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 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.

Urban trees provide essential ecosystem services on regulating temperature and humidity, filtering urban pollutants, and improving air quality. However, the increasing number of urban trees put pressure on maintenance and public safety. The total compensatory value of the trees, consisting of inspection, maintenance, and settlement of tree damages, is more than $2 trillion USD. At this point in time, there is no known research on manifesting guidance on automated tree health assessment. The Internet-of-Things (IoT) proliferates the deployment of wireless sensors and networks. A concept of the IoT trees is raised to implement various sensors on the trees for automated health monitoring and assessment. In this paper, an urban tree health index (UTHI) is first developed to indicate the health of urban IoT trees. The index will facilitate preventive measures on urban trees. To construct the indexing model, seven (7) dynamic (time-series) features and seven (7) static features are extracted to explore the ambient effects on urban tree health. Afterward, a heterogeneous neural network (HNN) for UTHI modeling is developed to adopt the heterogeneous feature structure. In HNN, the dynamic features are analyzed in the gated recurrent unit (GRU) layer and the static features are analyzed in a hidden layer. The novel fusion layer then aggregates the outputs computed from those layers and further explores unseen correlations among all features. The experimental result verifies that the HNN-based modeling achieves high accuracy and model fitness with the error rate of less than 5%. In addition, the HNN achieves 34% to 66% improvement of accuracy in comparison with the other machine learning algorithms. The supremacy of the developed model is that all indexing features can be predefined or monitored by the IoT sensors, thus rendering an automated and economic urban tree management.