but can be readily applied to other types of network infrastructures that require the online approximation of large and distributed data sets. Download this paper. Up to Conference Papers. | |||||
| WSN-Control: Signal Reconstruction through
Compressive Sensing in Wireless Sensor Networks
Authors: Giorgio Quer, Davide Zordan, Riccardo Masiero, Michele Zorzi, Michele Rossi
Abstract
The main contribution of this paper is the implementation and experimental evaluation of a signal reconstruction framework for Wireless Sensor Networks (WSNs). We design WSN-Control, an architecture to control a WSN from an external server connected to the Internet. Within such architecture, we implement a compression and recovery technique that combines Principal Component Analysis (PCA) and Compressive Sensing (CS) to reconstruct signals with many components from a sensor field through the collection of a relatively small number of samples, i.e., through incomplete representations of the actual signal. Overall, our experimental results show that a careful use of CS recovery is effective and can lead to a fully automated system for data gathering and reconstruction of real world and non-stationary signals in WSNs. In detail, WSN-Control effectively recovers signals showing some temporal and/or spatial correlation, from a relatively small number of samples, even below 20 %, keeping the relative reconstruction error smaller than 0.005.
Signals with more irregular and quickly varying statistics are also recovered, even though the reconstruction error becomes highly dependent on the number of collected samples. CS minimization is obtained through the recently proposed NESTA optimization algorithm. Our implementation of CS recovery is available here .
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| Distributed Subgradient Methods for Delay Tolerant Networks Authors: Riccardo Masiero, Giovanni Neglia
Abstract
In this paper we apply distributed sub-gradient methods to optimize global performance in Delay Tolerant Networks (DTNs). These methods rely on simple local node operations and consensus algorithms to average neighbours' information. Existing results for convergence to optimal solutions can only be applied to DTNs in the case of synchronous operation of the nodes and memory-less random meeting processes. In this paper we address both these issues. First, we prove convergence to the optimal solution for a more general class of mobility models. Second, we show that, under asynchronous operations, a direct application of the original sub-gradient method would lead to suboptimal solutions and we propose some adjustments to solve this problem. Further, at the end of the paper, we illustrate a possible DTN application to demonstrate the validity of this optimization approach.
Download this paper. Up to Conference Papers. | |||||
| The NAUTILUS project: Physical Parameters,Architectures and Network Scenarios Authors: Riccardo Masiero, Paolo Casari, Michele Zorzi
Abstract
The NAUTILUS (Network Architecture and protocols for Underwater Telerobotics via acoustIc Links in Ubiquitous Sensing, monitoring and explorations) project aims at providing a comprehensive study of the technical issues related to the realization of a complete solution for
the network architecture and the communications protocols needed for the tele-operation of underwater robots. When pursuing this goal, the need to implement realistic scenarios for underwater simulations clearly emerges. In this paper, starting from the investigation on the state-of-the-art carried out for the NAUTILUS project,we list the main concepts and parameters that underlie realistic simulations
of underwater scenarios. Also, we present and thoroughly discuss the choices made in terms of parameters, network architectures and models for the NAUTILUS project itself. We believe that the information collected in this paper provides a good starting point for the development of a realistic underwater performance evaluation tool.
Download this paper. Up to Conference Papers. | |||||
| DESERT Underwater: an NS-Miracle-based framework to DEsign, Simulate, Emulate and Realize Test-beds for Underwater network protocols Authors: Riccardo Masiero, Saiful Azad, Federico Favaro, Matteo Petrani, Giovanni Toso, Federico Guerra, Paolo Casari, Michele Zorzi
Abstract
DESERT Underwater (short for DEsign, Simulate, Emulate and Realize Test-beds for Underwater network protocols) is a complete set of public C/C++ libraries to support the design and implementation of underwater network protocols. Its creation stems from the will to push the studies on underwater networking beyond simulations.Implementing research solutions on actual devices, in fact, is of key importance to realize a communication and networking architecture that allows heterogeneous nodes to communicate reliably in the underwater environment. In this paper, we first discuss the rationale behind this work, and, then we list and briefly describe all the DESERT Underwater libraries currently implemented. In line with the current trends in underwater networking, our approach makes it possible to reuse the same code prepared
for simulations in order to realize underwater network prototypes. We also present some preliminary tests that confirm the feasibility of the proposed solution for the design and evaluation of underwater network protocols. In this perspective, we believe that DESERT Underwater
is a useful tool to profitably develop and test real world applications.
Download this paper. Up to Conference Papers. | |||||