Virtual Network Function Embedding with Quantum Annealing

In recent years, the growing number of devices connected to the internet led network operators to continuously expand their own infrastructures. In order to simplify this scaling process, the research community is currently investigating the opportunity to move the complexity from a hardware to a software domain, through the introduction of a new paradigm, called Network Functions Virtualisation (NFV). It considers standard hardware platforms where many virtual instances are allocated to implement specific network services. However, despite the theoretical benefits, the mapping of the different virtual instances to the available physical resources represents a complex problem, difficult to be solved classically. The present work proposes a Quadratic Unconstrained Binary Optimisation (QUBO) formulation of this embedding process, exploring the implementation possibilities on D-Wave’s Quantum Annealers. Many test cases, with realistic constraints, have been considered to validate and characterise the potential of the model, and the promising results achieved are discussed throughout the document. The technical discussion is enriched with comparisons of the results obtained through heuristic algorithms, highlighting the strengths and the limitations in the resolution of the QUBO formulation proposed on current quantum machines

PROMET&O: A Multidisciplinary Approach to Monitor Indoor Environmental Quality

Recent studies have explored the influence of Indoor Environmental Quality (IEQ) on the occupants’ perception, behavior and productivity at work [1]. Also, it has been proved that a poor IEQ may turn in negative further consequences on occupants also affecting mental comfort and health [2]. The assessment of IEQ is thus a complex task due to its nature that considers the thermal, air quality, lighting and acoustics domains at the same time. Alongside with these aspects, there is no evidence of the extent to which exposure to day-to-day low-frequency electromagnetic fields may arise long term health issues, although international guidelines specify exposure limits for work places. Recent works, such as [3], have also investigated the use of Wireless Sensor Networks (WNSs) for air pollution monitoring, however, they typically refer to urban environment. To sum up, multi-domain investigations are therefore needed, and the use of accurate devices for the acquisition of objective IEQ metrics is mandatory. This research aims at developing an innovative, accurate and low-cost system for the in-field monitoring of IEQ, i.e., the so called PROMET&O system. With respect to the current state of the art, PROMET&O will provide the integration of the measured IEQ metrics with feedback of the perceived Indoor Environmental Comfort (IEC) from occupants, encouraging best practices for energy saving. The proposed system architecture is shown in Fig. 1 and consists in several Multi-Sensors (MSs) collecting data related to the IEQ metrics to be monitored, which are sent to an open-access platform for further processing. Each MS is provided with a set of sensors, whose outputs are periodically sampled by a controller, shown in Fig. 2. Sensors have been selected being low-cost, low-power and small-sized. The respective measurement range and accuracy are reported in Tab. 1 and agree with the specific IEQ standards. To avoid self-heating or cross-sensitivity issues, a first placement of the MS layout is proposed as shown in Fig. 3. Based on the sampled data, statistics of the measured quantities are evaluated and transferred to the server to be stored in a database. Future work will focus on further development and experimental validation of such system in an open space offices to correlate IEQ measured metrics with occupants’ feedback

Development and Metrological Characterization of a Multi-sensor Device for Indoor Environmental Quality (IEQ) monitoring

Indoor Environmental Quality (IEQ), which affects people's health, comfort, well-being and productivity, combines thermal, visual, acoustic and air quality conditions. This work deals with design, development and metrological characterization of a low-cost multi-sensor device that is able to detect the quality conditions of indoor environments for IEQ purposes. The device, hereafter referred as PROMET&O (PROactive Monitoring for indoor EnvironmenTal quality & cOmfort) embeds a set of low-cost sensors that measure air temperature and relative humidity, illuminance, sound pressure level, carbon monoxide, carbon dioxide, particulate matter, formaldehyde, and nitrogen dioxide. The basic architecture of the device is described and the design criteria that are related to the measurement requirements are highlighted. Particular attention has been paid towards the traceability assurance of the measurements provided by PROMET&O by means of specifically conceived calibration procedures, which have been tailored to the requirements of each measurement quantity. The calibration is based on the comparison to reference standards following commonly employed or ad-hoc developed technical procedures. The defined calibration procedures can be applied both for the single sensors and for the set of sensors integrated in the multi-sensor case. For the latter, the effects of the percentage of permeable case surface and the sensors allocation are also investigated. A preliminary uncertainty evaluation of the proposed multi-sensor device is reported for the carbon dioxide and the illuminance sensors taking the defined calibration procedures into account

The cosmic ray primary composition in the "knee" region through the EAS electromagnetic and muon measurements at EAS-TOP

Abstract The evolution of the cosmic ray primary composition in the energy range 10 6 –10 7 GeV (i.e. the "knee" region) is studied by means of the e.m. and muon data of the Extensive Air Shower EAS-TOP array (Campo Imperatore, National Gran Sasso Laboratories). The measurement is performed through: (a) the correlated muon number ( N μ ) and shower size ( N e ) spectra, and (b) the evolution of the average muon numbers and their distributions as a function of the shower size. From analysis (a) the dominance of helium primaries at the knee, and therefore the possibility that the knee itself is due to a break in their energy spectrum (at E k He =(3.5±0.3)×10 6 GeV) are deduced. Concerning analysis (b), the measurement accuracies allow the classification in terms of three mass groups: light (p,He), intermediate (CNO), and heavy (Fe). At primary energies E 0 ≈10 6 GeV the results are consistent with the extrapolations of the data from direct experiments. In the knee region the obtained evolution of the energy spectra leads to: (i) an average steep spectrum of the light mass group ( γ p,He >3.1), (ii) a spectrum of the intermediate mass group harder than the one of the light component ( γ CNO ≃2.75, possibly bending at E k CNO ≈(6–7)×10 6 GeV), (iii) a constant slope for the spectrum of the heavy primaries ( γ Fe ≃2.3–2.7) consistent with the direct measurements. In the investigated energy range, the average primary mass increases from 〈ln A 〉=1.6–1.9 at E 0 ≃1.5×10 6 GeV to 〈ln A 〉=2.8–3.1 at E 0 ≃1.5×10 7 GeV. The result supports the standard acceleration and propagation models of galactic cosmic rays that predict rigidity dependent cut-offs for the primary spectra of the different nuclei. The uncertainties connected to the hadronic interaction model (QGSJET in CORSIKA) used for the interpretation are discussed

Final results of the tests on the resistive plate chambers for the ALICE muon arm

Abstract The trigger for the ALICE muon spectrometer will be issued by single-gap, low resistivity bakelite resistive plate chambers (RPCs). The trigger system consists of four 5.5 × 6.5 m 2 RPC planes arranged in two stations, for a total of 72 detectors. One hundred and sixteen detectors have been assembled and tested in Torino. The tests have been performed with the streamer mixture developed for heavy ion data-taking. The tests include: the detection of gas leaks and parasitic currents; the measurement of the efficiency with cosmic rays, with particular regard to the uniformity of the efficiency throughout the whole active surface, with a granularity of about 2 × 2 cm 2 ; the measurement of the dark current and of the mean and localised noise rate. All the RPCs produced have been characterised. Among them, the detectors to be finally installed in ALICE and some spare have been selected; 17% of all the produced detectors have been discarded. A short description of the test set-up is given. The results of the tests are presented, with particular regard to the performance of the selected detectors

Cryogenic Characterization of FBK RGB-HD SiPMs

We report on the cryogenic characterization of Red Green Blue - High Density (RGB-HD) SiPMs developed at Fondazione Bruno Kessler (FBK) as part of the DarkSide program of dark matter searches with liquid argon time projection chambers. A dedicated setup was used to measure the primary dark noise, the correlated noise, and the gain of the SiPMs at varying temperatures. A custom-made data acquisition system and analysis software were used to precisely characterize these parameters. We demonstrate that FBK RGB-HD SiPMs with low quenching resistance (RGB-HD-LR$_q$) can be operated from 40 K to 300 K with gains in the range $10^5$ to $10^6$ and noise rates on the order of a few Hz/mm$^2$

An investigation on duty-cycle for particulate matter monitoring with light-scattering sensors

Air pollution is a critical phenomenon of the era we live in. Traditional approaches to monitor this phenomenon involve the use of a sparse network of high-cost, high-precision fixed devices. Thanks to the decreasing cost of low-end sensors, it is now possible to implement much cheaper air pollution monitoring devices with respect to the past. Despite having lower accuracy, these devices are able to correctly monitor quantities such as Particulate Matter (PM). However, devices operating within the Internet of Things (IoT) framework still present several issues, such as power supply constraint, logging of redundant information, aging of sensors. To address these issues, this paper analyses how a change in the duty-cycle operation of PM sensors impacts aggregated data, to evaluate the loss of information. This analysis has been applied to both raw and calibrated values. The calibration adopted is a Multivariate Linear Regression using Relative Humidity as an additional independent variable. Results show that, in certain circumstances, even a great reduction of the active time does not significantly increase the information loss. The adoption of a duty-cycle operation mode enables a significant reduction of power consumption, slows the aging of sensors, and reduces logging and transmission of redundant information. Furthermore, since a reduced number of samples are generally required in a single location, mobile continuous sampling strategies can be adopted in order to increase the coverage of PM sensors

Measuring Particulate Matter: An Investigation on Sensor Technology, Particle Size, Monitoring Site

Light-scattering particulate matter sensors can be very inexpensive, but also unreliable. They are often presented as a solution for the creation of dense monitoring networks, offering an alternative approach to mathematical modeling and interpolation techniques applied to data from institutional monitoring stations. The purpose of this article is to study the benefits provided by light-scattering sensors in measuring daily concentrations of particulate matter, when they are used to complement the existing institutional network of high-quality instrumentation. A one-year experiment campaign was performed, placing 56 light-scattering sensors close to an official monitoring station. The effectiveness of the sensors is evaluated by comparing the correlation of the sensors with the official data of the nearby station, with respect to the correlation between the same station and other stations in the official monitoring network. Finally, the different data sources are calibrated on a single reference station, in order to act as predictors of the PM concentration in the point of interest. Estimates are analyzed using root mean square error (RMSE) comparison