  This paper addresses the problem of minimizing latency with partial
computation offloading within Industrial Internet-of-Things (IoT) systems in
in-network computing (COIN)-assisted Multiaccess Edge Computing (C-MEC) via
ultra-reliable and low latency communications (URLLC) links. We propose a
digital twin (DT) scheme for a multiuser scenario, allowing collaborative
partial task offloading from user equipment (UE) to COIN-aided nodes or MEC.
Specifically, we formulate the problem as joint task offloading decision, ratio
and resource allocation. We employ game theory to create a low-complexity
distributed offloading scheme in which the task offloading decision problem is
modelled as an exact potential game. Double Deep Q-Network (DDQN) is utilized
within the game to proactively predict optimal offloading ratio and resource
allocation. This approach optimizes resource allocation across the whole system
and enhances the robustness of the computing framework, ensuring efficient
execution of computation-intensive services. Additionally, it addresses
centralized approaches and UE resource contention issues, thus ensuring faster
and more reliable communication.
