This work analyzes the effect of mobility entropy in real-time representation of human positioning in digital twin applications. Real-time representation of positioning data is challenging due to the energy constraints of IoT devices, inducing a tradeoff between representation accuracy in digital twin applications and energy efficiency in real-world IoT devices. We consider the utilization of mobility entropy, which reflects the dynamics of a movement, to leverage the tradeoff for more efficient management of both IoT and digital twins. Our preliminary analysis shows that our proposed calculation of mobility entropy can accurately capture mobility characteristics and suggests that this value can be used future methods of enhancing and optimizing accuracy and energy efficiency.
