IoT-Inspired Smart Theft Control Framework for Logistic Industry

Smart logistics industry leverages advanced software and hardware systems to enable efficient transmission. The incorporation of smart technologies, including digital twin (DT) and blockchain assesses vulnerabilities in the logistics industry, making them effective for physical attacks by users for stealing and theft control. DT persists a transformative potential in optimizing industrial operations. By bridging the physical and digital worlds, they enable real-time monitoring, predictive analytics, and enhanced decision making, driving innovations in efficiency, security, and sustainability. Conspicuously, the primary objective is to propose an effective logistic monitoring system for ensuring automated theft control. Specifically, the proposed model determines the logistic transmission patterns through secure surveillance using Internet of Things-empowered blockchain technology. Moreover, the deep learning technique of a bi-directional convolutional neural network is used to assess theft and stealing vulnerability by users in real-time for optimal decision making. The proposed approach has been demonstrated to enable accurate real-time analysis of vulnerable behavior. Based on the experimental simulations, the suggested solution effectively facilitates the development of superior logistic monitoring. The performance of the proposed system is evaluated using several statistical metrics, including latency rate (26.15 s), data processing cost, prediction efficiency (accuracy (96.12%), specificity (97.53%), and F-measure (97.25%), reliability (93.34%), and stability (0.74).