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Construction is known as one of the most dangerous industries in terms of worker safety. Collisions due the excessive proximity of workers to moving construction vehicles are one of the leading causes of fatal and non-fatal accidents on construction sites internationally. Proximity warning systems (PWS) have been proposed in the literature as a solution to detect the risk for collision and to alert workers and equipment operators in time to prevent collisions. Although the role of sensing technologies for situational awareness has been recognised in previous studies, several factors still need to be considered. This paper describes the design of a prototype sensor-based PWS, aimed mainly at small and medium-sized construction companies, to collect real-time data directly from construction sites and to warn workers of a potential risk of collision accidents. It considers, in an integrated manner, factors such as cost of deployment, the actual nature of a construction site as an operating environment and data protection. A low-cost, ultra-wideband (UWB)-based proximity detection system has been developed that can operate with or without fixed anchors. In addition, the PWS is compliant with the General Data Protection Regulation (GDPR) of the European Union. A privacy-by-design approach has been adopted and privacy mechanisms have been used for data protection. Future work could evaluate the PWS in real operational conditions and incorporate additional factors for its further development, such as studies on the timely interpretation of data. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) approach relies on the use of the Internet Protocol (IP) as a pervasive network protocol. IP acts as a "glue" for interconnecting end devices (on the field side) and end users, leveraging on very diverse lower-level and upper-level protocols. The need for scalability would suggest the adoption of IPv6, but the large overhead and payloads do not match with the constraints dictated by common wireless solutions. For this reason, compression strategies have been proposed to avoid redundant information in the IPv6 header and to provide fragmentation and reassembly of long messages. For example, the Static Context Header Compression (SCHC) protocol has been recently referenced by the LoRa Alliance as a standard IPv6 compression scheme for LoRaWAN-based applications. In this way, IoT end points can seamlessly share an end-to-end IP link. However, implementation details are out of the specifications' scope. For this reason, formal test procedures for comparing solutions from different providers are important. In this paper, a test method for assessing architectural delays of real-world deployments of SCHC-over-LoRaWAN implementations is presented. The original proposal includes a mapping phase, for identifying information flows, and a subsequent evaluation phase, in which flows are timestamped and time-related metrics are computed. The proposed strategy has been tested in different use cases involving LoRaWAN backends deployed all around the world. The feasibility of the proposed approach has been tested by measuring the end-to-end latency of IPv6 data in sample use cases, obtaining a delay of less than 1 s. However, the main result is the demonstration that the suggested methodology permits a comparison of the behavior of IPv6 with SCHC-over-LoRaWAN, allowing the optimization of choices and parameters during deployment and commissioning of both infrastructure components and software. [ABSTRACT FROM AUTHOR]

Early initiation of antiretroviral therapy and adherence to achieve viral load suppression (VLS) are crucial for reducing morbidity and mortality of perinatally HIV-infected infants. In this descriptive cohort study of 39 HIV perinatally infected infants, who started treatment at one month of life in Mozambique, we aimed to describe the viral response over 2 years of follow up. VLS ≤ 400 copies/mL, sustained VLS and viral rebound were described using a Kaplan–Meier estimator. Antiretroviral drug transmitted resistance was assessed for a sub-group of non-VLS infants. In total, 61% of infants reached VLS, and 50% had a rebound. Cumulative probability of VLS was 36%, 51%, and 69% at 6, 12 and 24 months of treatment, respectively. The median duration of VLS was 7.4 months (IQR 12.6) and the cumulative probability of rebound at 6 months was 30%. Two infants had resistance biomarkers to drugs included in their treatment regimen. Our findings point to a low rate of VLS and high rate of viral rebound. More frequent viral response monitoring is advisable to identify infants with rebound and offer timely adherence support. It is urgent to tailor the psychosocial support model of care to this specific age group and offer differentiated service delivery to mother–baby pairs. [ABSTRACT FROM AUTHOR]

Nowadays, the data flow architecture is considered as a general solution for the acceleration of a deep neural network (DNN) because of its higher parallelism. However, the conventional DNN accelerator offers only a restricted flexibility for diverse network models. In order to overcome this, a reconfigurable convolutional neural network (RCNN) accelerator, i.e., one of the DNN, is required to be developed over the field-programmable gate array (FPGA) platform. In this paper, the sparse optimization of weight (SOW) and convolutional optimization (CO) are proposed to improve the performances of the RCNN accelerator. The combination of SOW and CO is used to optimize the feature map and weight sizes of the RCNN accelerator; therefore, the hardware resources consumed by this RCNN are minimized in FPGA. The performances of RCNN-SOW-CO are analyzed by means of feature map size, weight size, sparseness of the input feature map (IFM), weight parameter proportion, block random access memory (BRAM), digital signal processing (DSP) elements, look-up tables (LUTs), slices, delay, power, and accuracy. An existing architectures OIDSCNN, LP-CNN, and DPR-NN are used to justify efficiency of the RCNN-SOW-CO. The LUT of RCNN-SOW-CO with Alexnet designed in the Zynq-7020 is 5150, which is less than the OIDSCNN and DPR-NN. [ABSTRACT FROM AUTHOR]

The current perspective about urban development expects 70% of energy consumption will be concentrated in the cities in 2050. In addition, a growing density of people in the urban context leads to the need for increased security and safety for citizens, which imply a better lighting infrastructure. Smart solutions are required to optimize the corresponding energy effort. In developing countries, the cities' lighting is limited and the lighting world map is strongly significant about the urban density of the different areas. Nevertheless, in territories where the illumination level is particularly high, such as urban contexts, the conditions are not homogenous at the microscale level and the perceived security is affected by artificial urban lighting. As an example, 27.2% of the families living in the city of Milan, ombardy Region, Italy, consider critical the conditions of lighting in the city during the night, although the region has diffused infrastructure. The paper aims to provide a local illuminance geographic information system (GIS) mapping at the neighborhood level that can be extended to the urban context. Such an approach could unveil the need to increase lighting to enhance the perceived safety and security for the citizens and promote a higher quality of life in the smart city. Lighting mapping can be matched with car accident mapping of cities and could be extended to perceived security among pedestrians in urban roads and green areas, also related to degradation signs of the built environment. In addition, such an approach could open new scenarios to the adaptive street lighting control used to reduce the energy consumption in a smart city: the perceived security of an area could be used as an additional index to be considered during the modulation of the level of the luminosity of street lighting. An example of a measurement set-up is described and tested at the district level to define how to implement an extensive monitoring campaign based on an extended research schema. [ABSTRACT FROM AUTHOR]

In this paper, we present a conceptual study on a Virtual Power Plant (VPP) architecture for the optimal management of Distributed Energy Resources (DERs) owned by prosumers participating in Demand-Side Management (DSM) programs. Compared to classical VPP architectures, which aim to aggregate several DERs dispersed throughout the electrical grid, in the proposed VPP architecture the supervised physical domain is limited to single users, i.e., to single Points of Delivery (PODs) of the distribution network. The VPP architecture is based on a service-oriented approach, where multiple agents cooperate to implement the optimal management of the prosumer's assets, by also considering different forms of Demand Response (DR) requests. The considered DR schemes range from Price-Based DRs to Event-Based DRs, covering both the normal operating functions and the emergency control requests applied in modern distribution networks. With respect to centralized approaches, in this study the control perspective is moved from the system level to the single prosumer's level, who is allowed to independently provide flexible power profiles through the aggregation of multiple DERs. A generalized optimization model, formulated as a Mixed-Integer Linear Programming (MILP) problem, is also introduced. Such a model is able to compute the optimal scheduling of a prosumer's assets by considering both DR requests and end-users' requirements in terms of comfort levels while minimizing the costs. [ABSTRACT FROM AUTHOR]

Nowadays, a huge amount of digital data is frequently changed among different embedded devices over wireless communication technologies. Data security is considered an important parameter for avoiding information loss and preventing cyber-crimes. This research article details the low power high-speed hardware architectures for the efficient field programmable gate array (FPGA) implementation of the advanced encryption standard (AES) algorithm to provide data security. This work does not depend on the Look-Up Table (LUTs) for the implementation the SubBytes and InvSubBytes stages of transformations of the AES encryption and decryption; this new architecture uses combinational logical circuits for implementing SubBytes and InvSubBytes transformation. Due to the elimination of LUTs, unwanted delays are eliminated in this architecture and a subpipelining structure is introduced for improving the speed of the AES algorithm. Here, modified positive polarity reed muller (MPPRM) architecture is inserted to reduce the total hardware requirements, and comparisons are made with different implementations. With MPPRM architecture introduced in SubBytes stages, an efficient mixcolumn and invmixcolumn architecture that is suited to subpipelined round units is added. The performances of the proposed AES-MPPRM architecture is analyzed in terms of number of slice registers, flip flops, number of slice LUTs, number of logical elements, slices, bonded IOB, operating frequency and delay. There are five different AES architectures including LAES, AES-CTR, AES-CFA, AES-BSRD, and AES-EMCBE. The LUT of the AES-MPPRM architecture designed in the Spartan 6 is reduced up to 15.45% when compared to the AES-BSRD. [ABSTRACT FROM AUTHOR]

The management of the distribution network is becoming increasingly important as the penetration of distributed energy resources is increasing. Reliable knowledge of the real-time status of the network is essential if algorithms are to be used to help distribution system operators define network configurations. State Estimation (SE) algorithms are capable of producing such an accurate snapshot of the network state but, in turn, require a wide range of information, e.g., network topology, real-time measurement and power profiles from customers/productions. Those profiles which may, in principle, be provided by smart meters are not always available due to technical limitations of existing Advanced Metering Infrastructure (AMI) in terms of communication, storage and computing power. That means that power profiles are only available for a subset of customers. The paper proposes an approach that can overcome these limitations: the remaining profiles, required by SE algorithms, are generated on the basis of customer-related information, identifying clusters of customers with similar features, such as the same contract and pattern of energy consumption. For each cluster, a power profile estimator is generated using long-term power profiles of a limited sub-set of customers, randomly selected from the cluster itself. The synthesized full power profile, representing each customer of the distribution network, is then obtained by scaling the power profile estimator of the cluster to which the customer belongs, by the monthly energy exchanged by that customer, data that are easily available. The feasibility of the proposed approach was validated considering the distribution grid of Unareti SpA, an Italian Distribution System Operator (DSO), operating in northern Italy and serving approximately one million customers. The application of the proposed approach to the actual infrastructure shows some limitations in terms of the accuracy of the estimation of the power profile of the customer. In particular, the proposed methodology is not fully able to properly represent clusters composed of customers with a large variability in terms of power exchange with the distribution network. In any case, the root mean square error of the synthesized full power profile with the respect to validation power profiles belonging to the same cluster is, in the worst case, on the order of 6.3%, while in the rest of cases is well below 5%. Thus, the proposed approach represents a good compromise between accuracy in representing the behavior of customers on the network and resources (in terms of computational power, data storage and communication resources) to achieve that results. [ABSTRACT FROM AUTHOR]

Research on flexible unit systems (FUS) with the context of descriptive, predictive, and prescriptive analysis have remarkably progressed in recent times, being now reinforced in the current Industry 4.0 era with the increased focus on integration of distributed and digitalized systems. In the existing literature, most of the work focused on the individual contributions of the above mentioned three analyses. Moreover, the current literature is unclear with respect to the integration of degradation and upgradation models for FUS. In this paper, a systematic literature review on degradation, residual life distribution, workload adjustment strategy, upgradation, and predictive maintenance as major performance measures to investigate the performance of the FUS has been considered. In order to identify the key issues and research gaps in the existing literature, the 59 most relevant papers from 2009 to 2020 have been sorted and analyzed. Finally, we identify promising research opportunities that could expand the scope and depth of FUS. [ABSTRACT FROM AUTHOR]

The EU Green Deal, beginning in 2019, promoted a roadmap for operating the transition to a sustainable EU economy by turning climate issues and environmental challenges into opportunities in all policy areas and making the transition fair and inclusive for all. Focusing on the built environment, the voluntary adoption of rating systems for sustainability assessment is growing, with an increasing market value, and is perceived as a social responsibility both by public administration and by private companies. This paper proposes a framework for shifting from a static sustainability assessment to a digital twin (DT)-based and Internet of Things (IoT)-enabled dynamic approach. This new approach allows for a real-time evaluation and control of a wide range of sustainability criteria with a user-centered point of view. A pilot building, namely, the eLUX lab cognitive building in the University of Brescia, was used to test the framework with some sample applications. The educational building accommodates the daily activities of the engineering students by constantly interacting with the sensorized asset monitoring indoor comfort and air quality conditions as well as the energy behavior of the building in order to optimize the trade-off with renewable energy production. The framework is the cornerstone of a methodology exploiting the digital twin approach to support the decision processes related to sustainability through the whole building's life cycle. [ABSTRACT FROM AUTHOR]

In the Industry 4.0 the communication infrastructure is derived from the Internet of Things (IoT), and it is called Industrial IoT or IIoT. Smart objects deployed on the field collect a large amount of data which is stored and processed in the Cloud to create innovative services. However, differently from most of the consumer applications, the industrial scenario is generally constrained by time-related requirements and its needs for real-time behavior (i.e., bounded and possibly short delays). Unfortunately, timeliness is generally ignored by traditional service provider, and the Cloud is treated as a black box. For instance, Cloud databases (generally seen as "Database as a service"—DBaaS) have unknown or hard-to-compare impact on applications. The novelty of this work is to provide an experimental measurement methodology based on an abstract view of IIoT applications, in order to define some easy-to-evaluate metrics focused on DBaaS latency (no matter the actual implementation details are). In particular, the focus is on the impact of DBaaS on the overall communication delays in a typical IIoT scalable context (i.e., from the field to the Cloud and the way back). In order to show the effectiveness of the proposed approach, a real use case is discussed (it is a predictive maintenance application with a Siemens S7 industrial controller transmitting system health status information to a Cloudant DB inside the IBM Bluemix platform). Experiments carried on in this use case provide useful insights about the DBaaS performance: evaluation of delays, effects of involved number of devices (scalability and complexity), constraints of the architecture, and clear information for comparing with other implementations and for optimizing configuration. In other words, the proposed evaluation strategy helps in finding out the peculiarities of Cloud Database service implementations. [ABSTRACT FROM AUTHOR]

In the last few years, the technology re-evolution has deeply transformed several aspects of everyday life. For sure, one technology with a strong impact is the so-called Internet of Things (IoT). The IoT paradigm made it possible to break down the data barrier between the vertical domains on which the traditional information and communication technology (ICT) world was organized. Recently, the designers of home automation systems have begun looking to the IoT paradigm to ease the deployment of systems that are able to collect data from different plants. Such a situation has driven further evolution from the traditional automation system, where logic is defined by the programmer or by the user, to a cognitive system that is able to learn from the user' habits regarding what should be the best configuration of plants. Several countries are funding renovations of public and private buildings for improving energy efficiency. Generally, such renovations are only focusing on the structure of the building and of its energy performance (e.g., the thermal envelope, window units, air-conditioning plants, and renewable generators) and largely ignoring the use of intelligent devices. On the contrary, scientific literature and practice have demonstrated that the wider use of IoT sensors, as well as distributed and remote intelligence, is fundamental to optimize energy consumption. This research work aimed to identify issues due the application of cognitive solutions during the renovation phase of buildings. In particular, the paper presents a cognitive architecture to support the operation and management phases of buildings, thanks to the massive digitalization of the entire supply chain of the construction sector from the single building element to the entire construction process. Such an architecture is capable of combining data from the IoT sensors and actuators of smart objects installed during the renovation phase, as well as legacy building automation systems. As an indication of the capability of the proposed solution, an intelligent window device was developed and validated. Within the Energy, Life Styled, and Seismic Innovation for Regenerated Buildings (ELISIR) project, window units are equipped with sensors to monitor indoor and outdoor condition behaviours of users. In addition, windows are able to react to changes in the environment by means of actuators that enable motorized opening and shading. Thanks to the cognitive layer designed in the project, the window is able to automatically define the best rules for opening and shading by using the local controller to satisfy user's habits and energy efficiency targets. The cognitive layer defines the appropriate rules for opening and shading using the decision tree algorithm applied to the data generated by the sensors in order to infer users' preferences. For this research, two prototypes of the window units were installed in two offices of Politecnico di Milano, Italy. The accuracy of this algorithm to classify the users' behaviour and preferences was found to be around 90%, considering an observation interval of two months. [ABSTRACT FROM AUTHOR]

The adoption of the distributed generation paradigm is introducing several changes in the design and operation of modern distribution networks. Modern grid codes are becoming more and more complex, and the adoption of smart protection systems is becoming mandatory. However, the adoption of newer and smarter units is only half of the story. Proper communication networks must be provided as well, and the overall costs may become critical. In this work, the adoption of the Long-Range Wide Area Network (LoRaWAN) technology is suggested as a viable approach to implement the coordination of Interface Protection Systems. A proper communication architecture based on the LoRaWAN Class B technology was proposed and evaluated in order to assess its feasibility for the considered application. A scalability analysis was carried out, by computing the number of devices that can be handled by a single LoRaWAN Gateway (GW) and the maximum expected time of response between a triggering event and the arrival of the related coordination command. The results of the study showed that up to 312 devices can be managed by a single GW, by assuring a maximum response time of 22.95 s. A faster maximum response time of 6.2 s is also possible by reducing the number of managed devices to 12. [ABSTRACT FROM AUTHOR]

In this paper a general model for the estimation of the uncoordinated charging costs of Electric Vehicles (EVs) in the presence of distributed and intermittent generation, and variable electricity tariffs is presented. The proposed method aims at estimating the monthly average cost of uncoordinated charging of a single EV depending on the hour at which the EV is plugged into the EV Supply Equipment (EVSE). The feasibility and relevance of the proposed model is verified by applying the considered cost estimation method to a suitable use case. A single EV charging service offered at a public building equipped with a Photovoltaic (PV) system has been considered as reference case. The proposed model has been applied to the PV production and loads consumption data collected during one year, and the results of the study compared with the Time-Of-Use (TOU) electricity tariff. The application of the proposed model identified noticeable deviations among the computed EV charging costs and the reference TOU profile, with differences up to 40%, depending on the considered month and on the time of charging during the day. It can be concluded that such model could be used to properly detect opportunities of energy savings, and to define dedicated EV price signals that could help to promote the optimal use of distributed energy resources. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) is becoming a reality thanks to Industry 4.0, which requires the Internet connection of as many industrial devices as possible. The sharing and storing of a huge amount of data in the Cloud allows the implementation of new analysis algorithms and the delivery of new “services” with added value. From an economical point of view, several factors can decide the success of Industry 4.0 new services but, among others, the “short latency” can be one of the most interesting, especially in the industrial market that is used to the “real-time” concept. For these reasons, this work proposes an experimental methodology to investigate the impact of quality of service parameters on the communication delay from the production line to the Cloud and vice versa, when gateways with OPC UA (Open Platform Communications Unified Architecture) are used for accessing data directly in the production line. In this work, the feasibility of the proposed test methodology has been demonstrated by means of a use case with a Siemens S7 1500 controller exchanging data with the IBM Bluemix platform. The experimental results show that, thanks to the proposed method, the solutions based on OPC UA for the implementation of industrial IoT gateways can be easily evaluated, compared and optimized. For instance, during the 14-day observation period of the considered use case, the great impact on performance of the Quality of Service parameters emerged. Indeed, the average communication delay from the production line to the Cloud may vary from less than 90 ms to about 300 ms. [ABSTRACT FROM AUTHOR]

The rising concerns about global warming and environmental pollution are increasingly pushing towards the replacement of road vehicles powered by Internal Combustion Engines (ICEs). Electric Vehicles (EVs) are generally considered the best candidates for this transition, however, existing power grids and EV management systems are not yet ready for a large penetration of EVs, and the current opinion of the scientific community is that further research must be done in this field. The so-called Vehicle-to-Grid (V2G) concept plays a relevant role in this scenario by providing the communication capabilities required by advanced control and Demand-Side Management (DSM) strategies. Following this research trend, in this paper the communication requirements for the DSM of EVs in urban environments are discussed, by focusing on the mobile communication among EVs and smart grids. A specific system architecture for the DSM of EVs moving inside urban areas is proposed and discussed in terms of the required data throughput. In addition, the use of a Low-Power Wide-Area Network (LPWAN) solution—the Long-Range Wide Area Network (LoRaWAN) technology—is proposed as a possible alternative to cellular-like solutions, by testing an experimental communication infrastructure in a real environment. The results show that the proposed LPWAN technology is capable to handle an adequate amount of information for the considered application, and that one LoRa base station is able to serve up to 438 EVs per cell, and 1408 EV charging points. [ABSTRACT FROM AUTHOR]