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Optimizing digital twins in the Industrial Internet of Things (IIoT) requires secure and adaptable AI models. The IIoT enables digital twins, virtual replicas of physical assets, to improve real-time decision-making, but challenges remain in trust, data security, and model accuracy. This paper presents a novel framework combining blockchain technology and federated learning (FL) to address these issues. By deploying AI models on edge devices and using FL, data privacy is maintained while enabling collaboration across industrial assets. Blockchain ensures secure data management and transparency, while explainable AI (XAI) enhances interpretability. The framework improves transparency, control, security, privacy, and scalability for self-optimizing digital twins in IIoT. A real-world evaluation demonstrates the framework's effectiveness in enhancing security, explainability, and optimization, offering improved efficiency and reliability for industrial operations. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have demonstrated remarkable performance in many areas but require significant computation and storage resources. Quantization is an effective method to reduce CNN complexity and implementation. The main research objective is to develop a scalable quantization algorithm for CNN hardware design and model the performance metrics for the purpose of CNN implementation in resource-constrained devices (RCDs) and optimizing layers in deep neural networks (DNNs). The algorithm novelty is based on blending two quantization techniques to perform full model quantization with optimum accuracy, and without additional neurons. The algorithm is applied to a selected CNN model and implemented on an FPGA. Implementing CNN using broad data is not possible due to capacity issues. With the proposed quantization algorithm, we succeeded in implementing the model on the FPGA using 16-, 12-, and 8-bit quantization. Compared to the 16-bit design, the 8-bit design offers a 44% decrease in resource utilization, and achieves power and energy reductions of 41% and 42%, respectively. Models show that trading off one quantization bit yields savings of approximately 5.4K LUTs, 4% logic utilization, 46.9 mW power, and 147 μ J energy. The models were also used to estimate performance metrics for a sample DNN design. [ABSTRACT FROM AUTHOR]

We provide (1) an overview of various present and future applications of smart contracts across various industries including real estate, finance, and healthcare and (2) an assessment of the efficacy of smart contracts as a means of replacing or supplementing traditional contracts. Disclosed in this paper are (1) present and future applications of smart contracts and potential risks and downsides, and (2) legal considerations when using smart contracts to replace or supplement traditional contracts. Aspects of blockchain technologies can be applied to traditional contracts, in part or in whole, to reduce common challenges associated with contracts. Specifically, smart contracts can be integrated with or replace traditional contracts with the benefit of ensuring reciprocal obligations are enforced and aid in ensuring mutual consent, offer and acceptance, consideration and legal purpose. [ABSTRACT FROM AUTHOR]