Digital Twin for Continual Learning in Location Based Services

Decoupling the physical world and providing standardized service interfaces is still challenging when devel-oping Location Based Services (LBS). This lack also hinders the possibility of developing Intelligent services on top of LBS architectures. In this paper, we propose a multi-layer Digital Twin-based architecture that aims to enable the development of machine learning-based Intelligent LBS (I-LBS) that are able to adapt, evolve, and perform Continual Learning (CL). The platform uses Digital Twins to ensure physical abstraction and provide cyber-physical knowledge to the I-LBSs, which is defined as an execution graph of operation modules. Finally, we simulated a use-case for this platform in the complex scenario of Healthcare organization and management where the I-LBS classifies allowed/not allowed trajectories of users inside a real-existing hospital scenario depending on their role in the organization. The use case is implemented as a Deep Learning-based reconstruction task of high-resolution trajectories processed by the DT architecture that also deploys the I-LBS. The platform is evaluated in terms of physical complexity and computational time on the DT side and on both a traditional machine learning setting and a replay-based CL one for the intelligence side to demonstrate the flexibility and adaptability features introduced by the components for dynamic or unseen scenarios.