The development in the Internet-of-Things (IoT) has advanced healthcare. The direction of healthcare has been transformed from treatment to health management. Thus, more data are being generated and aggregated than ever before while healthcare moves toward patient-oriented and analytic application. This paper discusses the different aspects of smart healthcare along with the health data and patient-oriented health management.

The ubiquitous connectivity between the things in a supply chain ecosystem will contribute to the information flow essential for establishing smart solutions for efficient, reliable, and intelligent supply chain operations. In this article, a new model integrating supply chain and IoT, referred to as SCoT, is proposed to evolve into SSCE and thus enhance supply chain productivity. Productivity in this article considers not only the rate of product output but also product quality and the rate of new product development. First, the ubiquitous and ultra-reliable connectivity necessary for connecting all things in SCoT is newly defined. Four fundamental applications in SSCE are identified, and four related use cases are discussed. Seamless information access in the fully connected SCoT network will facilitate supply chain management. A novel universal SCoT platform is thus proposed to perform intelligent operations on quality measurement, product integration, and decision making. Hitherto, there is no universal standard that quantitatively grades all Supply Chain of Things with a single indicator. To ensure the platform's objectiveness and consistency, an emerging IoT standard of IEEE P2668, namely IDex, is incorporated into the platform. IDex provides quantitative measurement, grading, and ranking of all IoT things. In addition, IDex also benefits the platform by contributing three extra functions to advise on a solution for quality improvement, integration guidance, and performance prediction. Ultimately, SCoT will proliferate five key impacts on automated quality control, efficient integration, smart transportation, intelligent decision making, and end-to-end traceability. Finally, three key future research directions in the implementation of SCoT are discussed.

The Internet of Things (IoT) has been attracting people to its capability to deal with smart applications. However, with the development of IoT, there are more attacks to threaten IoT systems. Especially, the distributed denial of service (DDoS) attack can lead to mighty destruction to IoT servers, causing the whole IoT network to be out-of-service. Hitherto, given the lack of a common standard for defining the IoT-driven DDoS (IoT-DDoS) attack, the DDoS defense system for IoT is developed without accurate guidance. Additionally, defense against multi-layer IoT-DDoS attacks is rarely covered by previous works. To address these issues, a deep reinforcement learning-based multi-layer IoT-DDoS defense system (DRL-MLDS) is proposed with the reward metrics in compliance with IEEE P2668 – the first of its kind. In addition, to provide a resilient blocking time configuration for false-positive samples, a new power-law-based blocking time mechanism is developed to cooperate with the DRL-MLDS. The outcome reveals that the DRL-MLDS can reach the same accuracy level (i.e., more than 96%) as previous works under single protocol-based IoT DDoS attack, as well as providing around 97% defense accuracy on multi-layer IoT-DDoS attack, which was rarely discussed in previous works. Additionally, by applying the IEEE P2668-compliant reward metrics, the applicability index (ADex) of DRL-MLDS can be improved from 3.2 to 4.4, fulfilling the recommendation of ADex (e.g., $\mathbf {>}3.5$ ) toward IoT best practices. The DRL-MLDS can be extended to Metaverse design and applications.

The defects within perovskite films are the fatal roadblock limiting the performance of perovskite solar cells (PSCs). Herein, we develop a patching strategy to obtain high‐quality perovskite film using bifunctional trifluorophenylacetic acid (TFPA). Theoretical calculation and experiments reveal that the –COOH groups in TFPA effectively passivate the deep‐energy‐level defects of perovskite by combining with Pb clusters on the perovskite grain surfaces to enhance the efficiency of PSCs, and hydrophobic benzene groups containing fluorine in TFPA are exposed to improve the stability of PSCs. The power conversion efficiency (PCE) of PSCs with TFPA is enhanced from 22.95% to 24.56%. Most importantly, we successfully fabricated flexible PSCs using TFPA with the high PCE of 22.65%. The devices with TFPA maintain 93.22% of the initial efficiency MPP under continuous irradiation for 10.5 h, while the devices without TFPA only retain 82.22% of the initial efficiency under the same conditions for just 5 h. Meanwhile, the unsealed devices with TFPA hold 93.59% of the initial efficiency when stored in air for 3912 h. image

The Received signal strength (RSS) fingerprint-based approaches are widely used for indoor location-based services. The emerging Long Range Wide Area Network (LoRaWAN) is a cost-effective solution for indoor latency-tolerant location-based services attributed to its long-range property. In general, there are serious RSS fluctuations due to fadings along the communication path, thus significantly jeopardizing the localization accuracy. To overcome the challenge, we propose the extreme RSS to stabilize the fingerprint database and formulate boundary autocorrelation to downsize tremendously the searching complexity and thus proliferating localization accuracy. In essence, the RSS fluctuations are modeled as a Bernoulli random process so that the RSS stability can be estimated by a newly defined fluctuation analytic function. To mitigate the impact of the perturbative fluctuation, the extreme RSS is further defined to cultivate a highly stable and robust fingerprint database which withstand environmental dynamics. In addition, boundary autocorrelation is developed to measure and compare the similarity between the measured RSS values versus the prestored fingerprint database. RSS values with low autocorrelation coefficients are eradicated from the typically lengthy searching. The downsized complexity significantly improves the localization accuracy. Experiments were carried out and the results revealed that the proposed method achieved sub-10-meter localization accuracy in indoor environments. Such accuracy is encouraging and superior in contemporary LoRaWAN measurements.