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Vegetal oils are considered a suitable substitute for mineral oil that is widely used in power transformers as insulation and cooling medium. Due to the fact that more and more power transformers containing this alternative insulating fluid are being constructed a mathematical ageing models is needed to help predict transformer failures. The continued performance of power transformers depends on the condition of its paper insulation mainly. In this sense, this paper analyses Kraft paper degradation through the loss of its mechanical strength. Accelerated thermal ageing test of the paper in two different vegetal oils were carried out at three temperatures during diverse periods of time, in order to obtain information on the kinetics of the ageing degradation of the paper. The evolution of the mechanical properties of paper failure are analysed as a function of temperature and ageing time. In addition, the results obtained are compared with the traditional method of degradation analysis, based on the measurement of the degree of polymerisation. The authors are grateful for the funding received to carry out this work from the State Scientific and Technical Research and Innovation Plan under the DPI2013-43897-P grant agreement, financed by the Government of Spain.

Blockchain technology has become almost as famous for incidents involving security breaches as for its innovative potential. We shed light on the prevalence and nature of these incidents through a database structured using the STIX format. Apart from OPSEC-related incidents, we find that the nature of many incidents is specific to blockchain technology. Two categories stand out: smart contracts, and techno-economic protocol incentives. For smart contracts, we propose to use recent advances in software testing to find flaws before deployment. For protocols, we propose the PRESTO framework that allows us to compare different protocols within a five-dimensional framework., 8 pages, 1 figure, accepted for presentation as a regular paper at IEEE Blockchain 2018 (Halifax, Canada)

This paper presents the design of a novel chipless harmonic RFID sensor in paper substrate based on a variable attenuator implemented as a π resistive network, which drives a single Schottky diode frequency doubler and a system of nested tapered annular slot antennas. The passive tag is interrogated by a signal at f0 = 1.2 GHz and the signal transmitted back to the reader is converted to 2f0 = 2.4 GHz in order for the system to be immune to clutter returns. The sensor information is encoded in the magnitude of the re-transmitted signal and a dynamic range around 20 dB is experimentally demonstrated.

Digitally-intensive PLLs have already demonstrated their effectiveness as frequency synthesizers for cellular applications, being able to fulfill the specifications in terms of phase noise, spurs and power dissipation thanks to the adoption of embedded digital processing that efficiently reduces the analog impairments. In this paper we review how these techniques further enable other capabilities, namely both wideband and linear frequency modulation. The application considered as a case study is the fast chirp generation for frequency-modulated continuous wave radar at millimeter waves. We illustrate how the combination of the two-point modulation of a digital PLL and the background digital pre-distortion of the DCO enable both fast and linear modulation.

Assistive service and companion robots are versatile and dexterous actuators that operate in our daily living environment. These robots are able to manipulate physical objects, to displace themselves and to engage in conversations. Human behavior is dynamic and oftentimes unpredictable, therefore it is crucial for such robotic systems to be assisted by a cloud-backend which: i) analyzes data from sensor and wearables ii) determines which robotic tasks need to be executed and iii) provides the necessary support for the execution of these tasks in our daily living environment. In this paper, we present our Internet-of-Robotic-Things system architecture design for a case study on personal interactions by a companion robot to alleviate behavioral disturbances of people with dementia.

Metallic materials are in a peculiar position for realization of electromagnetic shielding and antennas. Implanted devices, sometimes also responsible for vital functions of the hosting human body, should present reduced response to external electromagnetic interferences. In case transmission of data between the device and external environment is additionally required, the device should also be equipped with an antenna. The biocompatibility of such metallic parts is of fundamental importance: being them positioned on the external part of the device, they are the most exposed to be in close contact with the body environment. Investigations on the timely interaction between the metal based materials and the simulated hosting body by standardized physiological solution is described and quantified.

Software Defined Network (SDN) is considered as the key paradigm for the development of future networks, but there is still a lack of operational solutions, especially for an Internet Service Provider environment. In this work we propose the idea of an advanced SDN device, referred to as Smart SDN Forwarding Device (SSFD), able to implement Network Functions (NFs) on top of the Data Plane: in this way, it will be possible to realize hybrid SDN/IP networks and to reduce the load on the SDN Controller. We also provide a prototype, realized by means of open source software, that represents a first step toward the deployment of an operational device. The performance evaluation realized connecting the SSFD prototype to the experimental LISP network, shows that its performance are very close to the ones of a legacy device: the delay introduced for the control level operations is limited and negligible from an user perspective.

Combined with block diagonalization (BD) methods, Tomlinson-Harashima (TH) precoding and vector perturbation (VP) are attractive non-linear precoding techniques for multi-user multiple-input multiple-output (MU-MIMO) broadcast channels. In this paper, we first briefly review several non- linear precoding schemes including BD-VP, TH and zero-forcing (TH-ZF) and TH-VP, and then derive the achievable sum rate of the hybrid TH-VP precoding. After that, we compare the achievable sum rates and diversity gains of these non-linear precoding schemes with zero-forcing VP (ZF-VP) and dirty paper coding (DPC). The achievable sum rate of TH-VP is shown to approach the sum capacity of DPC asymptotically and outperforms BD-VP, TH-ZF and ZF- VP.

A key issue for the success of IoT, industry 4.0 and precision agriculture is the development of sensors based on new materials and technologies. In particular, sensors which are environmentally friendly and low cost are of interest. Cellulose is one of the most abundant material on the earth. Eventually, it is green, cheap and presents several intriguing mechanical and electrical properties. In this paper the sensing properties of an all-polymeric composite, realized by using bacterial cellulose, organic conductors and ionic liquids are investigated. More specifically, it is shown that this novel composite is capable of sensing flexural deformations and producing a corresponding electrical signal. The obtained results pave the road to the possibility of realizing a new class of deformation sensors that fits the requirements of more sustainable sensor technologies. The sensor has been conceived, realized and an experimental campaign with the characterization has been performed obtaining very intriguing results.

A cache-aided $K$-user Gaussian broadcast channel (BC) is studied. The transmitter has a library of $N$ files, from which each user requests one. The users are equipped with caches of different sizes, which are filled without the knowledge of the user requests in a centralized manner. Differently from the literature, it is assumed that each file can be delivered to different users at different rates, which may correspond to different quality representations of the underlying content, e.g., scalable coded video segments. Accordingly, instead of a single achievable rate, the system performance is characterized by a rate tuple, which corresponds to the vector of rates users' requests can be delivered at. The goal is to characterize the set of all achievable rate tuples for a given total cache capacity by designing joint cache and channel coding schemes together with cache allocation across users. Assuming that the users are ordered in increasing channel quality, each file is coded into $K$ layers, and only the first $k$ layers of the requested file are delivered to user $k$, $k=1,...,K$. Three different coding schemes are proposed, which differ in the way they deliver the coded contents over the BC; in particular, time-division, superposition, and dirty paper coding schemes are studied. Corresponding achievable rate regions are characterized, and compared with a novel outer bound. To the best of our knowledge, this is the first work studying the delivery of files at different rates over a cache-aided noisy BC., Comment: Part of this work was presented at the IEEE International Symposium on Information Theory, Colorado, USA, June 2018

In this paper, we consider the detection of suspiciously high correlation between malicious Internet users that are collaborating in order to cause a Denial of Service (DoS) Attack. The main goal is to recognise cyber incidents (more specifically, collaboration/dependency between different users) in order to ultimately isolate their behavior and avoid the consequenses of the DoS attack. The method relies on the analysis of the data traffic across the concerned network (with both incoming and outgoing traffic) in an effort to identify correlations between different users. The paper models normal and malicious behaviour via hidden Markov models, and analyses the performance of the method using both mathematical reasoning and simulations. © 2016 IEEE.

This paper presents a 2.4 GHz vector modulator fabricated on mechanically flexible, low-cost, environmentally friendly, celluose-based substrate. This circuit can be adopted either as a digitally controlled target for radars' testing, or, more in general, as an I/Q modulator in RF systems. The system is composed by a directional coupler, two mixers and a Wilkinson divider, all of them designed in microstrip technology and resulting in a (64 × 119) mm2 planar device. The modulator functionality is demonstrated by showing the results of a wired test in which the modulator is connected to a signal generator (input), to a spectrum analyzer (output) and digitally controlled by means of an Arduino board.

In this paper we propose two importance sampling methods for the efficient symbol error rate (SER) estimation of maximum likelihood (ML) multiple-input multiple-output (MIMO) detectors. Conditioned to a given transmitted symbol, computing the SER requires the evaluation of an integral outside a given polytope in a high-dimensional space, for which a closed-form solution does not exist. Therefore, Monte Carlo (MC) simulation is typically used to estimate the SER, although a naive or raw MC implementation can be very inefficient at high signal-to-noise-ratios or for systems with stringent SER requirements. A reduced variance estimator is provided by the Truncated Hypersphere Importance Sampling (THIS) method, which samples from a proposal density that excludes the largest hypersphere circumscribed within the Voronoi region of the transmitted vector. A much more efficient estimator is provided by the existing ALOE (which stands for "At Least One rare Event") method, which samples conditionally on an error taking place. The paper describes in detail these two IS methods, discussing their advantages and limitations, and comparing their performances. The work of V. Elvira was partially supported by Agence Nationale de la Recherche of France under PISCES project (ANR-17-CE40-0031-01) and the French-American Fulbright Commission. The work of I. Santamaria was partly supported by the Ministerio de Econom´ıa y Competitividad (MINECO) of Spain, and AEI/FEDER funds of the E.U., under grant TEC2016-75067-C4-4-R (CARMEN).

K. Miyagoshi, Y. Teranishi, T. Kawakami, T. Yoshihisa and S. Shimojo, "Proposal of a Logical Sensor Architecture using WoT-Based Edge Microservices," 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), Madrid, Spain, 2020, pp. 1223-1228, doi: 10.1109/COMPSAC48688.2020.00-89., 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC) [13-17 July 2020, Madrid, Spain], This paper proposes 'WoT-based Logical Sensor Architecture (WLSA),' a novel virtual sensor architecture on the basis of Web of Thing (WoT) that enables IoT applications to treat the sensor data and processing results in a uniform way in edge computing environments. WLSA reduces the computing and network resources required for the IoT applications by reusing the processing results in multiple data flows. This paper also proposes a data flow transforming (DFT) algorithm, in which a subset of the data flow is automatically replaced with a process that reuses the result of running processes so that the application developers do not need to conscious of sharing the processing result. We have implemented a prototype system and an object detection application based on WLSA using Node-RED data flow processing framework in an edge computing environment and evaluated its effectiveness. The evaluation results showed that WLSA reduced the execution time with less usage of computing resources. The prototype could accommodate 20 applications with less than 100 ms response time under 4% CPU usage.

This paper briefly summarizes an ongoing investigation on millimetre-wave (mmWave) channel sounding using a low complexity single RF chain mode mixing lens–loaded cavity. The main advantage of using the mode–mixing technique is to be able to process the information required to estimate the direction-of-arrival (DoA) of the incoming signal just by reading the signal at the single RF–chain output. This massively reduces the system complexity compared to a standard N–element array antenna that requires around N times RF chains. Specifically, this paper presents a way by which signal directivity towards the coverage area can be enhanced by placing a lens in–front of an oversized cavity. This provides an extra channel gain to the incoming wave, consequently enhancing the DoA estimation resolution. This ongoing investigation can substantially simplify the physical hardware layer of the mmWave channel sounder, offering a great potential for the forthcoming mmWave 5G.

This paper reports a novel modular multilevel converter with interleaved sub-modules (ISM-MMC). The ISM-MMC exhibit a higher scalability in current rating then conventional MMC structures with parallel devices. It can employ low-cost, low-current power switches rather than their bulky and expensive counterparts normally designed in classical MMCs. Another remarkable feature is that the number of the output voltage levels is synthetically multiplied by the number of interleaved SMs. The ISM-MMC is capable of bringing the known advantages of MMC to low voltage - high power applications making it a good candidate for the sector of ultra-fast chargers for electrical vehicles where typical power rating in excess of 1 MW is required for the low voltage supply. A proper modulation scheme is implemented and explained in this paper. A comparison with a classical MMC topology is also provided in terms of number of voltage levels, output voltage harmonic content, and number of components by fixing the number of SMs. Simulation results are given to demonstrate the feasibility of the proposed topology and the implemented modulation scheme. Despite this paper is dealing with a single-phase configuration, the extension to a three-phase scheme can be obtained in a straightforward manner.

In this paper the design and first experimental results of surface mountable bandpass filters in the L- and C- bands are presented. Used for frequency converters in High Throughput Satellite (HTS) systems, the L-band filter is centred at 960MHz with 500MHz bandwidth (FBW=52%) while the C- band filter is centred at 5.35GHz with 2.5GHz bandwidth (FBW=47%). The results presented in this paper are the outcomes of an ESA ARTES AT project called SUMO (‘Surface Mountable Filters for Frequency Converters’). Pre-distorted filter characteristics are proposed for both L- and C-band architectures in order to obtain flatter in-band responses (IL- flatness

A self-reliant, economically-affordable and maximum-efficiency solar water pumping system is proposed in this paper. The autonomy is achieved powering the system through a PV source which well fits for remote or isolated applications. The costs are reduced by removing the DC-DC voltage converter usually present in these applications. The efficiency is enhanced by using a Synchronous Reluctance motor that directly control the pump speed through a Maximum Power Point Tracking algorithm. The paper includes novel implementation hints and a batch of experimental results that prove the effectiveness of the proposal.

This paper proposes a 3D lumped parameter thermal network capable of estimating the temperature distribution in classical, salient-pole wound-field synchronous generators. The developed 3D lumped parameter thermal network can be used for the thermal design and/or analysis of any wound-field synchronous generator geometry, thus offering certain degrees of flexibility.As the accurate estimation of the loss distribution is critical for the accuracy of the proposed 3D lumped parameter thermal network, an electromagnetic finite element model is used in this paper as loss determination tool for an off-the-shelf 400 kVA generator considered as a case study. The simulated losses are validated against experimental measurements to safely include them in the developed network. Finally, the stator and rotor temperatures evaluated through the 3D lumped parameter thermal network are also validated by comparing them against experimental results.

In this paper we propose an approach for reusing formula solutions to reduce the impact of Satisfiability Modulo Theories (SMT) solvers on the scalability of symbolic program analysis. SMT solvers can efficiently handle huge expressions in relevant logic theories, but they still represent a main bottleneck to the scalability of symbolic analyses, like symbolic execution and symbolic model checking. Reusing proofs of formulas solved during former analysis sessions can reduce the amount of invocations of SMT solvers, thus mitigating the impact of SMT solvers on symbolic program analysis. Early approaches to reuse formula solutions exploit equivalence and inclusion relations among structurally similar formulas, and are strongly tighten to the specific target logics. In this paper, we present an original approach that reuses both satisfiability and unsatisfiability proofs shared among many formulas beyond only equivalent or related-by-implication formulas. Our approach straightforwardly generalises across multiple logics. It is based on the original concept of distance between formulas, which heuristically approximates the likelihood of formulas to share either satisfiability or unsatisfiability proofs. We show the efficiency and the generalisability of our approach, by instantiating the underlying distance function for formulas that belong to most popular logic theories handled by current SMT solvers, and confirm the effectiveness of the approach, by reporting experimental results on over nine millions formulas from five logic theories.

This paper faces a challenge in power education: how to teach the students the impact of different transmission lines on the power system at power frequency. More specifically, the paper exploits the very effective and elegant Ossanna’s theorem in order to make clear which are the allowable electrical regimes of different transmission line typologies in terms of power losses, voltage drops, transmissible powers and the conditions for the voltage collapse. Moreover, the formulation of the complex power at the sending-end of the power transmission line inferred from the Ossanna’s theorem, which has never been shown in technical literature, is clearly presented. It is worth noting that although the paper is focused on high and extra-high voltage transmission lines, the presented didactical approach can be indifferently applied to any voltage level.

In this paper the sequence impedances of an existing HVAC insulated cable, protected by a steel reinforced concrete structure, are estimated. The paper aims at investigating if the presence of metallic protective structures significantly affects the sequence impedances of the line. The present analyses are carried out by applying both the multiconductor cell analysis and the finite element method in order to model the HVAC line with and without protective structure. The results arising from the use of the different modelling approaches are compared.

In this paper, we study the potential advantages of integrating Unmanned Aerial Vehicles (UAVs) equipped with Base Stations, with a terrestrial mobile network infrastructure. In particular, our main focus here lays on handover and its role in B5G (Beyond 5G) networks. We consider a scenario where multiple UAVs and terrestrial bases jointly provide service to mobile ground users. By combining the UAV dynamic trajectory and radio access procedures, we investigate the impact and network performance of ground-to-air handovers. With respect to previous papers, we discuss how to optimise the UAV segment of the network. Final results show that the UAV needs to adjust its speed, height and antenna radiation angle to improve network performance.

Passive radars are notoriously robust to jamming because the receiver location cannot be detected. Moreover, the use of OFDM based reference signals further improves the robustness against jammers. Nevertheless, recent papers have shown that deception/spoofing-type of jamming may still be possible. The idea was to use a target-on-board deception jammer aimed at deceiving the receiver by preventing it from precisely locating and identifying the target when passive radar imaging is used. In this paper, the same authors test the proposed approach by using a measured DVB-T reference signal. A realistic scenario is emulated by using a multichannel DVB-T signal (three DVB-T channels - about 24 MHz bandwidth).

Size and absolute concentration of suspensions of nanoparticles are important information for the study and development of new materials and products in different industrial applications spanning from biotechnology and pharmaceutics to food preparation and conservation. Laser Transmission Spectroscopy (LTS) is the only methodology able to measure nanoparticle size and concentration by performing a single measurement. In this paper we report on a new variable gain calibration procedure for LTS-based instruments allowing to decrease of an order of magnitude the experimental indetermination of the particle size respect to the conventional LTS based on the double ratio technique. The variable gain calibration procedure makes use of a specifically designed tunable-gain, dual-channel, dual-phase Lock-In Amplifier (LIA) whose input voltage signals are those ones generated by two Si photodiodes that measure the laser beam intensities passing through the sample containing the nanoparticles and a reference optical path. The LTS variable gain calibration procedure has been validated by firstly using a suspension of NIST standard polystyrene nanoparticles even 36 hours after the calibration procedure was accomplished. The paper reports in detail the LIA implementation describing the design methodologies and the electronic circuits. As a case example of the characterization of biological nanostructures, we demonstrate that a single LTS measurement allowed to determine size density distribution of a population of extracellular vesicles extracted from orange juice (25 nm in size) with the presence of their aggregates having a size of 340 nm and a concentration smaller than 3 orders of magnitude.

The fourth industrial revolution combines cutting-edge technologies such as Industrial Internet of Things (IIoT) and cloud computing. However, this inevitable revolution faces challenges due to the presence of legacy equipment in industrial systems. Legacy equipment hinders the adoption of emerging cloud technologies due to data privacy, security and interoperability issues. This paper proposes a secure cloud migration approach for industrial systems and investigates whether it can meet real-time control system requirements without compromising system safety and security. The proposed approach is generic enough to be applied for different industrial sectors with minimal interruption to operations during the cloud migration process. Due to the large number of IIoT devices, manual system monitoring to pin-point issues is a tedious and time-consuming task. This paper also proposes an automated monitoring and management framework for large complex IIoT network that tracks and reports issues using different forms of notifications. Experimental validation on a real microgrid testbed facility concluded that the proposed approach is promising for time-critical industrial systems.

In the last years the multimedia traffic on IP networks has increased significantly. According to Cisco, the video contents are expected to reach 82% of the total IP traffic by 2022. The explosion of video on demand and real time/live event videos causes several issues for Telco networks, such as increased delay in content delivery, frequent congestions and service unavailability. In this paper we analyse an integration model between the broadcaster’s network and the Telcos’ fixed access networks. In particular, contents from the distribution network of broadcasters are directly injected into the central offices of Telco’s network. This integrated architecture allows offloading the core and metro segments of the Telco network and, at the same time, it enables broadcasters to provide contents with reduced latency and to avoid typical inconveniences in Telco’s network. Performance analysis is carried out in terms of the required transmission power per multiplex. As shown in the paper, the considered integrated network architecture can achieve cost savings in terms of transmission power per multiplex. The possibility of improving current FTTC access networks for supporting broadcast by means of sharing of multiple copper pairs has been also investigated.

The blockchain has found numerous applications in many areas with the expectation to significantly enhance their security. The Internet of things (IoT) constitutes a prominent application domain of blockchain, with a number of architectures having been proposed for improving not only security but also properties like transparency and auditability. However, many blockchain solutions suffer from inherent constraints associated with the consensus protocol used. These constraints are mostly inherited by the permissionless setting, e.g. computational power in proof-of-work, and become serious obstacles in a resource-constrained IoT environment. Moreover, consensus protocols with low throughput or high latency are not suitable for IoT networks where massive volumes of data are generated. Thus, in this paper we focus on permissioned blockchain platforms and investigate the consensus protocols used, aiming at evaluating their performance and fault tolerance as the main selection criteria for (in principle highly insecure) IoT ecosystem. The results of the paper provide new insights on the essential differences of various consensus protocols and their capacity to meet IoT needs.

Network densification over space and spectrum is expected to be key to enabling the requirements of next generation mobile systems. The pitfall is that radio resource allocation becomes substantially more complex. In this paper we propose LaSR, a practical multi-connectivity scheduler for OFDMA-based multi-RAT systems. LaSR makes optimal discrete control actions by solving a sequence of simple optimization problems that do not require prior information of traffic patterns. In marked contrast to previous work, the flexibility of our approach allows us to construct scheduling policies that achieve a good balance between system cost and utility satisfaction, while jointly operate across heterogeneous RATs, accommodate real-system requirements, and guarantee system stability. Examples of system requirements considered in this paper include (but are not limited to): constraints on how scheduling data can be encoded onto signaling protocols (e.g. LTE’s DCI), delays when turning on/off radio units, or on/off cycles when using unlicensed spectrum. We evaluate our scheduler via a thorough simulation campaign in a variety of scenarios with e.g. mobile users, RATs using unlicensed spectrum (using a duty cycle access mechanism), imperfect queue state information, and constrained signaling protocol. The authors would like to thank the Spanish Government (Ministerio de Economía y Competitividad, Fondo Europeo de Desarrollo Regional, FEDER) for its support through the project ADVICE (TEC2015-71329-C2-1-R) and 5G-Transformer Project (Grant 761536).

The $2 \times 25$ kV railway systems are multi-engineering systems which involve many simulation issues. They require appropriate modelling and simulation tools to solve these issues and accurately reflect characteristic changes during system operation. This paper analyses the AC railway systems characteristics and several hypotheses that are proposed to simplify these simulation issues. After that, the systems simulation models are built by utilisation of two different cyber-physical modelling and simulation tools: Modelica and Matlab-Simscape. The respective characteristics of these two tools are compared in this paper as well. The results show that the two different tools have the nearly equivalent accuracy, and Modelica is more flexible and easier to use.

International audience; We present the design of a highly wearable ex-oskeleton of hand fingers that can be used for rehabilitation applications. One of the main challenges in the design of this type of device is to reduce as much as possible the encumbrance and weight, and at the same time to guarantee performance suitable to provide a realistic and reliable motion to the user. In the proposed solution each finger is actuated by means of a single motor, and finger joint rotations are coupled to produce a natural and intuitive movement of the finger, for this reason, we exploited the concept of postural synergies. The paper presents the main design steps, the criteria adopted to choose the mechanical structure of the exoskeleton, and the features of its prototype. Compared to other solutions present in the literature, the device presented in this paper has limited weight (40 g per finger) with an interesting level of performance in terms of force (15 N).

In an era of technological revolution in which everything becomes smarter and connected, the blockchain can introduce a new model for energy transactions able to grant more simplicity, security and transparency for end-users. The blockchain technology is characterized by a distributed architecture without a trusted and centralized authority, and, therefore, it appears as the perfect solutions for managing exchanges between peers. In this paper, a market algorithm that can be easily transferred to a smart contract for maximizing the match between produced and consumed energy in a micro-grid is presented. The algorithm supports energy transactions between peers (both producers and consumers) and could be one of the main executables implemented using a blockchain platform. The case study presented in this paper shows how the end-users through the blockchain could select among the possible energy transactions those more suitable to offer specific ancillary services to the grid operator without involving the grid operator itself or a third-party aggregator.

In this paper, an electric vehicle smart charging approach is proposed to minimize the renewable power curtailment in a self-consumption facility consisting in a sustainable prosumer building equipped with a solar photovoltaic field and a micro wind turbine generator. This facility is not allowed to inject surplus energy into the public distribution power grid in order to minimize network congestions. As stationary energy storage devices are not competitive at nanogrid level nowadays, the smart charging of electric vehicles seems to be a feasible and cost-effective solution. The paper describes the Mixed-Integer Linear Programming model which has been specifically developed to operate the nanogrid optimally allowing a 76% reduction of the curtailed energy in comparison with the same system without the smart charging station, and a 67% daily savings in comparison with a traditional consumer of the same size and characteristics.

In this paper, the authors present a study comparing the reliability and the availability of several architectures based on LoRaWAN Linear Sensor Networks, to be employed for the detection of Black Powder in pipelines. The paper describes how to maximize the network reliability and availability once the constraints related both to the number of nodes and to the number of gateways have been established. In particular, the paper presents a general approach based on the introduction of redundancies in the linear communication network infrastructure, aiming to provide a general methodology to overcome the risk of network failure in case on sensor nodes malfunctioning. A comprehensive set of six possible scenarios has been described and commented in order to identify the most efficient strategy depending on the desired reliability degree.

Contours extraction, also known as chain-code extraction, is one of the most common algorithms of binary image processing. Despite being the raster way the most cache friendly and, consequently, fast way to scan an image, most commonly used chain-code algorithms perform contours tracing, and therefore tend to be fairly inefficient. In this paper, we took a rarely used algorithm that extracts contours in raster scan, and optimized its execution time through template functions, look-up tables and decision trees, in order to reduce code branches and the average number of load/store operations required. The result is a very fast solution that outspeeds the state-of-the-art contours extraction algorithm implemented in OpenCV, on a collection of real case datasets. Contribution-This paper significantly improves the performance of existing chain-code algorithms, by smartly introducing decision trees to reduce code branches and the average number of load/store operations required.

The current cellular technology and vehicular networks cannot satisfy the mighty strides of vehicular network demands. Resource management has become a complex and challenging objective to gain expected outcomes in a vehicular environment. The 5G cellular network promises to provide ultra-high-speed, reduced delay, and reliable communications. The development of new technologies such as the network function virtualization (NFV) and software defined networking (SDN) are critical enabling technologies leveraging 5G. The SDN-based 5G network can provide an excellent platform for autonomous vehicles because SDN offers open programmability and flexibility for new services incorporation. This separation of control and data planes enables centralized and efficient management of resources in a very optimized and secure manner by having a global overview of the whole network. The SDN also provides flexibility in communication administration and resource management, which are of critical importance when considering the ad-hoc nature of vehicular network infrastructures, in terms of safety, privacy, and security, in vehicular network environments. In addition, it promises the overall improved performance. In this paper, we propose a flow-based policy framework on the basis of two tiers virtualization for vehicular networks using SDNs. The vehicle to vehicle (V2V) communication is quite possible with wireless virtualization where different radio resources are allocated to V2V communications based on the flow classification, i.e., safety-related flow or non-safety flows, and the controller is responsible for managing the overall vehicular environment and V2X communications. The motivation behind this study is to implement a machine learning-enabled architecture to cater the sophisticated demands of modern vehicular Internet infrastructures. The inclination towards robust communications in 5G-enabled networks has made it somewhat tricky to manage network slicing efficiently. This paper also presents a proof of concept for leveraging machine learning-enabled resource classification and management through experimental evaluation of special-purpose testbed established in custom mininet setup. Furthermore, the results have been evaluated using Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Deep Neural Network (DNN). While concluding the paper, it is shown that the LSTM has outperformed the rest of classification techniques with promising results. King Saud University

Already available WiFi direct and upcoming 5G Device-to-Device (D2D) communication mechanisms are paving the way for the development of Mobile Ad-hoc Networks (MANET) applications. This trend involves the cooperation of nearby mobile nodes in charge of dispatching messages. In addition, the consolidation of the Fog paradigm enables innovative scenarios characterized by the interaction of MANET and Edge nodes. For instance, tourists visiting a city form a MANET to share pictures while the municipality provides Internet connectivity via Edge devices. However, it is required to address specific issues stemming from the collaborative nature of D2D communication, ranging from limited node capabilities providing multi-hop networks to unreliable connectivity due to node mobility. This paper presents the Reliable and Dynamic Routing Technique (RaDRT) solution, adopting the Software Defined Networking (SDN) approach to regulate routing of traffic flows in such Edge-MANET environments. To this purpose, RaDRT originally exploits the joint combination of three primary guidelines: 1) SDN to monitor/manage the state of the mobile network also considering different Quality of Service (QoS) requirements of concurrently running applications, 2) dynamic management of service priority to tune if and how packets are forwarded in a fine-grained per-flow differentiated manner, and 3) joined mobile/fixed solution to maximize the overall QoS also evaluating path reliability based on node mobility. This paper presents the Reliable and Dynamic Routing Technique (RaDRT) solution, adopting the Software Defined Networking (SDN) approach to regulate routing of traffic flows in such Edge-MANET environments. To this purpose, RaDRT originally exploits the joint combination of three primary guidelines: 1) SDN to monitor/manage the state of the mobile network also considering different Quality of Service (QoS) requirements of concurrently running applications, 2) dynamic management of service priority to tune if and how packets are forwarded in a fine-grained per-flow differentiated manner, and 3) joined mobile/fixed solution to maximize the overall QoS also evaluating path reliability based on node mobility.

The advent of the Internet of Things (IoT) together with its spread in industrial environments have changed production lines, by dramatically fostering the dynamicity of data sharing and the openness of machines. However, the increased flexibility and openness of the industrial environment (also pushed by the adoption of Edge devices) must not negatively affect the security and safety of production lines and its operational processes. In fact, opening industrial environments towards the Internet and increasing interactions among machines may represent a security threat, if not properly managed. The paper originally proposes the adoption of the Blockchain to securely store in distributed ledgers topology information and access rules, with the primary goal of maximizing the cyber-resiliency of industrial networks. In this manner, it is possible to store and query topology information and security access rules in a completely distributed manner, ensuring data availability even in case a centralized control point is temporarily down or the network partitioned. Moreover, Blockchain consensus algorithms can be used to foster a participative validation of topology information, to reciprocally ensure the identity of interacting machines/nodes, to securely distribute topology information and commands in a privacy-preserving manner, and to trace any past modification in a non-repudiable manner. The paper originally proposes the adoption of the Blockchain to securely store in distributed ledgers topology information and access rules, with the primary goal of maximizing the cyber-resiliency of industrial networks. In this manner, it is possible to store and query topology information and security access rules in a completely distributed manner, ensuring data availability even in case a centralized control point is temporarily down or the network partitioned. Moreover, Blockchain consensus algorithms can be used to foster a participative validation of topology information, to reciprocally ensure the identity of interacting machines/nodes, to securely distribute topology information and commands in a privacy-preserving manner, and to trace any past modification in a non-repudiable manner.

Nowadays software systems are organized around several and heterogeneous components. For example, a modern application can be composed of different microservices, along with dedicated components for machine learning analytics and recurring batch processing jobs. While containers offer a means to deploy the system and tackle heterogeneity, these components have different execution models, can exploit different resource types (e.g., CPUs and GPUs) and result in completely different execution times (milliseconds vs hours). This complexity calls for a new, scalable architecture to allow the systems to operate efficiently. This paper presents COCOS, an architecture, based on containers and control-theory, that is able to manage large and heterogeneous software systems. The architecture is based on a three-level hierarchy of controllers that cooperatively enforce user-defined requirements on execution times and consumed resources. The paper also shows a prototype implementation of COCOS based on Kubernetes, a well-known container orchestrator. The evaluation shows the efficiency of COCOS when dealing with microservices, Spark jobs and machine learning applications.

This paper has been presented at the 2020 European Conference on Networks and Communications (EuCNC) In the Zero-touch network and service management (ZSM) architecture, devised by ETSI, making predictions on the observed data is among the functions provided by the analytics block of the control loop cycle. Prediction performance depends on several parameters, such as the utilized computational resources, the leveraged prediction techniques, the deployment location of the prediction tools with respect to the data.This paper proposes a Hybrid Forecast Framework (HFF) running at the network edge or in the cloud to provide fore-casting with the performance required by the control loop cycle. Forecasting at the edge might shorten the control loop cycle if resources shall be made available locally where data is collected. However, in general, edge computational resources are less abundant than the cloud ones, thus causing longer time to perform the prediction. On the opposite, forecasting in the cloud might require more time for the data to reach the utilized tools but more computational resources could be exploited. The HFF is based on utilizing traditional time series analysis prediction algorithms to minimize the utilized resources and energy at the edge while it exploits AI/ML tools to make predictions in the cloud.Results show that for short lead time (i.e., the time, in the future, at which the status of the considered parameter is predicted) edge-based prediction exploiting time series analysis provides better accuracy, requires less resources and time (thus energy) than cloud-based prediction. However, if the lead time is long, cloud-based prediction exploiting Artificial Intelligence/Machine Learning (AI/ML) provides better accuracy. Thus, if the lead time is long, it is preferable because the long lead time compensates for the higher time for prediction, mainly due to data transfer. This work has been partially supported by the project "Scheme for Promotion of Academic and Research Collaboration (SPARC)", MHRD, Govt. of India and the EU Commission through the 5GROWTH project (grant agreement no. 856709).

Energy efficiency is a critical issue in the management of data centers, which form the backbone of cloud computing. Virtual resource management has a significant impact on improving the energy efficiency of data centers. Despite the progress in this area, virtual resource management has been considered mainly at two separate levels: application assignment and virtual machine placement. It has not been well-investigated in a unified framework for both levels, limiting further improvement in the energy efficiency of data centers. To address this issue, this paper proposes the virtual resource management problem for energy efficiency as a constrained optimization problem. Then, this paper simplifies the problem through profile-guided task classification and problem decomposition for complexity reduction and improved energy efficiency. After that, a three-phase framework and algorithms are presented for profiling and profile updating, task classification and application assignment, and successive virtual machine placement. Experimental studies show energy savings of 8–12% by the three-phase framework compared to the existing technique.

This paper presents a multistage approach for the day-ahead scheduling of a local energy community (LEC) aimed at considering the uncertainty associated to both the generation from renewable sources and the energy consumption. The considered LEC corresponds to a community of prosumers connected to the same distribution power network. In general, the considered prosumers are equipped with photovoltaic generating units, energy storage units, and local loads. The scheduling of the LEC is defined by a distributed optimization procedure based on the alternating direction method of multipliers (ADMM), which minimizes the total energy procurement costs. The adopted approach adjusts the operational planning according to the current operative conditions by means of a scenario-based multistage procedure. This paper describes the scenario tree generation and an intraday decision-making procedure based on the day-ahead solution. Moreover, numerical tests are presented for several operating scenarios.

In this paper a loss analysis of Dual Active Bridge DC-DC converter is performed. The circuit is conceived for DC Electrical Power Distribution System on aircraft, so leading to a 28V/270V system, whose operation in phase shift modulation is discussed. In particular, the paper proposes a comparison, in terms of power losses, among different wide band-gap commercial devices by means of thermal models suitable for accurate simulations in PLECS® environment. Numerical results highlight the impact of conduction and switching losses on system performance in terms of power density and safe operation.

In the paper, a method for improving the performance of data-driven Soft Sensors in the presence of small datasets problems is proposed. The method is based on a preliminary feature extraction phase, followed by a data selection method. Selected data are used to train neural-network-based Soft Sensors, modeling the featureoutput nonlinear relationship. A case of study referring to a Sour Water Stripping plant is used in the paper to validate the suitability of the proposed methodology.

Instrument transformers (ITs) are vital components of power quality (PQ) measurement systems in electrical transmission and distribution grids. Whereas measurement methods and accuracy are well defined for PQ measurement instruments, accurate knowledge about IT performance in presence of grid disturbances is missing. Main research themes of a project recently funded within the EMPIR program are described in the paper. The project focuses on the set-up of a metrological framework to enable the traceable measurement of PQ parameters when ITs are included in the measurement chains. Attention is focused on the development of reference measurement systems and the definition of IT test procedures, as well as on the quantification of the performance of ITs under realistic disturbance conditions. Moreover, an extensive characterization of voltage and current ITs will provide the basis for the identification of different sets of limits for the IT accuracy performance in PQ application and the definition of PQ accuracy classes., The research described in this Conference paper has been carried out within the 19NRM05 IT4PQ, which has received funding from the EMPIR programme co-financed by the Participating States and from the European Union's Horizon 2020 research andinnovation programme." Funder name: European Metrology Programme for Innovation and Research (EMPIR) Funder ID: 10.13039/100014132

Generalized Frequency-Division Multiplexing (GFDM) is a candidate modulation for future wireless cellular networks. The main reason stands in the flexibility of its structure that could fulfill ideas and challenges of forthcoming network scenarios. A known issue of GFDM is its worse performance compared to orthogonal frequency-division multiplexing, which is due to the interference among transmitted symbols. A mathematical model of such an interference has been proposed in a recent paper by exploiting the parallelism that exists between GFDM and discrete Gabor transform. The model allows for the design of different types of linear and non-linear equalizers. With the goal of increasing the transmission reliability, in this paper the introduction of channel coding is considered together with an appropriate interleaving. The computation of the Log-Likelihood Ratio (LLR) is described, which allows for soft decoding in the case of maximum likelihood and linear minimum mean squared error detection. The gain in performance achieved with channel coding and time-frequency interleaving is demonstrated by means of Monte Carlo simulations for the standard 64-state rate-1/2 convolutional code. A comparison with an approach for soft decoding proposed in the literature for GFDM is also reported.

This paper deals with a split capacitor three-phase four-wire inverter, able to deal with unbalanced ac currents and/or voltages. The considered topology can be used in many applications such as photovoltaic systems, chargers for electrical vehicles, active filters, and in general all the grid-connected applications. One parameter that must be considered in the converter design is the ac (output) current ripple, which should be determined and minimized in order to improve the system efficiency. In this paper, the evaluation of the ac current ripple for the three-phase split-capacitor inverter is developed with reference to both balanced and unbalanced output conditions. In particular, the peak-to-peak and the rms current ripple are analytically determined as a function of the modulation index, for each phase. Initially, reference is made to single-carrier sinusoidal PWM, being, in general, a simple and effective solution. The interleaved multiple-carrier PWM strategy is then considered in order to mitigate the current ripple in the neutral wire, while not affecting its phase counterpart. Numerical simulations have been carried out in order to verify the analytical developments.

Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning (DL) is already present in many applications ranging from computer vision for medicine to autonomous driving of modern cars as well as other sectors in security, healthcare, and finance. However, to achieve impressive performance, these algorithms employ very deep networks, requiring a significant computational power, both during the training and inference time. A single inference of a DL model may require billions of multiply-and-accumulated operations, making the DL extremely compute- and energy-hungry. In a scenario where several sophisticated algorithms need to be executed with limited energy and low latency, the need for cost-effective hardware platforms capable of implementing energy-efficient DL execution arises. This paper first introduces the key properties of two brain-inspired models like Deep Neural Network (DNN), and Spiking Neural Network (SNN), and then analyzes techniques to produce efficient and high-performance designs. This work summarizes and compares the works for four leading platforms for the execution of algorithms such as CPU, GPU, FPGA and ASIC describing the main solutions of the state-of-the-art, giving much prominence to the last two solutions since they offer greater design flexibility and bear the potential of high energy-efficiency, especially for the inference process. In addition to hardware solutions, this paper discusses some of the important security issues that these DNN and SNN models may have during their execution, and offers a comprehensive section on benchmarking, explaining how to assess the quality of different networks and hardware systems designed for them., Accepted for publication in IEEE Access

This paper illustrates the control system of a five-phase wound-rotor induction machine that can be used in applications where, besides the torque generation, a certain amount of active power must be transferred to loads collocated on the rotor frame. This requirement is common in rotary assembly platforms, bottle filling and capping systems, that embed electric actuators, sensors and process controllers on a rotating disk. The use of a five-phase machine avoids the slipring contacts to feed the rotor load. However, the choice of the set-point values for the motor currents involves magnetic, electric and thermal constraints. The paper illustrates a methodology of analysis, and develops a technique to deliver power to the rotor loads through the third-order spatial component of the magnetic field, while keeping the motor torque under control and reducing the rotor current as much as possible to minimize the rotor Joule losses. The experimental tests on a scaled prototype demonstrate the practical potential of the machine.

To estimate and predict the energy savings achievable with Building Automation and Control Systems, dynamic simulations are of fundamental importance. Such simulations can be performed with different types of software, among which, those based on simplified physical models are currently studied in the scientific literature. In fact, they can represent a fair compromise between the computational demand and the result accuracy. This paper shows how the CARNOT Toolbox for MATLAB/Simulink® can be effectively used for evaluating the impact of the automation system (used to manage the heating system) on the energy performance of a study case building. The building is located in the historic center of Pisa, Italy, and it is characterized by high wall thickness, which results in high thermal inertia. The building heating system has two type of heat generators: an air to water heat pump and a condensing boiler. The rooms of the building, currently used as offices, are heated by fan coil units. The control system, that manages the heating system, consists of a central multi-controller and a logical interface. They communicate, using the Modbus protocol, with all the components of the heating system that are connected by a bus cable. In the paper, the energy consumptions and CO 2 emissions of the building were evaluated for different scenarios, characterized by the implementation of different control functions of the heating system, selected according to the EN 15232 technical standard specifications.