Electric power grid infrastructure has revolutionized our world and changed the way of living. So has blockchain technology. The hierarchical electric power grid has been shifting from a centralized structure to a decentralized structure to achieve higher flexibility and stability, and blockchain technology has been widely adopted in the energy sector to deal with grid management, billing, metering, and so on, because of its nature of decentralization. Here, the aim is to provide a multi‐dimensional review on the technological advances of the blockchain in smart grids. Its corresponding applications based on these advances, including company projects and use cases, are summarized. Furthermore, the security threat issues in smart grids, Ethereum Virtual Machine (i.e. the operating environment of consensus mechanisms), and smart contracts are analysed, with a brief conclusion to manifest the prior tasks in building secure blockchain‐based infrastructures in smart grids. As such, the challenges and features of different protocols and their applicability in each use case are identified to provide an insightful guide for future research studies.

Air-borne particulate matter, PM_2.5 (PM having a diameter of less than 2.5 micrometers), has aroused widespread concern and is a core indicator of severe air pollution in many cities globally. In our study, we present a validated framework to predict the daily PM_2.5 distributions, exemplified by a use case of Shijiazhuang City, China, based on daily aerosol optical depth (AOD) datasets. The framework involves obtaining the high-resolution spatiotemporal AOD distributions, estimation of the spatial distributions of PM_2.5 and the prediction of these based on a convolutional long short-term memory (ConvLSTM) model. In the estimation part, the eXtreme gradient boosting (XGBoost) model has been determined as the estimation model with the lowest root mean square error (RMSE) of 32.86 µg/m³ and the highest coefficient of determination regression score function (R²) of 0.71, compared to other common models used as a baseline for comparison (linear, ridge, least absolute shrinkage and selection operator (LASSO) and cubist). For the prediction part, after validation and comparison with a seasonal autoregressive integrated moving average (SARIMA), which is a traditional time-series prediction model, in both time and space, the ConvLSTM gives a more accurate performance for the prediction, with a total average prediction RMSE of 14.94 µg/m³ compared to SARIMA’s 17.41 µg/m³. Furthermore, ConvLSTM is more stable and with less fluctuations for the prediction of PM_2.5 in time, and it can also eliminate better the spatial predicted errors compared to SARIMA.

Researchers estimate that more than 8.3 billion tonnes of plastic have been produced since the early 1950s; however, only 9% of all plastic waste ever produced has been recycled. In this article, we propose a plastic credit driven system consisting of a recyclability index (RI) and plastic credit to impel plastic recycling and increase the quality of recyclable plastics through a market self-regulation mechanism. The RI is designed to evaluate the recyclability value of different plastic products based on their material compositions. The plastic credit, defined by the quantitative relation between the RI and product information, can be issued or traded by system stakeholders. Instead of setting rigid industry standards to regulate plastic quality, we construct a governance community among industry participants using blockchain-enabled smart contracts to self-regulate and monitor plastic production and trading. The proposed system is constructed on a consortium blockchain and a public blockchain to negotiate the RI, issue credits, and trade credits using smart contracts. Through the overall system performance analysis, the experimental results demonstrate that the designed plastic credit system can promote a demand shift toward plastic products with higher plastic recyclability and achieve a lightweight operation for resource requirements and system maintenance.

As a novel distributed machine learning approach, federated learning (FL) is proposed to train a global model while preserving data privacy. However, some studies manifest that adversaries can still recover private information from the gradients. Differential privacy (DP) is a rigorous mathematical tool to protect records in a database against leakage. It has been widely applied in FL by perturbing the gradients. Nevertheless, while using DP in FL, the convergence performance of the global model is inevitably degraded. In this article, we implement a DP-based FL scheme, which achieves local DP (LDP) by adding well-designed Gaussian noise on the gradients before clients upload them to the server. After that, we propose two strategies to improve the convergence performance of the DP-based FL. Both methods are realized by modifying the local objective function to limit the effect of LDP noise on convergence without degrading the privacy protection level. We then provide the detailed framework which adopts the LDP scheme and two strategies. The framework on different machine learning models is tested by simulation results, which show that our framework can improve the convergence performance up to 40% faster under different noise compared with other DP-based FL. Finally, we show the theoretical convergence guarantee of our proposed framework by first presenting the expected decrease in the global loss function for one round of training and then providing the upper convergence bound after multiple communication rounds.

Web 3.0 introduces the concept of crypto-network entities, which are independently defined by public key pairs and connected to Web 3.0 cyberspace. In this paper, we present a spacetime coordinate system that is based on fractal polytopes in any dimension, utilizing blockchain technology for discrete time and consensus. Furthermore, we define novel network entities and functions that utilize hyperdimensional deterministic switching and routing protocols, as well as blockchain-enabled mutual authentication. In addition to our spacetime network architecture, we devise a multi-tier identity scheme that extends the native Web 3.0 crypto-network-entity to the outer cyber and physical world, including internet infrastructure and future generations of mobile networks, such as 6G. This provides legal compliance with anonymity and linkability to all derived identifiers of entities. By integrating crypto-network entities and novel hyperdimensional space-time networks, we establish a unified, holistic Web 3.0 network that is persistent in both spacetimes and extended to our cyber and physical world.