Georgios Angelopoulos

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In the last few years, Network Coding (NC) has been shown to provide several advantages, both in theory and in practice. However, its applicability to battery-operated systems under strict power constraints has not been proven yet, since most implementations are based on high-end CPUs and GPUs. This work represents the first effort to bridge NC theory with(More)
— In this paper, we evaluate the performance of random linear network coding (RLNC) in low data rate indoor sensor applications operating in the ISM frequency band. We also investigate the results of its synergy with forward error correction (FEC) codes at the physical layer in a joint channel-network coding (JCNC) scheme. RLNC is an emerging coding(More)
— This paper proposes a partial packet recovery scheme, called Packetized Rateless Algebraic Consistency (PRAC). PRAC exploits intra and inter-packet consistency to identify and recover erroneous packet segments, without recourse to cross-layer or detailed feedback information. In the absence of cross-layer coordination or detailed feedback, the prevailing(More)
Proposal Summary—Nowadays, since more and more battery-operated devices are involved in applications with continuous sensing, development of an efficient sampling mechanisms is an important issue for these applications. In this paper, we investigate power efficiency aspects of a recently proposed adap-tive nonuniform sampling. This sampling scheme minimizes(More)
—In this paper, we present an empirical rate-distortion study of a communication scheme that uses compressive sensing (CS) as joint source-channel coding. We investigate the rate-distortion behavior of both point-to-point and distributed cases. First, we propose an efficient algorithm to find the 1-regularization parameter that is required by the Least(More)
—Advances in sampling and coding theory have contributed significantly towards lowering power consumption of resource-constrained devices, e.g. battery-operated sensor nodes, enabling them to operate for extended periods of time. In this paper, rate and energy efficiency of a recently proposed adaptive nonuniform sampling framework by Feizi et al., called(More)
—Compressed Sensing (CS) is a signal acquisition approach aiming to reduce the number of measurements required to capture a sparse (or, more generally, compressible) signal. Several works have shown significant performance advantages over conventional sampling techniques, through both theoretical analyses and experimental results, and have established CS as(More)
—This paper introduces AdaptCast, an integrated source to transmission scheme for wireless sensor networks (WSNs) that efficiently represents collected data and increases their robustness against channel errors across a wide range of signal to noise (SNR) values in a rateless fashion. AdaptCast leverages sparsity inherent in the majority of physical signals(More)