Age of Twin (AoT): A New Digital Twin Qualifier for 6G Ecosystem

With the enhanced zero-touch operation and service management capabilities of digital twin technology, network management authorities have started implementing digital twin modeling. They achieve descriptive, predictive, and prescriptive twinning with rule-based replication and pre-defined synchronization mechanisms. However, it is not possible to reach out to the extreme needs of the sixth generation (6G) services, such as ultra-high data density (uHDD) and event-defined ultra-reliable low latency communication (EDuRLLC) services, with these traditional twin modeling methods. This is because these 6G services require cognitive abilities to manage twin-to-twin interactions by enabling extreme connectivity. For this reason, we propose a new digital twin modeling qualifier, age of twin (AoT), to measure the digital twin data freshness, especially to use in 6G deployments. In AoT formation, we consider device density, packet deadlines, link capacity, and buffer size metrics by relating to the three V of big data characteristics; velocity, volume, and variety. Besides, we form an AoT umbrella to cover topology-wise, service-type-wise, and traffic-type-wise digital twin modeling needs. We perform the AoT-based twin modeling for each AoT class and converge to an AoT value. According to the results, the high twinning rate contributes to increased data freshness and, thus, near-zero AoT value.