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With the availability of data and the increasing capabilities of data processing tools, many businesses are leveraging historical sales and demand data to implement smart inventory management systems. Demand forecasting is the process of estimating the consumption of products or services for future time periods. It plays an important role in the field of inventory control and Supply Chain, since it enables production and supply planning and therefore can reduce delivery times and optimize Supply Chain decisions. This paper presents an extensive literature review about demand forecasting methods for time-series data. Based on analysis results and findings, a new demand forecasting tool for inventory control is proposed. First, a forecasting pipeline is designed to allow selecting the most accurate demand forecasting method. The validation of the proposed solution is executed on Stock&Buy case study, a growing online retail platform. For this reason, two new methods are proposed: (1) a hybrid method, Comb-TSB, is proposed for intermittent and lumpy demand patterns. Comb- TSB automatically selects the most accurate model among a set of methods. (2) a clustering-based approach (ClustAvg) is proposed to forecast demand for new products which have very few or no sales history data. The evaluation process showed that the proposed tool achieves good forecasting accuracy by making the most appropriate choice while defining the forecasting method to apply for each product selection.

Though they can be traced back to different roots, both smart design and smart systems have to do with the recent developments of artificial intelligence. There are two major questions related to them: (i) What way are smart design and smart systems enabled by artificial narrow, general, or super intelligence? and (ii) How can smart design be used in the realization of smart systems? and How can smart systems contribute to smart designing? A difficulty is that there are no exact definitions for these novel concepts in the literature. The endeavor to analyze the current situation and to answer the above questions stimulated an exploratory research whose first findings are summarized in this paper. Its first part elaborates on a plausible interpretation of the concept of smartness and provides an overview of the characteristics of smart design as a creative problem solving methodology supported by artificial intelligence. The second part exposes the paradigmatic features and system engineering issues of smart systems, which are equipped with application-specific synthetic system knowledge and reasoning mechanisms. The third part presents and elaborates on a conceptual model of AI-based couplings of smart design and smart systems. The couplings may manifest in various concrete forms in real life that are referred to as “connectors” in this paper. The principal types of connectors are exemplified and discussed. It has been found that smart design tends to manifest as a methodology of blue-printing smart systems and that smart systems will be intellectualized the enablers of implementation of smart design. Understanding the affordances of and creating proper connectors between smart design and smart systems need further explorative research.

In this article, design and development of smart systems require cooperation of different technical disciplines. This multidisciplinarity represents a challenge for today's ICT (Information and Communication Technologies) standardization environment. This environment has evolved over the years and today comprises a huge variety of hundreds of Standards Setting Organisations, partly interconnected by a complex network of very diverse types of links and cooperation agreements. Yet, despite this complexity, ICT standardization is still very much monodisciplinary. Moreover, smart systems’ future true ubiquity and the resulting massive societal ramifications imply that societal stakeholders also need to participate in their standardization, thus further complicating the process. Integrating the necessary background information about the current standardization environment with two empirical studies the article sheds some light on the need for, as well as the benefits and issues to be associated with, multidisciplinarity in ICT standards setting. To resolve these issues, the article proposes the deployment of BOMOS, a collection of guidelines that establish a layered set of activities necessary for the management of a standardization process. While this solution may further increase the complexity of the standardization environment, it offers much greater flexibility and caters much better for the multidisciplinary, cross-domain standardization than the current system does.

With the demand for accurately recognizing human actions and environmental situations, multifunctional sensors are essential elements for smart applications in various emerging technologies, such as smart robots, human-machine interface, and wearable electronics. Low-dimensional materials provide fertile soil for multifunction-integrated devices. This review focuses on the multifunctional sensors for mechanical stimulus and environmental information, such as strain, pressure, light, temperature, and gas, which are fabricated from low-dimensional materials. The material characteristics, device architecture, transmission mechanisms, and sensing functions are comprehensively and systematically introduced. Besides multiple sensing functions, the integrated potential ability of supplying energy and expressing and storing information are also demonstrated. Some new process technologies and emerging research areas are highlighted. It is presented that optimization of device structures, appropriate material selection for synergy effect, and application of piezotronics and piezo-phototronics are effective approaches for constructing and improving the performance of multifunctional sensors. Finally, the current challenges and direction of future development are proposed.

Cyber-Physical Systems (CPS) allow us to manipulate objects in the physical world by providing a communication bridge between computation and actuation elements. In the current scheme of things, this sought-after control is marred by limitations inherent in the underlying communication network(s) as well as by the uncertainty found in the physical world. These limitations hamper fine-grained control of elements that may be separated by large-scale distances. In this regard, soft computing is an emerging paradigm that can help to overcome the vulnerabilities, and unreliability of CPS by using techniques including fuzzy systems, neural network, evolutionary computation, probabilistic reasoning and rough sets. In this paper, we present a comprehensive contemporary review of soft computing techniques for CPS dependability modeling, analysis, and improvement. This paper provides an overview of CPS applications, explores the foundations of dependability engineering, and highlights the potential role of soft computing techniques for CPS dependability with various case studies, while identifying common pitfalls and future directions. In addition, this paper provides a comprehensive survey on the use of various soft computing techniques for making CPS dependable., IEEE Access

Blockchain technologies have recently come to the forefront of the research and industrial communities as they bring potential benefits for many industries. This is due to their practical capabilities in solving many issues currently inhibiting further advances in various industrial domains. Securely recording and sharing transactional data, establishing automated and efficient supply chain processes, and enhancing transparency across the whole value chain are some examples of these issues. Blockchain offers an effective way to tackle these issues using distributed, shared, secure, and permissioned transactional ledgers. The employment of blockchain technologies and the possibility of applying them in different situations enables many industrial applications through increased efficiency and security; enhanced traceability and transparency; and reduced costs. In this paper, different industrial application domains where the use of blockchain technologies has been proposed are reviewed. This paper explores the opportunities, benefits, and challenges of incorporating blockchain in different industrial applications. Furthermore, the paper attempts to identify the requirements that support the implementation of blockchain for different industrial applications. The review reveals that several opportunities are available for utilizing blockchain in various industrial sectors; however, there are still some challenges to be addressed to achieve better utilization of this technology.

This study aims to explore the use of force vs. time data obtained from an isometric handgrip test to match a frailty state based on the TFI score. BodyGrip, a novel prototype system, is used for handgrip strength over 10 s time interval tests. A cross-sectional study with a non-probabilistic sample of community-dwelling elderly women was conducted. The force/time data collected from the dominant handgrip strength test, together with the Tilburg Frailty Indicator (TFI) test results, were used to train artificial neural networks. Different models were tested, and the frailty matching of TFI scores reached a minimum accuracy of 75%. Despite the small sample size, the BodyGrip system appears to be a promising tool for exploring new frailty-related features. The adopted strategy foresees ultimately configuring the system to be used as an expedite mode for identifying individuals at risk, allowing an easy, quick, and frequent person-centered care approach. Additionally, it is suitable for following up of the elderly in particular, and it may assume a relevant role in the mitigation of the increase in frailty evolution during and after the imposed isolation of the COVID-19 pandemic. Further use of the system will improve the robustness of the artificial neural network algorithm.

Wound healing is a complex tissue regeneration process involving many changes in multiple physiological parameters. The pH and temperature of a wound site have long been recognized as important biomarkers for assessing wound healing status. For effective wound management, wound dressings integrated with wearable sensors and systems used for continuous monitoring of pH and temperature have received much attention in recent years. Herein, recent advances in the development of wearable pH and temperature sensors and systems based on different sensing mechanisms for wound status monitoring and treatment are comprehensively summarized. Challenges in the areas of sensing performance, infection identification threshold, large-area 3-dimensional detection, and long-term reliable monitoring in current wearable sensors/systems and emerging solutions are emphasized, providing critical insights into the development of wearable sensors and systems for wound healing monitoring and management.

The interactions between humans and unmanned aerial vehicles (UAVs), whose applications are increasing in the civilian field rather than for military purposes, are a popular future research area. Human–UAV interactions are a challenging problem because UAVs move in a three-dimensional space. In this paper, we present an intelligent human–UAV interaction approach in real time based on machine learning using wearable gloves. The proposed approach offers scientific contributions such as a multi-mode command structure, machine-learning-based recognition, task scheduling algorithms, real-time usage, robust and effective use, and high accuracy rates. For this purpose, two wearable smart gloves working in real time were designed. The signal data obtained from the gloves were processed with machine-learning-based methods and classified multi-mode commands were included in the human–UAV interaction process via the interface according to the task scheduling algorithm to facilitate sequential and fast operation. The performance of the proposed approach was verified on a data set created using 25 different hand gestures from 20 different people. In a test using the proposed approach on 49,000 datapoints, process time performance of a few milliseconds was achieved with approximately 98 percent accuracy.

Modern control strategies for district-level heating and cooling supply systems pose a difficult challenge. In order to integrate a wide range of hot and cold sources, these new systems will rely heavily on accumulation and much lower operating temperatures. This means that predictive models advising the control strategy must take into account long-lasting thermal effects but must not be computationally too expensive, because the control would not be possible in practice. This paper presents a simple but powerful systematic approach to reducing the complexity of individual components of such models. It makes it possible to combine human engineering intuition with machine learning and arrive at comprehensive and accurate models. As an example, a simple steady-state heat loss of buried pipes is extended with dynamics observed in a much more complex model. The results show that the process converges quickly toward reasonable solutions. The new auto-generated model performs 5 &times, 104 times faster than its complex equivalent while preserving essentially the same accuracy. This approach has great potential to enhance the development of fast predictive models not just for district heating. Only open-source software was used, while OpenModelica, Python, and FEniCS were predominantly used.

Distributed systems provide smart functionality to everyday objects with the help of wireless sensors using the internet. Since the last decade, the industry is struggling to develop efficient and intelligent protocols to integrate a huge number of smart objects in distributed computing environments. However, the main challenge for smart and distributed system designers lies in the integration of a large number of heterogeneous components for faster, cheaper, and more efficient functionalities. To deal with this issue, practitioners are using edge computing along with server and desktop technology for the development of smart applications by using Service-Oriented Architecture (SOA) where every smart object offers its functionality as a service, enabling other objects to interact with them dynamically. In order to make such a system, researchers have considered context-awareness and Quality of Service (QoS) attributes of device services. However, context modeling is a complicated task since it could include everything around the applications. Moreover, it is also important to consider non-functional interactions that may have an impact on the behavior of the complete system. In this regard, various research dimensions are explored. However, rich context-aware modeling, QoS, user priorities, grouping, and value type direction along with uncertainty are not considered properly while modeling of incomplete or partial domain knowledge during ontology engineering, resulting in low accuracy of results. In this paper, we present a semantic and logic-based formal framework (hybrid) to find the best service among many candidate services by considering the limitations of existing frameworks. Experimental results of the proposed framework show the improvement of the discovered results.

International audience; Enterprises and governments are focusing their efforts on reducing the gap between the physical and the digital world to address various challenges. This digital transformation is also charac-terised by a change of paradigm, based on moving from computer-aided technologies to Smart Systems, which are present in several domains such as Smart Cities, Smart Manufacturing, or Smart Services. In this scenario, the exploration of the conceptualisation of Smart Systems has been addressed in the scientific literature with the objective to understand the characteristics of such systems and the implications of their use. However, there is a lack of a generic view of the concept since most studies focus on specific domains. This view is necessary to provide common ground for the development or implementation of Smart Systems within various domains, specially for those that have not been tackled yet. This work aims at addressing this issue through the exploration and analysis of conceptualisations of Smart Systems and Smartness within the scientific literature. For this purpose, a Systematic Literature Review is performed aiming at gathering papers including definitions of Smart Systems, regardless of the application domain. Text mining processing techniques are applied to obtain insights from the definitions extracted from the collected papers. Considering the findings of the literature review and the analysis of the extracted definitions, we enumerate and describe a set of characteristics of Smart Systems. We also provide a diagrammatic view of the elements of a Smart System and their relationships through a meta-model.

The cyber processing layer of smart systems based on a cognitive dynamic system (CDS) can be a good solution for better decision making and situation understanding in non-Gaussian and nonlinear environments (NGNLE). The NGNLE situation understanding means deciding between certain known situations in NGNLE to understand the current state condition. Here, we report on a cognitive decision-making (CDM) system inspired by the human brain decision-making. The simple low-complexity algorithmic design of the proposed CDM system can make it suitable for real-time applications. A case study of the implementation of the CDS on a long-haul fiber-optic orthogonal frequency division multiplexing (OFDM) link was performed. An improvement in Q-factor of ~7 dB and an enhancement in data rate efficiency ~43% were achieved using the proposed algorithms. Furthermore, an extra 20% data rate enhancement was obtained by guaranteeing to keep the CDM error automatically under the system threshold. The proposed system can be extended as a general software-based platform for brain-inspired decision making in smart systems in the presence of nonlinearity and non-Gaussian characteristics. Therefore, it can easily upgrade the conventional systems to a smart one for autonomic CDM applications.

Yapay zekâ ve buna bağlı alt çalışma konularındaki bilimsel yöntemler, neredeyse her alanda yaygın biçimde kullanılmaktadır. Özellikle savunma sanayi ve tarım bu alanların en cazip uygulama merkezlerini oluşturmaktadırlar. Tarımda veya bahçelerde kullanılmak üzere daha önce geliştirmiş olduğumuz sulama ve ilaçlama prototip mobil robotumuzun, nesne (ağaç veya değil) tanı ma kısmında yeterli düzeyde başarılı neticeler alınamamıştı. Bu çalışmada, bu mobil robot ile gerçek zamanlı görüntüler üzerinde karmaşık (asimetrik) yapıya sahip bitki nesnelerinin tanınması ve bu tanınma sonucuna göre ilaçlama mekanizmasının tetiklenme sinin başarımını artırılmıştı r. Bitki nesnelerinin tanınması amacı ile Viola Jones nesne tanıma algoritması kullanılmıştır. Bu algoritma ile yapılan eğitim ve parametrik düzenlemeler sonucunda elde edilen modelin 5 farklı senaryo yolu üzerinde sınanması sonucunda, m obil sulama/ilaçlam a robotunun görme yetisi ve bitki nesnelerini tanıma başarısının yaklaşık olarak %96.5 gibi oldukça yüksek bir düzeye çıkarıldığı görülmüştür. Ayrıca, ihtiyaç dolayısı ile uygulanan, herhangi bir nesneye ait görüntü eğitim setinin oluşturulmas ına yönelik h ızlı ve kolay bir yöntemin detayları makalede açıklanmıştır. Scientific methods in artificial intelligence and related sub-studies are widely used in almost every field. Defense industry and agriculture are the most attractive application areas of these fields. Sufficient successful results could not be obtained in the object (tree or not) recognition part of our irrigation and spraying prototype mobile robot, which we developed earlier for use in agriculture or gardens. In this study, with this mobile robot, the performance of the identification of complex (asymmetric) plant objects on real-time images and the triggering of the spraying mechanism according to this recognition result has been increased. Viola-Jones object capture algorithm was used to identify plant objects. As a result of the testing the model obtained through the training and parametric arrangements made to this algorithm on 5 different scenario paths, it has been observed that the success of recognizing plant objects and vision ability of the mobile irrigation/medication robot has been increased to a very high level of approximately 96,5%. In addition, the details of a quick and easy method for creating an image training set for any object that is applied due to need are explained in the article.

The article is devoted to a relevant and broad topic of today – Artificial Intelligence. The rapid development of information technologies, the progress in data collection methods in particular, present new opportunities for the implementation of various technologies. The article is a brief overview of different areas of life and fields of activity in which Artificial Intelligence systems and technologies have already been embedded. Данная статья посвящена актуальной и обширной теме искусственного интеллекта. Стремительное развитие информационных технологий, в частности прогресс в методах сбора данных, дарит много новых возможностей использования подобных новшеств. Статья является кратким обзором различных областей жизни и сфер деятельности, в которые уже были внедрены технологии и системы, связанные с искусственным интеллектом.

The subject of research is the activities of state institutions aimed at the development of digital technologies in the economy and other spheres of society life, particularly intelligent technologies for decision support and making. The purpose of research was to determine the prospects for using intelligent technologies in the federal risk management system. In the context of the Quality of Life problem solution, an approach to creating a factor model of the problem domain of ensuring security and sustainable socio-economic development is shown. The model identifies 12 spheres combining 72 interrelated factors. Problems and threats to be solved and overcome in the process of development and wide promotion of intelligent information technologies in the practice of managing socio-economic systems are considered. It is concluded that intelligent systems used for managing socio-economic systems must undergo strict verification of not only their adequacy but also to expose possible user manipulation devices embedded in them.

In today’s technology, when costs of time, energy and human resources are considered, efficient use of resources provides significant advantages over many aspects. In light of this, role of building automation systems, which are a part of smart cities, become even more important. At the very core of building automation systems there lies the efficient use of resources and systems for providing comfortable living situations. With the advancement in network technology, systems can be programmed smartly and any malfunctions on the systems can be detected and fixed remotely. In addition to that, all data gathered during this process can be analyzed to create machine learning solutions for a system to control and program itself. In this work, we pulled the sensor data and developed an interface to do analysis. Our aim is to understand how the system behaves. This interface will be the basis of our work on developing machine learning algorithms to predict system behaviour for programming the system for energy.

Analog components are fundamental blocks of smart systems, as they allow a tight interaction with the environment, in terms of both sensing/actuation and communication. This impacts on the design of the overall system, and mainly on the validation phase, that thus requires the joint simulation of digital and analog aspects. In this scenario, this paper proposes the automatic conversion of analog models to C++-based languages, to remove the overhead of co-simulation with traditional virtual platform tools. The proposed methodology allows to convert a given analog description to either: 1) a fully equivalent description or 2) an abstract representation for faster simulation which models only the aspects of interest. Effectiveness and correctness have been proved on a number of case studies that highlight the effectiveness and potentiality of the proposed methodology.

Brain-inspired intelligence using the cognitive dynamic system (CDS) concept is proposed to control the quality-of-service (QoS) over a long-haul fiber-optic link that is nonlinear and with non-Gaussian channel noise. Digital techniques such as digital-back-propagation (DBP) assume that the fiber optic link parameters, such as loss, dispersion, and nonlinear coefficients, are known at the receiver. However, the proposed CDS does not need to know about the fiber optic link physical parameters, and it can improve the bit error rate (BER) or enhance the data rate based on information extracted from the fiber optic link. The information extraction (Bayesian statistical modeling) using intelligent perception processing on the received data, or using the previously extracted models in the model library, is carried out to estimate the transmitted data in the receiver. Then, the BER is sent to the executive through the main feedback channel and the executive produces actions on the physical system/signal to ensure that the BER is continuously under the forward-error-correction (FEC) threshold. Therefore, the proposed CDS is an intelligent and adaptive system that can mitigate disturbance in the fiber optic link (especially in an optical network) using prediction in the perceptor and/or doing proper actions in the executive based on BER and the internal reward. A simplified CDS was implemented for nonlinear fiber optic systems based on orthogonal frequency division multiplexing (OFDM) to show how the proposed CDS can bring noticeable improvement in the system&rsquo, s performance. As a result, enhancement of the data rate by 12.5% and the Q-factor improvement of 2.74 dB were achieved in comparison to the conventional system (i.e., the system without smart brain).

Energy consumption is steadily increasing in the Kingdom of Saudi Arabia (KSA), which imposes continuous strains on the electrical load. Furthermore, consumption rationalization measures do not seem to improve the situation in any way. Therefore, the implementation of energy saving policies become an urgent need. This paper targets developing a smart energy-saving framework for integrating new advanced technologies and conventional Air Conditioning (AC) systems to achieve a comfortable environment, optimum energy efficiency and profitability. In this paper, a three-stage smart control framework, which allows controlling room temperature according to the user&rsquo, s preferences, is implemented. The first stage is a user identification process. In the second stage, a Petri Nets (PN) model monitors users and sends their preferred temperatures to the third stage. A PID controller is implemented in the third stage to regulate room temperatures. The interconnected sensing and actuating devices in this smart environment are configured to provide users with comfort and energy saving functionality. Experimental results show the good performances and features of the proposed approach. The proposed smart framework reduces the energy consumption of the current ON/OFF controller ( 219 . 09 kW) by a significant amount which reaches ( 116 . 58 kW) by ratio about 46 . 79 % . Reducing energy consumption is one of these important features in addition to system reactivity and user comfort.

The rapid rise and implementation of Smart Systems (i.e., multi-functional observation and platform systems that depict settings and/or identify situations or features of interest, often in real-time) has inversely paralleled and readily exposed the reduced capacity of human and societal systems to effectively respond to environmental hazards. This overarching review and essay explores the complex set of interactions found among Smart, Societal, and Environmental Systems. The resulting rise in the poorly performing response solutions to environmental hazards that has occurred despite best practices, detailed forecast information, and the use and application of real-time in situ observational platforms are considered. The application of Smart Systems, relevant architectures, and ever-increasing numbers of applications and tools development by individuals as they interact with Smart Systems offers a means to ameliorate and resolve confounding found among all of the interdependent Systems. The interactions of human systems with environmental hazards further expose society’s complex operational vulnerabilities and gaps in response to such threats. An examination of decision-making, the auto-reactive nature of responses before, during, and after environmental hazards; and the lack of scalability and comparability are presented with regard to the prospects of applying probabilistic methods, cross-scale time and space domains; anticipated impacts, and the need to account for multimodal actions and reactions—including psycho-social contributions. Assimilation of these concepts and principles in Smart System architectures, applications, and tools is essential to ensure future viability and functionalities with regard to environmental hazards and to produce an effective set of societal engagement responses. Achieving the promise of Smart Systems relative to environmental hazards will require an extensive transdisciplinary approach to tie psycho-social behaviors directly with non-human components and systems in order to close actionable gaps in response. Pathways to achieve a more comprehensive understanding are given for consideration by the wide diversity of disciplines necessary to move forward in Smart Systems as tied with the societal response to environmental hazards.

International audience; Industrial monitoring environments have evolved from single monolithic systems to widely distributed heterogeneous systems. These include the Internet of Things (IoT), Industrial IoT, Cyber-Physical Systems, and Enterprise Application. One of the key challenges is the integration of heterogeneous systems and data exchange interoperability. In the industrial smart gas project, in which this work takes place, current standard-based or middleware solutions are not sufficient to handle these issues and often require specific ad hoc developments. This paper proposes a generic, modular, and extensible interoperability architecture based on modeling principles. We provide a free software implementation and illustrate the approach on industrial use cases. Some criteria are then proposed for a first qualitative evaluation.

[EN] This work proposes a persuasion model based on argumentation theory and users' characteristics for improving the use of resources in bike sharing systems, fostering the use of the bicycles and thus contributing to greater energy sustainability by reducing the use of carbon-based fuels. More specifically, it aims to achieve a balanced network of pick-up and drop-off stations in urban areas with the help of the users, thus reducing the dedicated management trucks that redistribute bikes among stations. The proposal aims to persuade users to choose different routes from the shortest route between a start and an end location. This persuasion is carried out when it is not possible to park the bike in the desired station due to the lack of parking slots, or when the user is highly influenceable. Differently to other works, instead of employing a single criteria to recommend alternative stations, the proposed system can incorporate a variety of criteria. This result is achieved by providing a defeasible logic-based persuasion engine that is capable of aggregating the results from multiple recommendation rules. The proposed framework is showcased with an example scenario of a bike sharing system., This work was supported by the projects TIN2015-65515-C4-1-R and TIN2017-89156-R of the Spanish government, and by the grant program for the recruitment of doctors for the Spanish system of science and technology (PAID-10-14) of the Universitat Politècnica de València.

We live in complex times in the health, social, political, and energy spheres, and we must be aware of and implement new trends in intelligent social health systems powered by the Internet of Things (IoT). Sustainable development, energy efficiency, and public health are interrelated parameters that can transform a system or an environment for the benefit of people and the planet. The integration of sensors and smart devices should promote energy efficiency and ensure that sustainable development goals are met. This work is carried out according to a mixed approach, with a literature review and an analysis of the impact of the Sustainable Development Goals on the applications of the Internet of Things and smart systems. In the analysis of results, the following questions are answered about these systems and applications: (a) Are IoT applications key to the improvement of people’s health and the environment? (b) Are there research and case studies implemented in cities or territories that demonstrate the effectiveness of IoT applications and their benefits to public health? (c) What sustainable development indicators and objectives can be assessed in the applications and projects analyzed?

Today’s best-effort (BE) Internet of Things (IoT) faces challenges in providing the end-to-end-performance, security, and energy efficiency needed for the Smart Systems of the 21st century. These future smart systems will include smart cities , smart transportation systems , and smart manufacturing . This paper surveys the security weaknesses of the BE IoT. The BE-IoT cannot be partitioned into distinct interference-free virtual networks , which compromises performance, cyber-security, and energy efficiency. The design of a secure deterministic industrial-tactile IoT core network, which can embed millions of distinct secure deterministic virtual networks (SD-VNs) in layer 2, is then presented. Deterministic communications, combined with low-jitter scheduling, offers several benefits: 1) the removal of all congestion, interference, and DOS attacks; 2) a significant reduction in IoT router buffer sizes; 3) a significant reduction in IoT energy use; 4) a reduction of end-to-end IoT delays to the speed of light in fiber; and 5) deterministic packet-switches are relatively easy to synthesize using FPGA technologies. These benefits apply to optical and 5G wireless networks. Future smart systems can reserve their own congestion-free SD-VNs in layer 2 to manage their traffic, with significantly improved performance, security, and energy efficiency. A speed-of-light deterministic IoT core network can transform cloud services in the 21st century by exploiting a new technology: FPGAs combined with silicon photonics transceivers to achieve terabits/second of optical bandwidth. To illustrate the transformational potential, Big Data green cloud computing over a secure deterministic IoT spanning the European Union is explored.

Currently, a high percentage of the world’s population lives in urban areas, and this proportion will increase in the coming decades. In this context, indoor positioning systems (IPSs) have been a topic of great interest for researchers. On the other hand, Visible Light Communication (VLC) systems have advantages over RF technologies; for instance, they do not need satellite signals or the absence of electromagnetic interference to achieve positioning. Nowadays, in the context of Indoor Positioning (IPS), Visible Light Positioning (VLP) systems have become a strong alternative to RF-based systems, allowing the reduction in costs and time to market. This paper shows a low cost VLP solution for indoor systems. This includes multiple programmable beacons and a receiver which can be plugged to a smartphone running a specific app. The position information will be quickly and securely available through the interchange between the receiver and any configurable LED-beacon which is strategically disposed in an area. The implementation is simple, inexpensive, and no direct communication with any data server is required.

E-commerce is becoming more and more the main instrument for selling goods to the mass market. This led to a growing interest in algorithms and techniques able to predict products future prices, since they allow us to define smart systems able to improve the quality of life by suggesting more affordable goods and services. The joint use of time series, reputation and sentiment analysis clearly represents one important approach to this research issue. In this paper we present Price Probe, a suite of software tools developed to perform forecasting on products&rsquo, prices. Its primary aim is to predict the future price trend of products generating a customized forecast through the exploitation of autoregressive integrated moving average (ARIMA) model. We experimented the effectiveness of the proposed approach on one of the biggest E-commerce infrastructure in the world: Amazon. We used specific APIs and dedicated crawlers to extract and collect information about products and their related prices over time and, moreover, we extracted information from social media and Google Trends that we used as exogenous features for the ARIMA model. We fine-estimated ARIMA&rsquo, s parameters and tried the different combinations of the exogenous features and noticed through experimental analysis that the presence of Google Trends information significantly improved the predictions.

International audience; Smart and sensing systems represent a significant research theme that has motivated a number of research initiatives around the world. Smart systems typically consist of different components including sensors for signal acquisition, communication units for data transmission between components, control and management units for decision-making, and actuators to perform the appropriate actions. They have impacted applications in many fields such as manufacturing, healthcare, energy, environment, logistic, defence, monitoring, and mobility. As the complexity of such systems continue to grow, the challenge of developing integrated smart and sensing systems has surpassed the design complexity of their individual components. A smart and sensing system may include a large number of heterogeneous components, subsystems, smart and intelligent sensors, and may combine several correlated functionalities. Thus, the main issue of developing smart and sensing systems lies in the complexity to integrate and to manage these different components, technologies, and goals across a wide spectrum. In recent years, the emergence of IoT has amplified the capacity of sensing the world through a network of connected devices using the existing network infrastructure. Grouping together smart and sensing systems in an IoT setting to form large-scale distributed cyber-physical systems has tremendous potential in bringing smart systems to many application domains.This Journal’s Special Issue (SI) aims to promote relevant researches related to strategic views, problems, trends, solutions, and applications of IoT for Smart and Sensing Systems. This represents a more specialized area of interest than the wide application of IoT. The issue focuses more specifically on how industrial IoT contributes to the development of smart and sensing systems.

One of the aspects of the global trend in the development of Smart systems is considered in the paper, namely those solutions of the leading manufacturers of electrical household appliances that relate to the implementation of additional intelligent functions to devices in demand by the society - Smart Diagnosis, Smart Grid-ready, Smart Manager, Smart Access, Smart Adapt. It is emphasized that some tasks related to the energy efficiency of devices, the accuracy of testing tools, which should be used as a basis for the Smart System, remained unresolved. So, the built-in additional sensors and chips of "smart" subsystems require the application of additional measures aimed at reducing the power consumption of the device (preferably twice to restore the balance). On the example of the developed innovative electric motor of domestic refrigerator hermetic compressor drive, the existence of the possibilities for a significant increase in its efficiency has been proved. Since intellectual features of Smart System in its Smart Diagnosis dialog box should be based on monitoring data on power efficiency parameters of the device, a new concept for the development of an electric energy counter is proposed.

With the evolution of the Internet of things and smart cities, a new trend of the Internet of simulation has emerged to utilise the technologies of cloud, edge, fog computing, and high-performance computing for design and analysis of complex cyber-physical systems using simulation. These technologies although being applied to the domains of big data and deep learning are not adequate to cope with the scale and complexity of emerging connected, smart, and autonomous systems. This study explores the existing state-of-the-art in automating, augmenting, and integrating systems across the domains of smart cities, autonomous vehicles, energy efficiency, smart manufacturing in Industry 4.0, and healthcare. This is expanded to look at existing computational infrastructure and how it can be used to support these applications. A detailed review is presented of advances in approaches providing and supporting intelligence as a service. Finally, some of the remaining challenges due to the explosion of data streams; issues of safety and security; and others related to big data, a model of reality, augmentation of systems, and computation are examined.

Lighting is no longer just turning on a lamp. Solid State Lighting allows reaching high energy savings and visual perception, but the path to success was not easy: in the early years when LED were available on the market, several concerns slowed down their widespread implementation. The large spread of Lighting Systems based on LED arrived only when products quality increased thanks also to the development of new metrology paradigms able to certify LED performances peculiarities. Currently LED promises to widespread in every lighting application increasing not only energy savings, but night-time safety, health and wellbeing thanks to lighting systems based on connectivity and smart controls, but new design paradigms and metric headed to new objectives are necessary, therefore there will be no need to measure only energetic and light performances. We are moving toward an approach more sensitive to Human Needs: energy savings and light efficiency are of no more the only interest to focus on, we need to measure performance of a lighting system in terms of not only SI quantities. We need now a consolidated metrology and design approach to Smart System for complexity and suitable to compare lighting quality in term of human perception and wellbeing.

Part 15: Sustainability Improvements Through CNs; International audience; The vast diversity of current sensing devices and mechanisms enable the creation of data-rich environments which gives the opportunity to create smart systems. The smartness of systems must transcend individual interests and fulfill collective aspirations and environmental needs. Therefore, new products, processes, enterprises, communities and any kind of systems must be envisioned as Sensing, Smart and Sustainable (S3). This paper discusses the trends and challenges for S3 applied to everything. Furthermore, it suggests that the knowledge produced in the field of collaborative networks might be used to leverage the S3 capabilities.

Smart systems are characterised by their ability to analyse measured data in live and to react to changes according to expert rules. Therefore, such systems exploit appropriate data models together with actions, triggered by domain-related conditions. The challenge at hand is that smart systems usually need to process thousands of updates to detect which rules need to be triggered, often even on restricted hardware like a Raspberry Pi. Despite various approaches have been investigated to efficiently check conditions on data models, they either assume to fit into main memory or rely on high latency persistence storage systems that severely damage the reactivity of smart systems. To tackle this challenge, we propose a novel composition process, which weaves executable rules into a data model with lazy loading abilities. We quantitatively show, on a smart building case study, that our approach can handle, at low latency, big sets of rules on top of large-scale data models on restricted hardware., Comment: pre-print version, published in the proceedings of MOMO-17 Workshop

Modern smart systems are usually built by implementing SW functionalities executed on HW platforms composed of both digital and analog components. Validation is mainly implemented through simulation of the functional behavior of the entire smart system modeled by a Virtual Platform. It is thus crucial to achieve fast mixed-signal simulation by removing unnecessary overhead due to synchronization between multiple tools and unimportant details. This work proposes a methodology to abstract mixed-signal systems, by integrating digital and analog components in a homogeneous virtual platform model for efficient simulation. Two main contributions are provided: 1) an automatic abstraction technique for analog components, allowing to preserve only the details meaningful for the functional behavior of the entire platform by moving complexity from simulation to generation time and 2) a novel scheduling technique that exploits temporal decoupling and synchronization of digital and analog processes, to simulate them together in a homogeneous model.

Part 3: Systems Analysis; International audience; The Internet-of-Things (IoT) is today a reality, and Smart Systems have taken advantage of this to improve its own sense, act and control capabilities. IoT is a highly heterogeneous environment composed by a vast number of “things” (sensors, smart objects, etc.). These “things” are based on hardware platforms which can differ widely since manufactures are being capable of develop new devices every day to tackle different application domains. Consequently, a problem emerges regarding which will be the suitable, proper hardware solution for an IoT deployment. Make a right decision is probably one of the toughest challenges for science and technology managers. This work proposes a novel methodology to analyze a set of hardware alternatives based on user’s multi-criteria requirements, and advice on the more suitable hardware solution for a specific situation. For proof-of-concept it is used different Arduino boards as hardware alternatives, in which user requirements are based on hardware features. This methodology foresees its use during the development of Smart Systems (e.g.: Transportation, Healthcare) to optimize the selection of hardware platforms.

In this review, we discuss the role of sensor analytics point solutions (SNAPS), a reduced complexity machine-assisted decision support tool. We summarize the approaches used for mobile phone-based chemical/biological sensors, including general hardware and software requirements for signal transduction and acquisition. We introduce SNAPS, part of a platform approach to converge sensor data and analytics. The platform is designed to consist of a portfolio of modular tools which may lend itself to dynamic composability by enabling context-specific selection of relevant units, resulting in case-based working modules. SNAPS is an element of this platform where data analytics, statistical characterization and algorithms may be delivered to the data either via embedded systems in devices, or sourced, in near real-time, from mist, fog or cloud computing resources. Convergence of the physical systems with the cyber components paves the path for SNAPS to progress to higher levels of artificial reasoning tools (ART) and emerge as data-informed decision support, as a service for general societal needs. Proof of concept examples of SNAPS are demonstrated both for quantitative data and qualitative data, each operated using a mobile device (smartphone or tablet) for data acquisition and analytics. We discuss the challenges and opportunities for SNAPS, centered around the value to users/stakeholders and the key performance indicators users may find helpful, for these types of machine-assisted tools.

Intelligent tire concept constitutes one of the approaches to increase the accuracy of active safety systems in vehicle technology. The possibility of detecting tire&ndash, road interactions instantaneously has made these systems one of the most important research areas in automotive engineering. This study introduces the use of cost-effective flex and polyvinylidene fluoride strain sensors to estimate some dynamic tire features in free-rolling and real working conditions. The proposed solution combines a microcontroller-based readout circuit for the two sensors with a wireless data transmission system. A suitable prototype was realized and first experimental tests were conducted, in the laboratory as well as on the road. The energy consumption of the wireless monitoring system was optimized. Simulated and experimental results validate the proposed solution.

International audience; Distributed systems spread widely in industrial environments. One of the key challenges is the exchange and the aggregation of data between these sys-tems. Although standards play an important role to solve data interoperability is-sues between systems, these standards do not completely address existing indus-trial problems. In fact, it is not granted to have an industrial environment that complies with a unique standard; therefore, ad-hoc solutions are used to solve this issue. In this article, the authors propose a generic architecture to address the in-teroperability between systems. This architecture is developed based on model-based techniques and principles. Besides, it reduces the need for human interven-tion and time by developing once and reusing the building blocks of the architec-ture. Finally, the architecture is described in its application to a case study.

Smartness in contemporary society implies the use of massive data to improve the experience of people with connected services and products. The use of big data to collect information about people’s behaviours opens a new concept of “user-centred design” where users are remotely monitored, observed and profiled. In this paradigm, users are considered as sources of information and their participation in the design process is limited to a role of data generators. There is a need to identify methodologies that actively involve people and communities at the core of ecosystems of interconnected products and services. Our contribution to designing for social innovation in ecosystems relies on developing new methods and approaches to transform data-driven design using a participatory and co-creative data-enabled design approach. To this end, we present one of the methods we have developed to design “smart” systems called Experiential Design Landscapes (EDL), and two sample projects, Social Stairs and [Y]our Perspective. Social Stairs faces the topic of behaviour change mediated by sensing technologies. [Y]our Perspective is a social platform to sustain processes of deliberative democracy. Both projects exemplify our approach to data-enabled design as a social proactive participatory design approach.

Smart systems are miniaturized devices integrating computation, communication, sensing and actuation. As such, their design can not focus solely on functional behavior, but it must rather take into account different extra-functional concerns, such as power consumption or reliability. Any smart system can thus be modeled through a number of views, each focusing on a specific concern. Such views may exchange information, and they must thus be simulated simultaneously to reproduce mutual influence of the corresponding concerns. This paper shows how the IP-XACT standard, with some necessary extensions, can effectively support this simultaneous simulation. The extended IP-XACT descriptions allow to model extra-functional properties with a homogeneous format, defined by analysing requirements and characteristic of three main concerns, i.e., power, temperature and reliability. The IP-XACT descriptions are then used to automatically generate a skeleton of the simulation infrastructure in SystemC. The skeleton can be easily populated with models available in the literature, thus reaching simultaneous simulation of multiple concerns.

The paper presents a methodology for the automatic abstraction of analog magnetic components to achieve efficient simulation of complex smart system virtual platform. We aim at facilitating the validation process by allowing the simulation of both functional and extra-functional behaviors of the system with an unified simulation environment. The approach employs a process of simplification which removes unnecessary details of the analog model, enabling a much faster simulation.

International audience; This paper shows that context-aware applications commonly make implicit assumptions about a sensor infrastructure. Because context-awareness critically relies on these assumptions, the developer typically need to ensure their validity by encoding them in the application code, polluting it with non-functional concerns. This defensive programming approach can be avoided by formulating these assumptions aside from the application, thus factorizing them as an explicit model of the sensor infrastructure. This model can be expressed as a set of rules and can be checked automatically and continuously to ensure the reliability of a sensor infrastructure, both at installation time and during normal functioning. The usefulness of our approach is demonstrated in the domain of assisted living for seniors. We applied it to sensor data collected in the context of a 9-month field study of an assisted living platform, deployed at the home of 24 seniors. We show that several kinds of sensor malfunctions could have been identified upon their occurrence, thanks for our continuous checking, and resolved.

This chapter presents two case studies showing how the proposed approach applies to smart system design and optimization. The former is the virtual prototyping platform built for a laser pico-projector actuator, where MEMS, analog and digital components are simulated with the aim of optimizing the resulting image quality by means of firmware tuning. The latter, in the context of wearable equipment for inertial body motion reconstruction, deals with the modeling of an inertial sensor node, supporting system accuracy evaluation and sensor fusion enhancement. Finally, the Open-Source Test Case (OSTC) is described, showing a complete modeling and simulation flow on a publicly available design.

This book-presents new methods and tools for the integration and simulation of smart devices. The design approach described in this book explicitly accounts for integration of Smart Systems components and subsystems as a specific constraint. It includes methodologies and EDA tools to enable multi-disciplinary and multi-scale modeling and design, simulation of multi-domain systems, subsystems and components at all levels of abstraction, system integration and exploration for optimization of functional and non-functional metrics. By covering theoretical and practical aspects of smart device design, this book targets people who are working and studying on hardware/software modelling, component integration and simulation under different positions (system integrators, designers, developers, researchers, teachers, students etc.). In particular, it is a good introduction to people who have interest in managing heterogeneous components in an efficient and effective way on different domains and different abstraction levels. People active in smart device development can understand both the current status of practice and future research directions. Provides a comprehensive overview of smart systems design, focusing on design challenges and cutting-edge solutions; Enables development of a co-simulation and co-design environment that accounts for the peculiarities of the basic subsystems and components to be integrated; Describes development of modeling and design techniques, methods and tools that enable multi-domain simulation and optimization at various levels of abstraction and across different technological domains.

This paper introduces an artificial human joint working as testing machine for the experimental characterization of piezoelectric transducers for wearable applications. The diffusion of portable medical devices and low-power communication systems for telemedicine and telehealth is leading to the development of self-powered diagnostic systems supplied by small wearable generators. Laboratory tests are needed before the validation on human body for instance, to measure electric outputs, material properties and transducers reliability, as well as dedicated test benches must be designed and built. This work contributes to define experimental methodologies and test benches for the characterization of materials and electro-mechanical response of piezoelectric transducers. The proposed testing machine contributes to improve the effectiveness of harvesters design by providing preliminary data about performances, endurance and reliability. The same data are not easy to obtain from direct tests on the body due to the long test duration and the high repeatability and accuracy in imposing the movements. Additionally, the managing and control of multiple testing parameters is also needed, e.g. in design-of-experiments approaches. The functionality of the testing machine, which has been previously designed and built, has been proved with some qualitative measurements on piezo foil transducers coated with polymer.

This paper proposes a meet-in-the-middle approach for the modeling and simulation of heterogeneous systems. The starting point is a set of heterogeneous models, developed by adopting the designer's favorite design language and formalism. The methodology exploits automatic translation, abstraction and integration flows to generate a single homogeneous system-level description, that allows fast system simulation. The approach is supported by two novel design domain/abstraction level taxonomies, that generalize a state-of the art taxonomy [1] to identify what characteristics would allow efficient system-level simulation, together with the transformations that must be applied to the starting model to achieve them. The advantage of the approach on system design and prototyping is particularly evident on any kind of highly heterogeneous systems, such as smart systems. The overall automatic flow has been implemented and applied to an open-source case study to measure the performance of each identified abstraction level, and the impact of the proposed methodology.

People have a great variety in their needs. There is a great demand for personalized services, especially those interacting with their environment. In this paper, we propose a norm based approach for personalizing smart environments that constraint user requirements by means of non functional requirements expressed in terms of permissions, obligations or prohibitions.

While the most characteristic feature of a smart system is its capability of sensing a set of environmental quantities and actuating appropriate actions in response to those signals, it is obvious that a significant part of its functional operations is involved with the elaboration of the information carried by the signals [14]. This elaboration is usually done after converting the analog, asynchronous environmental signals into the digital domain.