Reconfigurable Intelligent Surface-Aided Cooperative NOMA with p-CSI Fading Channel toward 6G-Based IoT System.

Addressing the challenges of internet-based 5G technology, namely increasing density through micro-cell systems, frequency spectrum, and reducing resource costs, is needed to meet the use of IoT-based 6G technology with the goal of high-speed, high-capacity, and low-latency communication. In this research, we considered the coverage performance and ergodic capacity of the Reconfigurable Intelligent Surface (RIS)-aided cooperative nonorthogonal multiple-access network (NOMA) of an IoT system. This enables the upgrading of 5G- toward 6G-technology-based IoT systems. We developed a closest-form formula of near and far user coverage probabilities as a function of perfect channel statistical information (p-CSI) using only a single-input single-output (SISO) system with a finite number of RIS elements under the Nakagami-m fading channel. We also define ergodic capacity as a simple upper limit by simplifying the use of symbolic functions and it could be used for a sustained period. The simulation findings suggest that RIS-assisted NOMA has a reduced risk of outage than standard NOMA. All of the derived closed-form formulas agree with Monte Carlo simulations, indicating that the distant user's coverage probability outperforms the nearby user. The bigger the number of RIS parts, however, the greater the chance of coverage. They also disclose the scaling law of the number of phase shifts at the RIS-aided NOMA based on the asymptotic analysis and the upper bound on channel capacity. In both arbitrary and optimum phase shifts, the distant user's ergodic capacity outperforms the near user.

An Implementation of Trust Chain Framework with Hierarchical Content Identifier Mechanism by Using Blockchain Technology.

Advances in information technology (IT) and operation technology (OT) accelerate the development of manufacturing systems (MS) consisting of integrated circuits (ICs), modules, and systems, toward Industry 4.0. However, the existing MS does not support comprehensive identity forensics for the whole system, limiting its ability to adapt to equipment authentication difficulties. Furthermore, the development of trust imposed during their crosswise collaborations with suppliers and other manufacturers in the supply chain is poorly maintained. In this paper, a trust chain framework with a comprehensive identification mechanism is implemented for the designed MS system, which is based and created on the private blockchain in conjunction with decentralized database systems to boost the flexibility, traceability, and identification of the IC-module-system. Practical implementations are developed using a functional prototype. First, the decentralized application (DApp) and the smart contracts are proposed for constructing the new trust chain under the proposed comprehensive identification mechanism by using blockchain technology. In addition, the blockchain addresses of IC, module, and system are automatically registered to InterPlanetary File System (IPFS), individually. In addition, their corresponding hierarchical CID (content identifier) values are organized by using Merkle DAG (Directed Acyclic Graph), which is employed via the hierarchical content identifier mechanism (HCIDM) proposed in this paper. Based on insights obtained from this analysis, the trust chain based on HCIDM can be applied to any MS system, for example, this trust chain could be used to prevent the counterfeit modules and ICs employed in the monitoring system of a semiconductor factory environment. The evaluation results show that the proposed scheme could work in practice under the much lower costs, compared to the public blockchain, with a total cost of 0.002094 Ether. Finally, this research is developed an innovation trust chain mechanism that could be provided the system-level security for any MS toward Industrial 4.0 in order to meet the requirements of both manufacturing innovation and product innovation in Sustainable Development Goals (SDGs).

Federated Compressed Learning Edge Computing Framework with Ensuring Data Privacy for PM2.5 Prediction in Smart City Sensing Applications.

The sparse data in PM2.5 air quality monitoring systems is frequently happened on large-scale smart city sensing applications, which is collected via massive sensors. Moreover, it could be affected by inefficient node deployment, insufficient communication, and fragmented records, which is the main challenge of the high-resolution prediction system. In addition, data privacy in the existing centralized air quality prediction system cannot be ensured because the data which are mined from end sensory nodes constantly exposed to the network. Therefore, this paper proposes a novel edge computing framework, named Federated Compressed Learning (FCL), which provides efficient data generation while ensuring data privacy for PM2.5 predictions in the application of smart city sensing. The proposed scheme inherits the basic ideas of the compression technique, regional joint learning, and considers a secure data exchange. Thus, it could reduce the data quantity while preserving data privacy. This study would like to develop a green energy-based wireless sensing network system by using FCL edge computing framework. It is also one of key technologies of software and hardware co-design for reconfigurable and customized sensing devices application. Consequently, the prototypes are developed in order to validate the performances of the proposed framework. The results show that the data consumption is reduced by more than 95% with an error rate below 5%. Finally, the prediction results based on the FCL will generate slightly lower accuracy compared with centralized training. However, the data could be heavily compacted and securely transmitted in WSNs.

An overview of the development of vertical sampling technologies for ambient volatile organic compounds (VOCs).

Atmospheric volatile organic compounds (VOCs) are harmful to human health and the environment, and are precursors of other toxic air pollutants, e.g. ozone (O3) and secondary organic aerosols (SOAs). In recent years, due to scientific and technological advancements, vertical VOC profile in the atmosphere has been increasingly studied since it plays an essential role in the atmospheric research by providing multilevel three-dimensional data. Such information will improve the predictive ability of existing air quality models. This review summarizes the latest development of vertical VOC sampling technologies, highlighting the technical and non-technical challenges with possible solutions and future applications of vertical VOC sampling technologies. Further, other important issues concerning ambient VOCs have also been discussed, e.g. emission sources, VOC air samplers, VOC monitoring strategies, factors influencing airborne VOC measurement, the use of VOC data in air quality models and future smart city air quality management. Since ambient VOC levels can fluctuate significantly with altitude, technologies for vertical VOC profiling have been developed from building/tower-based measurements and tethered balloons to aircrafts, unmanned aerial vehicles (UAVs) and satellites in order to improve the temporal-spatial capacity and accuracy. Between the existing sampling methods, so far, UAVs are capable of providing more reliable VOC measurements and better temporal-spatial capacities. Heretofore, their disadvantages and challenges, e.g. sampling height, sampling time, sensitivity of the sensors and interferences from other chemical species, have limited the application of UAV for vertical VOC profiling.

Vertical stratification of volatile organic compounds and their photochemical product formation potential in an industrial urban area.

High emissions of volatile organic compounds (VOCs) from the petrochemical industry and vehicle exhaust may contribute to high ozone formation potential (OFP) and secondary organic aerosol formation potential (SOAFP). In this study, the vertical profiles of VOCs were created for the southern Taiwan industrial city of Kaohsiung. Vertical air samples were collected up to 1000 m using an unmanned aerial vehicle (UAV). In Renwu District, VOC distribution was affected by the inversion layer up to 200 m height. Total VOCs (36-327 ppbv), OFP (66-831 ppbv) and SOAFP (0.12-5.55 ppbv) stratified by height were the highest values at 300 m. The VOCs originated from both local and long-distance transport sources. These findings can be integrated into Kaohsiung's future air quality improvement plans and serve as a reference for other industrialized areas worldwide.