1,721,044 research outputs found
Using Active Learning to Decrease Probes for QoT Estimation in Optical Networks
No full textWe use active learning to reduce the number of probes needed for machine-learning-based QoT estimation. When building an estimation model based on Gaussian processes, only QoT instances that minimize estimation uncertainty are iteratively requested
An agent based framework for residential water usage modelling under social stimuli
No full textSmartH2O is an EU funded project aimed at increasing users' awareness about their water usage via a gamified platform where detailed information and statistics about the water usage patterns in the users' households are reported. Platform users can compete against each other in achieving the highest water usage reductions, thus stimulating virtuous behaviours. Such gamified portal also provides players' water usage data to an agent-based forecasting module which enables water utilities to get insights on the aggregate water usage of entire districts and on the effect of specific strategies to reduce/reallocate water usage, exploiting e.g. sensitivity to the social spreading of eco-friendly behaviours, by means of what-if experiments. In this paper we present the agent-based model, describe its building blocks and show its application to relevant use-cases by focusing on the social interaction aspect
A protocol for metering data pseudonymization in smart grids
No full textA tradeoff between data collection needs and user privacy is of paramount importance in the Smart Grid. This paper proposes a pseudonymization protocol for data gathered by the Smart Metres, which relies on a network infrastructure and a dedicated set of nodes, called privacy preserving nodes. The network privacy is enforced by a separation of duties; the privacy preserving nodes perform data pseudonymization without having access to the measurements, which are masked by means of a secret sharing scheme, while the entities accessing the data recover and relate the plain measurements generated by the same metre along a time window of finite duration but have no access to the metre identities. The paper also provides an evaluation of the security and of the performance of the protocol, comparing it to the two alternative encryption techniques, which mask the measurements by means of the Chaum mixing scheme or of an identity-based proxy re-encryption scheme
Reducing probes for quality of transmission estimation in optical networks with active learning
Full text linkEstimating the quality of transmission (QoT) of a lightpath before its establishment is a critical procedure for efficient design and management of optical networks. Recently, supervised machine learning (ML) techniques for QoT estimation have been proposed as an effective alternative to well-established, yet approximated, analytic models that often require the introduction of conservative margins to compensate for model inaccuracies and uncertainties. Unfortunately, to ensure high estimation accuracy, the training set (i.e., the set of historical field data, or samples, required to train these supervised ML algorithms) must be very large, while in real network deployments, the number of monitored/monitorable lightpaths is limited by several practical considerations. This is especially true for lightpaths with an above-threshold bit error rate (BER) (i.e., malfunctioning or wrongly dimensioned lightpaths), which are infrequently observed during network operation. Samples with above-threshold BERs can be acquired by deploying probe lightpaths, but at the cost of increased operational expenditures and wastage of spectral resources. In this paper, we propose to use active learning to reduce the number of probes needed for ML-based QoT estimation. We build an estimation model based on Gaussian processes, which allows iterative identification of those QoT instances that minimize estimation uncertainty. Numerical results using synthetically generated datasets show that, by using the proposed active learning approach, we can achieve the same performance of standard offline supervised ML methods, but with a remarkable reduction (at least 5% and up to 75%) in the number of training samples
Machine Learning methods for Quality-of-Transmission estimation : Chapter Seven
No full textMachine Learning (ML) is becoming an integral part of Quality-of-Transmission (QoT) estimation frameworks in optical networks. Application of ML is motivated by the increase in design and management complexity deriving by the emergence of new technologies such as elastic optical networking and coherent transmission. This chapter provides an overview of the application of ML-based methods for QoT estimation in optical networks. We start by introducing classical estimation approaches based on classification and regression, then we cover more recent methodologies, such as active learning and transfer learning. Additionally, we provide a discussion on the integration of ML-based QoT estimation within optimization tools for resource allocation. Finally, illustrative numerical results on the application of ML for QoT estimation conclude the chapter
Routing and Spectrum Assignment Integrating Machine-Learning-Based QoT Estimation in Elastic Optical Networks
Full text linkMachine Learning (ML) is under intense investigation in optical networks as it promises to lead to automation of a variety of management tasks, as amplifier gain equalization, fault recognition, Quality of Transmission (QoT) estimation, and many others. Though several studies focus on each of these specific tasks, the integration of ML-based estimations inside Routing and Spectrum Assignment (RSA) is still largely unexplored.This paper moves towards such integration. We develop a framework that leverages the probabilistic outputs of a ML-based QoT estimator to define the reach constraints in an Integer Linear Programming (ILP) formulation for RSA in an elastic optical network. In this integrated procedure, the RSA problem is solved iteratively by updating the reach constraints based on the outcome of a QoT estimator, to exclude lightpaths with unacceptable QoT. In our numerical evaluation, the proposed integrated method achieves savings in spectrum occupation up to 30% (around 20% on average) compared to traditional ILP-based RSA approaches with reach constraints based on margined analytical models
Detection and mitigation of the eclipse attack in chord overlays
No full textDistributed Hash Table-based overlays are widely used to support efficient information routing and storage in structured peerto-
peer networks, but they are also subject to numerous attacks aimed at disrupting their correct functioning. In this paper we
analyze the impact of the Eclipse attack on a Chord-based overlay in terms of number of key lookups intercepted by a collusion
of malicious nodes. We propose a detection algorithm for the individuation of ongoing attacks to the Chord network, relying on
features that can be independently estimated by each network peer, which are given as input to a C4.5-based binary classifier.
Moreover, we propose some modifications to the Chord routing protocol in order to mitigate the effects of such attacks. The
countermeasures can operate in a distributed fashion or assume the presence of a centralized trusted entity and introduce a limited
traffic overhead. The effectiveness of the proposed mitigation techniques has been shown through numerical results
Introduction to the JOCN Special Issue on Machine Learning Applied to QoT Estimation in Optical Networks
Full text linkOn the relation between the fields of Networked Music Performances, Ubiquitous Music, and Internet of Musical Things
Full text linkIn the past two decades, we have witnessed the diffusion of an increasing number of technologies, products, and applications at the intersection of music and networking. As a result of the growing attention devoted by academy and industry to this area, three main research fields have emerged and progressively consolidated: the Networked Music Performances, Ubiquitous Music, and the Internet of Musical Things. Based on the review of the most relevant works in these fields, this paper attempts to delineate their differences and commonalities. The aim of this inquiry is helping avoid confusion between such fields and achieve a correct use of the terminology. A trend towards the convergence between such fields has already been identified, and it is plausible to expect that in the future their evolution will lead to a progressive blurring of the boundaries identified today
An overview on networked music performance technologies
Full text linkNetworked music performance (NMP) is a potential game changer among Internet applications, as it aims at revolutionizing the traditional concept of musical interaction by enabling remote musicians to interact and perform together through a telecommunication network. Ensuring realistic performance conditions, however, constitutes a significant engineering challenge due to the extremely strict requirements in terms of network delay and audio quality, which are needed to maintain a stable tempo, a satisfying synchronicity between performers and, more generally, a high-quality interaction experience. In this paper, we offer a review of the psycho-perceptual studies conducted in the past decade, aimed at identifying latency tolerance thresholds for synchronous real-time musical performance. We also provide an overview of hardware/software enabling technologies for NMP, with a particular emphasis on system architecture paradigms, networking configurations, and applications to real use cases
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