Michele Rossi

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In this paper, we provide a comprehensive review of the existing literature on techniques and protocols for innetwork aggregation in wireless sensor networks. We first define suitable criteria to classify existing solutions, and then describe them by separately addressing the different layers of the protocol stack while highlighting the role of a(More)
Wireless reprogramming is a key functionality in wireless sensor networks (WSNs). In fact, the requirements for the network may change in time, or new parameters might have to be loaded to change the behavior of a given protocol. In large scale WSNs it makes economical as well as practical sense to upload the code with the needed functionalities without(More)
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(More)
In this paper we present a novel framework for ns2 to facilitate the simulation and, in general, the design of beyond 3G networks. The set of libraries we wrote for this purpose is called <i>Multi InteRfAce Cross Layer Extension</i> for ns2 (MIRACLE). They enhance the functionalities offered by the Network Simulator ns2 by providing an efficient and(More)
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(More)
This paper presents SYNAPSE++, a system for over the air reprogramming of wireless sensor networks (WSNs). In contrast to previous solutions, which implement plain negative acknowledgment-based ARQ strategies, SYNAPSE++ adopts a more sophisticated error recovery approach exploiting rateless fountain codes (FCs). This allows it to scale considerably better(More)
In this paper, we describe a practical realization of an Internet-of-Things (IoT) architecture at the University of Padova, Italy. Our network spans the floors of different buildings within the Department of Information Engineering, and is designed to provide access to basic services such as environmental monitoring and localization to University users, as(More)
Lossy temporal compression is key for energy-constrained wireless sensor networks (WSNs), where the imperfect reconstruction of the signal is often acceptable at the data collector, subject to some maximum error tolerance. In this article, we evaluate a number of selected lossy compression methods from the literature and extensively analyze their(More)
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(More)
In this paper, we present a study on the performance of TCP, in terms of both throughput and energy consumption, in the presence of a Wideband CDMA radio interface typical of third generation wireless systems. The results show that the relationship between throughput and average error rate is largely independent of the network load, making it possible to(More)