Swarm Intelligence for Enabling Human-Centric Telepresence in Citiverse Environment

Future sixth-generation networks are expected to support immersive and telepresence services in which users interact with digital environments through wearable and multisensory terminals. In dense and mobile scenarios, static access support may become insufficient, since the radio-link quality and service availability can change as the user group moves. This paper investigates a robotic network coordinator that operates as a mobile base station for a group of users wearing telepresence suits. The coordinator position is linked to the communication performance through the signal-to-interference-plus-noise ratio, the achievable transmission rate, and an M/M/1 queueing model. To support dynamic positioning, we propose a two-stage FCM–ACO method. First, fuzzy c-means clustering identifies the current user groups and determines their cluster centers. Then, ant colony optimization constructs a feasible trajectory for the mobile base station under the movement constraint, using a QoS-oriented objective that reflects packet delay and radio-link quality. Numerical results compare FCM–ACO with FCM, K-means, and K-means–ACO in terms of BS trajectory length, coverage ratio, delay, cost, and computation time. FCM–ACO achieves the shortest BS trajectory among the four evaluated configurations, reducing the distance traveled by approximately one third relative to FCM, nearly one half relative to K-means, and approximately 9% relative to K-means–ACO. This reduction comes with tradeoffs in coverage, delay, and computation time.