The convergence of the Internet of Things (IoT) and the metaverse creates systems where a Digital Twin (DT) sits between physical and virtual worlds. Keeping them in sync is hard when actions arrive at the same time with different priorities and delays. We present a DT template that supports two-way, asynchronous interactions and a stack of three policies: confirm matching intents, resolve conflicts with context (domain weights and timeliness), and rollback on invariant violations. We validate the approach in a smart building with DTs representing doors, windows, and lights operating within policy domains that include security, comfort, and energy efficiency. Without policies, conflicting windows already reach 33-56% with only two users for different request rates. With the policy stack, the share of conflicts resolved by policy increases with the reliability gap and can approach all the conflicts, reducing rollbacks. Finally, under asymmetric placement, by simulating the DT at different points in the network (e.g., edge close to physical devices and cloud close to the metaverse), rollbacks shift to the slower side but leave the overall resolution essentially unchanged.
Bridging IoT and the Metaverse: Policies for the Synchronization of Digital Twins
Marche C.;Nitti M.;Atzori L.;Porcu S.
2026-01-01
Abstract
The convergence of the Internet of Things (IoT) and the metaverse creates systems where a Digital Twin (DT) sits between physical and virtual worlds. Keeping them in sync is hard when actions arrive at the same time with different priorities and delays. We present a DT template that supports two-way, asynchronous interactions and a stack of three policies: confirm matching intents, resolve conflicts with context (domain weights and timeliness), and rollback on invariant violations. We validate the approach in a smart building with DTs representing doors, windows, and lights operating within policy domains that include security, comfort, and energy efficiency. Without policies, conflicting windows already reach 33-56% with only two users for different request rates. With the policy stack, the share of conflicts resolved by policy increases with the reliability gap and can approach all the conflicts, reducing rollbacks. Finally, under asymmetric placement, by simulating the DT at different points in the network (e.g., edge close to physical devices and cloud close to the metaverse), rollbacks shift to the slower side but leave the overall resolution essentially unchanged.
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