Riccardo Masiero

On the interplay between routing and signal representation for Compressive Sensing in wireless sensor networks

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.

Sensing, Compression, and Recovery for WSNs: Sparse Signal Modeling and Monitoring Framework

We address the problem of compressing large and distributed signals monitored by a Wireless Sensor Network (WSN) and recovering them through the collection of a small number of samples. We propose a sparsity model that allows the use of Compressive Sensing (CS) for the online recovery of large data sets in real WSN scenarios, exploiting Principal Component Analysis (PCA) to capture the spatial and temporal characteristics of real signals. Bayesian analysis is utilized to approximate the statistical distribution of the principal components and to show that the Laplacian distribution provides an accurate representation of the statistics of real data. This combined CS and PCA technique is subsequently integrated into a novel framework, namely, SCoRe1: Sensing, Compression and Recovery through ON-line Estimation for WSNs. SCoRe1 is able to effectively self-adapt to unpredictable changes in the signal statistics thanks to a feedback control loop that estimates, in real time, the signal reconstruction error. We also propose an extensive validation of the framework used in conjunction with CS as well as with standard interpolation techniques, testing its performance for real world signals. The results in this paper have the merit of shedding new light on the performance limits of CS when used as a recovery tool in WSNs.

Data Acquisition through Joint Compressive Sensing and Principal Component Analysis

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 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.

A Bayesian analysis of Compressive Sensing data recovery in Wireless Sensor Networks

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.

Field experiments for Dynamic Source Routing: S2C EvoLogics modems run the SUN protocol using the DESERT Underwater libraries

In this paper we present a performance evaluation and feasibility test of SUN, a routing protocol for underwater networks inspired to Dynamic Source Routing (DSR), to which it adds several features that improve its behavior in underwater environments. The evaluation has been performed with real devices, and has been made possible through a collaboration between the Department of Information Engineering (DEI) of the University of Padova, Italy and EvoLogics GmbH, Germany. In detail, the idea put in practice in this work is to command real hardware, i.e., the S2C acoustic modems of EvoLogics, by means of the ns2/NS-Miracle engine developed and extensively used primarily by research institutions. This approach favors code reuse and speeds up the realization of flexible and easily modifiable network prototypes. Our results show that SUN can deal with typical network issues such as the disconnection of a node and the appearance of additional nodes, and that it copes well with dynamic topology changes.

Open source suites for underwater networking: WOSS and DESERT underwater

Simulation and experimentation of underwater networks entail many challenges, which for the former are mainly related to the accurate modeling of the channel behavior, while they are typically logistic in nature for the latter. In this article, we present our experience with WOSS and DESERT Underwater, two open source suites address both classes of challenges. The suites build on and extend the capabilities of ns2 and NS-MIRACLE, two widely known software packages for network simulation. WOSS endows NS-MIRACLE with the capability to generate realistic channel patterns by automatically retrieving and processing the environmental boundary conditions that influence such patterns; DESERT Underwater makes it possible to evolve toward at-sea experiments by reusing the same code written for simulations, thereby minimizing the effort required for network deployment and control. Both suites have been widely tested and used in several projects: some examples are provided in this respect, including an account of some experiments carried out in collaboration with the NATO STO Centre for Maritime Research and Experimentation.

WSN-Control: Signal reconstruction through Compressive Sensing in Wireless Sensor Networks

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 5 · 10−3. 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 in [1].

The NAUTILUS project: Physical parameters, architectures and network scenarios

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.

Embedded systems for prototyping underwater acoustic networks: The DESERT Underwater libraries on board the PandaBoard and NetDCU

In this paper, we consider underwater network prototyping using the network simulation engine NS-Miracle, and investigate different embedded computer boards that can be employed for this task. In particular, we consider two embedded platforms with considerably different capabilities: the PandaBoard (a powerful platform that does not require any cross-compilation effort) and version 5.2 of the NetDCU board, which is much more constrained in terms of computational power, RAM and storage space. After describing the steps required to install NS-Miracle and the DESERT Underwater libraries on board these platforms, we report on the field experiments conducted to test the corresponding prototypes. Our results include a comparison between the two investigated platforms in terms of resources required (e.g., memory occupancy and energy expenditure) and performance in the execution of real-time software (e.g., delays introduced within the simulation framework). We believe that our work represents an interesting step towards the realization of underwater network prototypes made of heterogeneous nodes.

A Note on the Buffer Overlap Among Nodes Performing Random Network Coding in Wireless Ad Hoc Networks

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.