With the global transportation sector being a major contributor to greenhouse gas (GHG) emissions, transitioning to cleaner and more efficient forms of transportation is essential for mitigating climate change and improving air quality. Toward sustainable mobility, Fuel Cell Electric Vehicles (FCEVs) have emerged as a promising solution offering zero-emission transportation without sacrificing performance or range. However, FCEV adoption still faces significant challenges regarding refueling infrastructure. This work proposes an innovative refueling automation service for FCEVs to facilitate the refueling procedure and to increase the fuel cell lifetime, by leveraging (i) Big Data, namely, real-time mobility data and (ii) Machine Learning (ML) for the energy consumption forecasting to dynamically adjust refueling priorities. The proposed service was evaluated on a simulated FCEV energy consumption dataset, generated using both the Future Automotive Systems Technology Simulator and real-time data, including traffic information and details from a real-world on demand Public Transportation service in the Geneva Canton region. The experimental results showcased that all three ML algorithms achieved high accuracy in forecasting the vehicle’s energy consumption with very low errors on the order of 10% and below 20% for the normalized Mean Absolute Error and normalized Root Mean Squared Error metrics, respectively, indicating the high potential of the suggested service.

Weather constitutes a crucial factor that impacts many of the human outdoor activities, whether they are related to obligations or pleasure. In the contemporary era, due to climate change, the weather is more unstable and the forecasting task is more challenging than ever. By combining the Internet of Things (IoT) with Artificial Intelligence (AI), a new research field emerges that is called Artificial Intelligence of Things (AIoT) and could offer significant possibilities for the research community in order to efficiently tackle the short-term weather forecasting. Renewable energy sources constitute solutions for the achievement of sustainability development goals and could also offer power autonomy in a weather forecasting station. In the present research study, everWeather is proposed as a low-cost, self-powered weather forecasting station based on the AIoT paradigm and renewable energy. The proposed solution combines a variety of low-cost environmental sensors, the prowess of solar energy and an appropriate lightweight Machine Learning (ML) algorithm such as the Multiple Linear Regression (MLR) in order to forecast physical weather for the next half hour. Preliminary experiments have been conducted for the proposed solution validation and the corresponding results highlighted that the performance of the everWeather station is quite satisfactory, in terms of reliability and forecasting accuracy.

Smart farming has emerged as a promising approach to address the agriculture industry’s significant contribution to greenhouse gas (GHG) emissions. However, the effectiveness of current smart farming practices in mitigating GHG emissions remains a matter of ongoing debate. This review paper provides an in-depth examination of the current state of GHG emissions in smart farming, highlighting the limitations of existing practices in reducing GHG emissions and introducing innovative strategies that leverage the advanced capabilities of 6G-enabled IoT (6G-IoT). By enabling precise resource management, facilitating emission source identification and mitigation, and enhancing advanced emission reduction techniques, 6G-IoT integration offers a transformative solution for managing GHG emissions in agriculture. However, while smart agriculture focuses on technological applications for immediate efficiency gains, it also serves as a crucial component of sustainable agriculture by providing the tools necessary for long-term environmental supervision and resource sustainability. As a result, this study also contributes to sustainable agriculture by providing insights and guiding future advancements in smart farming, particularly in the context of 6G-IoT, to develop more effective GHG mitigation strategies for smart farming applications, promoting a more sustainable agricultural future.

Smart agriculture has laid a solid foundation for the development of society and the growth of national economies. However, due to the vast variety of cultivable lands, some of which may be in inaccessible regions, numerous requirements and challenges have emerged over time, necessitating the development of innovative technological solutions. Despite the fact that technological improvements have substantially facilitated the expansion of agricultural services, more effort is required to fully integrate novel solutions based on heterogeneous network architectures. In addition, the incorporation of new technology to achieve high performance, while preserving high quality and a minimal environmental footprint, remains a challenge for sustainable agricultural development. Therefore, researchers continue to work on designing and developing new technologies and techniques to meet these objectives. Consequently, the management of Internet of Things (IoT) data, the expansion of terrestrial connectivity, and the development of a sustainable infrastructure for the future generation of agriculture are critical research challenges. In this context, new technological approaches and solutions, such as Νon-Τerrestrial telecommunications Νetworks, Machine Learning (ML)-based algorithms, and Green IoT protocols, are being proposed and investigated. The aim of this chapter is to familiarize readers with the current agricultural landscape, its requirements, and the most promising technologies being developed to meet those needs.

Throughout human history, agriculture has undergone a series of progressive transformations based on ever-evolving technologies in an effort to increase productivity and profitability. Over the years, farming methods have evolved significantly, progressing from Agriculture 1.0, which relied on primitive tools, to Agriculture 2.0, which incorporated machinery and advanced farming practices, and subsequently to Agriculture 3.0, which emphasized mechanization and employed intelligent machinery and technology to enhance productivity levels. To further automate and increase agricultural productivity while minimizing agricultural inputs and pollutants, a new approach to agricultural management based on the concepts of the fourth industrial revolution is being embraced gradually. This approach is referred to as “Agriculture 4.0” and is mainly implemented through the use of Internet of Things (IoT) technologies, enabling the remote control of sensors and actuators and the efficient collection and transfer of data. In addition, fueled by technologies such as robotics, artificial intelligence, quantum sensing, and four-dimensional communication, a new form of smart agriculture, called “Agriculture 5.0,” is now emerging. Agriculture 5.0 can exploit the growing 5G network infrastructure as a basis. However, only 6G-IoT networks will be able to offer the technological advances that will allow the full expansion of Agriculture 5.0, as can be inferred from the relevant scientific literature and research. In this article, we first introduce the scope of Agriculture 5.0 as well as the key features and technologies that will be leveraged in the much-anticipated 6G-IoT communication systems. We then highlight the importance and influence of these developing technologies in the further advancement of smart agriculture and conclude with a discussion of future challenges and opportunities.

Without a doubt the Internet of Things (IoT) paradigm has been gaining attention over the last years. With the deployment of low cost sensors, a plethora of information is made available, giving rise to intelligent systems that combine data from multiple sources. Multiple IoT platforms have been developed to support different application domains such as smart cities, health, industry 4.0, etc. However, the vertical focus and usually isolated development of such platforms pose difficulties in offering cross-domain smart applications to meet society's needs. To this end, interoperability frameworks have emerged to allow the interplay of different platforms as well as the formation of federations among the entities contributing IoT devices through such platforms. The decentralized nature of IoT data federations presents challenges when it comes to their management and governance, particularly in ensuring fairness, security, trustworthiness and transparency among its members. However, permissioned blockchain-based solutions hold significant potential in addressing these issues due to their inherent characteristics. In this work, we propose a blockchain solution based on the Hyperledger Fabric for the decentralized management of IoT federations and their gover-nance by utilizing voting-based configurable rules that abide to the concept of Decentralized Autonomous Organizations (DAOs). The solution includes smart contracts for the secure creation and management of federations exposed through APIs, as well as a voting application for enabling the DAO dynamic. Our solution demonstrates a secure, trustworthy and transparent way for the formation and membership control of IoT federations, that holds potential for extension with smart contracts related to data marketplace, as well as reputation and tokenization mechanisms.

Undoubtedly, during the last few years, climate change has alerted the research community of the natural environment sector. Furthermore, the advent of the Internet of Things (IoT) paradigm has enhanced the research activity in the environmental field by offering low-cost sensors. Moreover, artificial intelligence and more specifically, statistical and machine learning methodologies have proved their predictive power in many disciplines and various real-world problems. As a result of the aforementioned, many scientists in the environmental research field have performed prediction models exploiting the strength of IoT data. Hence, insightful information could be extracted from the review of these research works and for this reason, a Systematic Literature Review (SLR) is introduced in the present manuscript in order to summarize the recent studies in the field under specific rules and constraints. From the SLR, 54 primary studies have been extracted during 2017–2021. The analysis showed that many IoT-based prediction models have been applied in the previous years to 10 different environmental issues, with promising results in the majority of the primary studies.

This article proposes a new approach to the design and assessment of Internet of Things (IoT) systems. Specifically, it identifies the principles that can be derived from the financial technology ecosystem and should be considered when designing an IoT system in order for it to be technically sound, economically efficient, and have the best market acceptance prospects. Considering a specific area of IoT systems, that of the smart agriculture, we then examine whether the proposed design principles are followed by the current state-of-the-art systems. Furthermore, we claim that our proposal can also be utilized as a more comprehensive basis for assessment in comparison to the technology readiness level, which indicates only the technological readiness of a project without grading its market potential.

Continuous patient monitoring during hospitalization is necessary to identify patterns of indicative risks or pathogens, whose early diagnosis and treatment is likely to lead to a reduction in morbidity and mortality and, consequently, a reduction in both the duration and cost of hospitalization. On the other hand, a patient falling from bed can cause serious damage to his health state, while the effect of pressure ulcers can be avoided by timely and accurate mapping of pressure points that inhibit tissue perfusion resulting in death. Recent technological advances and scientific achievements have introduced new and improved medical devices using highly-developed embedded control functions and interactivity. Current hospital beds include new forms of functionality, while still serving the same purpose. All of them are designed to fulfil a predefined purpose, whether that is to monitor the patient's vital signs continuously, in a non-obtrusive manner, or prevent a patient from falling off their bed or prevent the development of pressure ulcers. Over the last decades, this hospital bed evolution has brought a big change in both their standards and the overall patient care. In this work, we conduct a review of existing smart bed systems for patient monitoring, fall and pressure ulcer prevention from the state-of-the-art, focusing on evaluating smart bed systems that include any kind of smart features, like sensors and sensor mats, or exploiting Machine Learning (ML) algorithms and Wireless technology.