Long-Range Communications in Unlicensed Bands: the Rising Stars in the IoT and Smart City Scenarios

Connectivity is probably the most basic building block of the Internet of Things (IoT) paradigm. Up to know, the two main approaches to provide data access to the \emph{things} have been based either on multi-hop mesh networks using short-range communication technologies in the unlicensed spectrum, or on long-range, legacy cellular technologies, mainly 2G/GSM, operating in the corresponding licensed frequency bands. Recently, these reference models have been challenged by a new type of wireless connectivity, characterized by low-rate, long-range transmission technologies in the unlicensed sub-GHz frequency bands, used to realize access networks with star topology which are referred to a \emph{Low-Power Wide Area Networks} (LPWANs). In this paper, we introduce this new approach to provide connectivity in the IoT scenario, discussing its advantages over the established paradigms in terms of efficiency, effectiveness, and architectural design, in particular for the typical Smart Cities applications

Genetic Adversarial Training of Decision Trees

We put forward a novel learning methodology for ensembles of decision trees based on a genetic algorithm which is able to train a decision tree for maximizing both its accuracy and its robustness to adversarial perturbations. This learning algorithm internally leverages a complete formal verification technique for robustness properties of decision trees based on abstract interpretation, a well known static program analysis technique. We implemented this genetic adversarial training algorithm in a tool called Meta-Silvae (MS) and we experimentally evaluated it on some reference datasets used in adversarial training. The experimental results show that MS is able to train robust models that compete with and often improve on the current state-of-the-art of adversarial training of decision trees while being much more compact and therefore interpretable and efficient tree models

Quality-Aware Broadcasting Strategies for Position Estimation in VANETs

The dissemination of vehicle position data all over the network is a fundamental task in Vehicular Ad Hoc Network (VANET) operations, as applications often need to know the position of other vehicles over a large area. In such cases, inter-vehicular communications should be exploited to satisfy application requirements, although congestion control mechanisms are required to minimize the packet collision probability. In this work, we face the issue of achieving accurate vehicle position estimation and prediction in a VANET scenario. State of the art solutions to the problem try to broadcast the positioning information periodically, so that vehicles can ensure that the information their neighbors have about them is never older than the inter-transmission period. However, the rate of decay of the information is not deterministic in complex urban scenarios: the movements and maneuvers of vehicles can often be erratic and unpredictable, making old positioning information inaccurate or downright misleading. To address this problem, we propose to use the Quality of Information (QoI) as the decision factor for broadcasting. We implement a threshold-based strategy to distribute position information whenever the positioning error passes a reference value, thereby shifting the objective of the network to limiting the actual positioning error and guaranteeing quality across the VANET. The threshold-based strategy can reduce the network load by avoiding the transmission of redundant messages, as well as improving the overall positioning accuracy by more than 20% in realistic urban scenarios.Comment: 8 pages, 7 figures, 2 tables, accepted for presentation at European Wireless 201

Spinning nanorods - active optical manipulation of semiconductor nanorods using polarised light

In this Letter we show how a single beam optical trap offers the means for three-dimensional manipulation of semiconductor nanorods in solution. Furthermore rotation of the direction of the electric field provides control over the orientation of the nanorods, which is shown by polarisation analysis of two photon induced fluorescence. Statistics over tens of trapped agglomerates reveal a correlation between the measured degree of polarisation, the trap stiffness and the intensity of the emitted light, confirming that we are approaching the single particle limit.Comment: 7 pages, 4 figure

Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network

LTE and Millimeter Waves for V2I Communications: an End-to-End Performance Comparison

The Long Term Evolution (LTE) standard enables, besides cellular connectivity, basic automotive services to promote road safety through vehicle-to-infrastructure (V2I) communications. Nevertheless, stakeholders and research institutions, driven by the ambitious technological advances expected from fully autonomous and intelligent transportation systems, have recently investigated new radio technologies as a means to support vehicular applications. In particular, the millimeter wave (mmWave) spectrum holds great promise because of the large available bandwidth that may provide the required link capacity. Communications at high frequencies, however, suffer from severe propagation and absorption loss, which may cause communication disconnections especially considering high mobility scenarios. It is therefore important to validate, through simulations, the actual feasibility of establishing V2I communications in the above-6 GHz bands. Following this rationale, in this paper we provide the first comparative end-to-end evaluation of the performance of the LTE and mmWave technologies in a vehicular scenario. The simulation framework includes detailed measurement-based channel models as well as the full details of MAC, RLC and transport protocols. Our results show that, although LTE still represents a promising access solution to guarantee robust and fair connections, mmWaves satisfy the foreseen extreme throughput demands of most emerging automotive applications.Comment: 7 pages, 5 figures, 2 tables. Accepted to VTC-Spring 2019, workshop on High Mobility Wireless Communications (HMWC

Pulsed electric field processing of white grapes (cv. Garganega): Effects on wine composition and volatile compounds

Pulsed electric field (PEF) processing of white grapes (cv. Garganega) after crushing was studied on pilot-plant scale, to investigate the effects of the treatment on must and wine composition, wine color and predisposition to browning, wine aroma compounds and extraction of aroma precursors from grapes. PEF pre-treatment of grapes did not change the must or wine basic composition, nor was it able to modify the evolution of alcoholic fermentation. By contrast, PEF produced an increase in total dry extract, wine color and total phenolics. Treatment corresponding to a total specific energy of 22\u202fkJ\u202fkg 121 allowed more intense extraction of varietal aroma precursors without provoking excessive color evolution and extraction of phenolic compounds, apparently increasing the stability of wine towards oxidation. Due to the few papers available on this subject, PEF applications on white grapes should be optimized in further experiments