Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives

Consumer's privacy is a main concern in Smart Grids (SGs) due to the sensitivity of energy data, particularly when used to train machine learning models for different services. These data-driven models often require huge amounts of data to achieve acceptable performance leading in most cases to risks of privacy leakage. By pushing the training to the edge, Federated Learning (FL) offers a good compromise between privacy preservation and the predictive performance of these models. The current paper presents an overview of FL applications in SGs while discussing their advantages and drawbacks, mainly in load forecasting, electric vehicles, fault diagnoses, load disaggregation and renewable energies. In addition, an analysis of main design trends and possible taxonomies is provided considering data partitioning, the communication topology, and security mechanisms. Towards the end, an overview of main challenges facing this technology and potential future directions is presented

Gaussian Process models for ubiquitous user comfort preference sampling; global priors, active sampling and outlier rejection.

This paper presents a ubiquitous thermal comfort preference learning study in a noisy environment. We introduce Gaussian Process models into this field and show they are ideal, allowing rejection of outliers, deadband samples, and produce excellent estimates of a users preference function. In addition, informative combinations of users preferences becomes possible, some of which demonstrate well defined maxima ideal for control signals. Interestingly, while those users studied have differing preferences, their hyperparameters are concentrated allowing priors for new users. In addition, we present an active learning algorithm which estimates when to poll users to maximise the information returned

IoT and Sensor Networks in Industry and Society

The exponential progress of Information and Communication Technology (ICT) is one of the main elements that fueled the acceleration of the globalization pace. Internet of Things (IoT), Artificial Intelligence (AI) and big data analytics are some of the key players of the digital transformation that is affecting every aspect of human's daily life, from environmental monitoring to healthcare systems, from production processes to social interactions. In less than 20 years, people's everyday life has been revolutionized, and concepts such as Smart Home, Smart Grid and Smart City have become familiar also to non-technical users. The integration of embedded systems, ubiquitous Internet access, and Machine-to-Machine (M2M) communications have paved the way for paradigms such as IoT and Cyber Physical Systems (CPS) to be also introduced in high-requirement environments such as those related to industrial processes, under the forms of Industrial Internet of Things (IIoT or I2oT) and Cyber-Physical Production Systems (CPPS). As a consequence, in 2011 the German High-Tech Strategy 2020 Action Plan for Germany first envisioned the concept of Industry 4.0, which is rapidly reshaping traditional industrial processes. The term refers to the promise to be the fourth industrial revolution. Indeed, the first industrial revolution was triggered by water and steam power. Electricity and assembly lines enabled mass production in the second industrial revolution. In the third industrial revolution, the introduction of control automation and Programmable Logic Controllers (PLCs) gave a boost to factory production. As opposed to the previous revolutions, Industry 4.0 takes advantage of Internet access, M2M communications, and deep learning not only to improve production efficiency but also to enable the so-called mass customization, i.e. the mass production of personalized products by means of modularized product design and flexible processes. Less than five years later, in January 2016, the Japanese 5th Science and Technology Basic Plan took a further step by introducing the concept of Super Smart Society or Society 5.0. According to this vision, in the upcoming future, scientific and technological innovation will guide our society into the next social revolution after the hunter-gatherer, agrarian, industrial, and information eras, which respectively represented the previous social revolutions. Society 5.0 is a human-centered society that fosters the simultaneous achievement of economic, environmental and social objectives, to ensure a high quality of life to all citizens. This information-enabled revolution aims to tackle today’s major challenges such as an ageing population, social inequalities, depopulation and constraints related to energy and the environment. Accordingly, the citizens will be experiencing impressive transformations into every aspect of their daily lives. This book offers an insight into the key technologies that are going to shape the future of industry and society. It is subdivided into five parts: the I Part presents a horizontal view of the main enabling technologies, whereas the II-V Parts offer a vertical perspective on four different environments. The I Part, dedicated to IoT and Sensor Network architectures, encompasses three Chapters. In Chapter 1, Peruzzi and Pozzebon analyse the literature on the subject of energy harvesting solutions for IoT monitoring systems and architectures based on Low-Power Wireless Area Networks (LPWAN). The Chapter does not limit the discussion to Long Range Wise Area Network (LoRaWAN), SigFox and Narrowband-IoT (NB-IoT) communication protocols, but it also includes other relevant solutions such as DASH7 and Long Term Evolution MAchine Type Communication (LTE-M). In Chapter 2, Hussein et al. discuss the development of an Internet of Things message protocol that supports multi-topic messaging. The Chapter further presents the implementation of a platform, which integrates the proposed communication protocol, based on Real Time Operating System. In Chapter 3, Li et al. investigate the heterogeneous task scheduling problem for data-intensive scenarios, to reduce the global task execution time, and consequently reducing data centers' energy consumption. The proposed approach aims to maximize the efficiency by comparing the cost between remote task execution and data migration. The II Part is dedicated to Industry 4.0, and includes two Chapters. In Chapter 4, Grecuccio et al. propose a solution to integrate IoT devices by leveraging a blockchain-enabled gateway based on Ethereum, so that they do not need to rely on centralized intermediaries and third-party services. As it is better explained in the paper, where the performance is evaluated in a food-chain traceability application, this solution is particularly beneficial in Industry 4.0 domains. Chapter 5, by De Fazio et al., addresses the issue of safety in workplaces by presenting a smart garment that integrates several low-power sensors to monitor environmental and biophysical parameters. This enables the detection of dangerous situations, so as to prevent or at least reduce the consequences of workers accidents. The III Part is made of two Chapters based on the topic of Smart Buildings. In Chapter 6, Petroșanu et al. review the literature about recent developments in the smart building sector, related to the use of supervised and unsupervised machine learning models of sensory data. The Chapter poses particular attention on enhanced sensing, energy efficiency, and optimal building management. In Chapter 7, Oh examines how much the education of prosumers about their energy consumption habits affects power consumption reduction and encourages energy conservation, sustainable living, and behavioral change, in residential environments. In this Chapter, energy consumption monitoring is made possible thanks to the use of smart plugs. Smart Transport is the subject of the IV Part, including three Chapters. In Chapter 8, Roveri et al. propose an approach that leverages the small world theory to control swarms of vehicles connected through Vehicle-to-Vehicle (V2V) communication protocols. Indeed, considering a queue dominated by short-range car-following dynamics, the Chapter demonstrates that safety and security are increased by the introduction of a few selected random long-range communications. In Chapter 9, Nitti et al. present a real time system to observe and analyze public transport passengers' mobility by tracking them throughout their journey on public transport vehicles. The system is based on the detection of the active Wi-Fi interfaces, through the analysis of Wi-Fi probe requests. In Chapter 10, Miler et al. discuss the development of a tool for the analysis and comparison of efficiency indicated by the integrated IT systems in the operational activities undertaken by Road Transport Enterprises (RTEs). The authors of this Chapter further provide a holistic evaluation of efficiency of telematics systems in RTE operational management. The book ends with the two Chapters of the V Part on Smart Environmental Monitoring. In Chapter 11, He et al. propose a Sea Surface Temperature Prediction (SSTP) model based on time-series similarity measure, multiple pattern learning and parameter optimization. In this strategy, the optimal parameters are determined by means of an improved Particle Swarm Optimization method. In Chapter 12, Tsipis et al. present a low-cost, WSN-based IoT system that seamlessly embeds a three-layered cloud/fog computing architecture, suitable for facilitating smart agricultural applications, especially those related to wildfire monitoring. We wish to thank all the authors that contributed to this book for their efforts. We express our gratitude to all reviewers for the volunteering support and precious feedback during the review process. We hope that this book provides valuable information and spurs meaningful discussion among researchers, engineers, businesspeople, and other experts about the role of new technologies into industry and society

iCity. Transformative Research for the Livable, Intelligent, and Sustainable City

This open access book presents the exciting research results of the BMBF funded project iCity carried out at University of Applied Science Stuttgart to help cities to become more liveable, intelligent and sustainable, to become a LIScity. The research has been pursued with industry partners and NGOs from 2017 to 2020. A LIScity is increasingly digitally networked, uses resources efficiently, and implements intelligent mobility concepts. It guarantees the supply of its grid-bound infrastructure with a high proportion of renewable energy. Intelligent cities are increasingly human-centered, integrative, and flexible, thus placing the well-being of the citizens at the center of developments to increase the quality of life. The articles in this book cover research aimed to meet these criteria. The book covers research in the fields of energy (i.e. algorithms for heating and energy storage systems, simulation programs for thermal local heating supply, runtime optimization of combined heat and power (CHP), natural ventilation), mobility (i.e. charging distribution and deep learning, innovative emission-friendly mobility, routing apps, zero-emission urban logistics, augmented reality, artificial intelligence for individual route planning, mobility behavior), information platforms (i.e. 3DCity models in city planning: sunny places visualization, augmented reality for windy cities, internet of things (IoT) monitoring to visualize device performance, storing and visualizing dynamic energy data of smart cities), and buildings and city planning (i.e. sound insulation of sustainable facades and balconies, multi-camera mobile systems for inspection of tunnels, building-integrated photovoltaics (BIPV) as active façade elements, common space, the building envelopes potential in smart sustainable cities)

iCity. Transformative Research for the Livable, Intelligent, and Sustainable City

This open access book presents the exciting research results of the BMBF funded project iCity carried out at University of Applied Science Stuttgart to help cities to become more liveable, intelligent and sustainable, to become a LIScity. The research has been pursued with industry partners and NGOs from 2017 to 2020. A LIScity is increasingly digitally networked, uses resources efficiently, and implements intelligent mobility concepts. It guarantees the supply of its grid-bound infrastructure with a high proportion of renewable energy. Intelligent cities are increasingly human-centered, integrative, and flexible, thus placing the well-being of the citizens at the center of developments to increase the quality of life. The articles in this book cover research aimed to meet these criteria. The book covers research in the fields of energy (i.e. algorithms for heating and energy storage systems, simulation programs for thermal local heating supply, runtime optimization of combined heat and power (CHP), natural ventilation), mobility (i.e. charging distribution and deep learning, innovative emission-friendly mobility, routing apps, zero-emission urban logistics, augmented reality, artificial intelligence for individual route planning, mobility behavior), information platforms (i.e. 3DCity models in city planning: sunny places visualization, augmented reality for windy cities, internet of things (IoT) monitoring to visualize device performance, storing and visualizing dynamic energy data of smart cities), and buildings and city planning (i.e. sound insulation of sustainable facades and balconies, multi-camera mobile systems for inspection of tunnels, building-integrated photovoltaics (BIPV) as active façade elements, common space, the building envelopes potential in smart sustainable cities)

Open research issues on multi-models for complex technological systems

Abstract -We are going to report here about state of the art works on multi-models for complex technological systems both from the theoretical and practical point of view. A variety of algorithmic approaches (k-mean, dss, etc.) and applicative domains (wind farms, neurological diseases, etc.) are reported to illustrate the extension of the research area

Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges

Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrigue across scientific, commercial, and societal arenas. This fascination extends particularly to the Internet of Things (IoT), a landscape characterized by the interconnection of countless devices, sensors, and systems, collectively gathering and sharing data to enable intelligent decision-making and automation. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the IoT. Specifically, it starts by outlining the fundamental principles of IoT and the critical role of Artificial Intelligence (AI) in IoT systems. Subsequently, it delves into AGI fundamentals, culminating in the formulation of a conceptual framework for AGI's seamless integration within IoT. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education. However, adapting AGI to resource-constrained IoT settings necessitates dedicated research efforts. Furthermore, the paper addresses constraints imposed by limited computing resources, intricacies associated with large-scale IoT communication, as well as the critical concerns pertaining to security and privacy