Inter-Twin Connectivity for Digital Twin Networks in Secure Contactless Delivery Service Scenarios

Along with the advent of COVID-19 epidemic, delivery services have become one of the most common services in our daily life. However, the current services merely assume that the contact for handing over delivery items is denied to avoid facing between carriers and receivers. This extreme concept might lead to the ruin of typical processes for delivery services, such as face-to-face authentication, item checking, and payment in person. So, it may cause insufficiency regarding both sides of the secure item delivery and customers’ personal security. Therefore, this paper comes up with a novel CONTACTFUL delivery service with the digital twin paradigm to resolve contactless problems. The proposed idea establishes two-tier digital twin architecture to arrange digital twins and physical twins of carriers and receivers. Then, inter-digital-twin interaction virtually supports on-site delivery service processes. The inter-twin interaction relies on highly accurate proximity detection for items, carriers, and receivers. So, the proposed scheme considers machine-learning technologies and filtering algorithms to achieve high precision based on mobile-based Bluetooth low energy (BLE) beaconing. This scheme is implemented as a proof-of-concept prototype, and various field data are exploited to build ML-filtering models including their ensemble one. On the steel door, the ensemble model shows an accuracy improvement of 65% compared to SVM, 14% to CNN, and 73% to MLP. On the wood door, the ensemble model shows an accuracy improvement of 21% compared to SVM, 9% to CNN, and 17% to MLP.