• Rural Digital Europe
• Publications close
• Other research products close
• 7. Clean energy close

The explosive growth of wireless sensor platforms and their emerging wide range of application areas make the development of a sustainable and robust power source, an essential requirement to enable widespread deployment of these wireless devices. As a solution to this cardinal issue, this paper reports the design and fabrication of a resonant Vibration Energy Harvester (VEH) that comprises interleaved springs, manifesting a concertina shaped structure that can enable large mechanical amplitudes of oscillation. Within a relatively small footprint (9cm3), this concertina-VEH yields a large power density of 455.6μW/cm3g2 while operating at a resonant frequency of 75Hz. Additionally, the feasibility of the implemented VEH to support NFC based wireless sensor platform, that is yet uncharted, is also investigated in this work. A very low-power consumption Near Field Communication (NFC) wireless sensor node has been designed and developed for this purpose. The developed concertina VEH has been employed to power the electronics interface of this NFC sensor. Using mechanical energy derived from as low as 0.2g excitation, our study shows that the VEH can enhance the electromagnetic interaction between the transmitting antenna and the reader, resulting in a 120% increase in wireless communication range for the NFC sensor node. Such a high-performance energy harvester assisted NFC sensor node has the potential to be used in a wide range of Internet of Things (IoT) platforms as a reliable and sustainable power solution.

The unprecedented development, growth, and widespread pervasiveness of digital Information Communication Technologies (ICTs) have coincided with ever-increasing levels of consumption and the climate emergency. Digital ICTs, once lauded for their potential to dematerialize society, are now imposing additional burdens on the planet. The widespread consumption of personal electronics continues to grow at an enormous rate, while recycling of the scarce rare-earth minerals that are crucial to their development is negligible. As digital technologies become ubiquitous, the need for additional energy to power our ever-increasing number of digital devices and services must also keep pace. Moreover, despite their public veneer as progressives, digital tech companies are collaborating with fossil fuel companies to render oil and gas extraction more profitable and with greater speed, fuelling climate breakdown. Online social platforms are also being misused as podiums for dis/misinformation and falsehoods counter to the scientific consensus of anthropogenic climate change, allowing the digital tech sector to abdicate any social responsibility and denying the dire consequences of inaction. This review article explores the growing consumption demands and the ecological threat from digitalization and the digital tech sector: demands that will only intensify with our insatiable appetite for digital tech services and products. Such a review aims to draw closer attention to some ways such technology can be used to assist ecological research and conservation, but also to expand upon our understanding of the negative environmental aspects of a relentless push toward a Digital Society. In uncritically accepting Big Tech's virtuous credentials, we are choosing to ignore the immense power and influence they have over our lives, and the ways they may be propelling our environment toward collapse.

Turkey is a laggard in terms of the achievement of its Sustainable Development Goals (SDGs), and one of the primary issues it faces is environmental deterioration. Therefore, a policy-level reorientation may be needed to address this relevant issue. From this standpoint, this research assesses the impact of renewable energy (RE) use and financial development on the emissions of CO2 as well as the role of urbanization and agriculture, utilizing a dataset stretching between 1985 and 2019. By applying the innovative quantile-on-quantile regression (QQR) and non-parametric Granger causality in quantiles techniques, the study assesses the ways in which the quantiles of the independent variables affect the quantiles of CO2 emissions. The outcomes from the QQR show that in all quantiles, financial development, economic growth, urbanization, and agriculture impact CO2 emissions positively, while in the middle quantiles, the influence of renewable energy use on CO2 is negative. Furthermore, the outcomes from the non-parametric Granger causality test disclosed that in mean and variance, all the variables could predict CO2 emissions at different quantiles. A complete SDG-oriented policy framework has been proposed based on the research’s findings so that Turkey may move toward reaching its SDG 13 and SDG 7 targets.

The applications of the Internet of Things (IoT) technologies connect multiple devices directly and through the Internet. Autonomous vehicles utilise connectivity when updating their algorithms based on user data, interact with the infrastructure to get environmental information, communicate with other vehicles. They exchange information with pedestrians using mobile devices and wearables and provide information about the traffic attributes and data collected by the vehicle sensors. The connected and automated vehicles (CAV) require a significant quantity of collecting and processing data and through IoT applications and services the autonomous vehicles share information about the road, the present path, traffic, and how to navigate around different obstacles. This information can be shared between IoT connected vehicles and uploaded wirelessly to the cloud or/and edge system to be analysed and operated improving the levels of automation and the autonomous driving (AD) functions of each vehicle. This chapter gives an overview of the integration of IoT devices contributing to automated/autonomous driving, and the IoT infrastructure deployed and seamlessly integrated into the AUTOPILOT project use cases and pilot demonstrators, including the IoT platforms integration.

© 2000-2011 IEEE.Future distribution grids are expected to face an increasing penetration of electric vehicles (EVs) and heterogeneous distributed energy resources (DERs). This demands a distributed energy management (EM) to manage power generation and delivery of energy sources to maintain power quality under the impact of EV charging, to save operating costs, and to enhance resiliency. However, the global optimality of the distributed EM\"s optimization problem is still an issue in existing works because of the non-convex nature of the optimization problem. In this paper, a distributed EM strategy for grid-connected distribution networks is proposed. In particular, the EM strategy is composed of two steps. In the first step, some conditions of the EM optimization task are relaxed to apply an algorithm converging to the global optimality. The results of the first step are used to reconfigure constraints of the full optimization problem in Step 2. The proposed scheme is validated by implementing the real-time controller-hardware-in-the-loop (CHIL) experimentation on the IEEE 33 bus system. To study the impact of EV charging, EV data is collected from the market and the literature to generate realistic EV load profiles to demonstrate the effectiveness of the proposed strategy on saving operating costs and maintaining power quality.

AbstractMarine operations required to transfer technicians and equipment represent a significant proportion of the total cost of offshore wind. The profile of sites being considered for floating offshore wind farms (FOWFs), e.g., further from the shore and in harsher environments, indicates that these costs need to be assessed by taking into account the maintenance requirements and restricted weather windows. There is an immediate need to investigate the potential use of robotic systems in the wind farm's operations and maintenance (O&M) activities, to reduce the need for costly manned visits. The use of robotic systems can be critical, not only to replace repetitive activities and bring down the levelised cost of energy but also to reduce the health and safety risks by supporting human operators in performing the desired inspections. This paper provides a review of the state of the art in the applications of robotics for O&M of FOWFs. Emerging technology trends and associated challenges and opportunities are highlighted, followed by an outline of the agenda for future research in this domain.

Millions of battery-powered sensors deployed for monitoring purposes in a multitude of scenarios, e.g., agriculture, smart cities, industry, etc., require energy-efficient solutions to prolong their lifetime. When these sensors observe a phenomenon distributed in space and evolving in time, it is expected that collected observations will be correlated in time and space. In this paper, we propose a Deep Reinforcement Learning (DRL) based scheduling mechanism capable of taking advantage of correlated information. We design our solution using the Deep Deterministic Policy Gradient (DDPG) algorithm. The proposed mechanism is capable of determining the frequency with which sensors should transmit their updates, to ensure accurate collection of observations, while simultaneously considering the energy available. To evaluate our scheduling mechanism, we use multiple datasets containing environmental observations obtained in multiple real deployments. The real observations enable us to model the environment with which the mechanism interacts as realistically as possible. We show that our solution can significantly extend the sensors' lifetime. We compare our mechanism to an idealized, all-knowing scheduler to demonstrate that its performance is near-optimal. Additionally, we highlight the unique feature of our design, energy-awareness, by displaying the impact of sensors' energy levels on the frequency of updates.