Eddy current testing (ECT) is a widely adopted electromagnetic non-destructive testing (NDT) technique for detecting defects in conductive materials. In practical deployments, however, ECT systems often suffer from low signal-to-noise ratio, strong sensitivity to lift-off and environmental variations, and complex multi-parameter coupling, which makes robust signal interpretation challenging. Meanwhile, the growing demand for portable and always-on inspection pushes data processing toward resource-constrained embedded hardware. Tiny machine learning (TinyML) provides a promising pathway for enabling on-device intelligence by deploying compact models with low latency and low power consumption. This review summarizes recent progress in integrating TinyML into ECT, covering the ECT signal characteristics and key technical bottlenecks, the TinyML workflow and optimization techniques for embedded deployment, and representative application scenarios including pipeline inspection, corrosion detection, and thickness evaluation. We further analyze the main barriers to adoption, such as limited computing power and memory, data scarcity, calibration overhead, and generalization across materials, probes, and defect types, and we outline future research directions including physics-guided learning, federated learning, and standardized benchmarks for ECT-oriented TinyML evaluation.

In the current digital era, managing inheritance presents a critical challenge, necessitating a balance of effectiveness, security, and transparency. Traditional processes are often complex, time-consuming, and susceptible to fraud and disputes. This paper introduces the successor chain model called Waris-Chain, a blockchain-based solution designed to streamline and secure inheritance management. Waris-Chain integrates smart contracts and Non-Fungible Tokens (NFTs) to automate and verify inheritance processes, ensuring accuracy and reducing manual intervention. Developed using the Ethereum blockchain, ERC-1155 tokens, and MetaMask for authentication, Waris-Chain offers a comprehensive, adaptable, and secure platform. Performance evaluation shows that Waris-Chain achieves a high throughput of 477.36 transactions per hour, a low transaction latency of 7.54 seconds, with a 99.42% accuracy rate and a 0.58% error rate. Despite these advancements, challenges such as blockchain adoption, legal integration, and system scalability remain, suggesting avenues for future research to fully realize blockchain’s potential in inheritance management.

This study proposes an intelligent approach to identifying an injured soldier on blockchain-integrated Internet-of-Battlefield Things (IoBT) employing unmanned aerial vehicles (UAVs). The intelligent approach combines a unique deep learning (DL) model with a smartwatch-based heart-rate (HR) data collection technique. Different activation functions (i.e., MISH and Leaky rectified linear unit) are used in the proposed DL model to enhance the identification task by extracting the in-depth features from the images. Furthermore, a smart-watch-based HR data analyzing technique is introduced to confirm the injury of a soldier. However, due to the UAV’s low battery capacity, the identification task is offloaded to the neighboring edge computing server to improve system performance. Moreover, to restrict the access of registered IoT devices (e.g., UAV, smartwatch, etc.) and protect the sensitive data leakage on IoBT, a blockchain-integrated access control (ACL) mechanism is utilized. Detailed experimental results are provided for the proposed DL model that outperforms existing DL models. Besides, implementing a smartwatch-based HR data analysis technique for the soldiers improves the outcome of the proposed DL model. To provide a fine-grained data protection mechanism in the proposed system, a private blockchain-based ACL management policy is constructed utilizing hyperledger, and various assessment metrics have been scrutinized.

This article presents a deep learning (DL) model integrated automated and secure garbage management scheme using unmanned any vehicle (UxV) to minimize the human effort in terms of the traditional garbage management system. Different kinds of UxV (unmanned aerial vehicles, automated guided vehicles, unmanned surface vehicles, unmanned underwater vehicles, etc.) are utilized to establish an automated garbage management scheme to collect and place the garbage both from the ground and sea surfaces. However, due to the limited battery capacity and inadequate resources of different UxV, a lightweight DL model is developed to detect the garbage successfully with a higher accuracy rate. The proposed lightweight DL model uses two activation functions named MISH and rectified linear unit to enhance the feature extraction and detect the garbage. Moreover, a multiaccess edge computing (MEC) server is allocated in the proposed scheme to improve the Quality of Service (QoS) (i.e., reduce latency and improve security). Furthermore, a blockchain-based secure hazardous garbage (e.g., infectious, toxic, or radioactive materials) tracking technique is concluded in this scheme to identify the individual and reduce the potential harm to the environment. Experimental results demonstrate that the UxV can successfully detect the garbage using the proposed lightweight DL model within a minimum time frame and the obtained accuracy is higher than the other existing DL models. Besides, QoS has been investigated to verify the efficacy of the proposed scheme. Finally, a private blockchain network is established to demonstrate the performance of the proposed hazardous garbage tracking technique.