  The industrial Internet of Things (IIoT) and network slicing (NS) paradigms
have been envisioned as key enablers for flexible and intelligent manufacturing
in the industry 4.0, where a myriad of interconnected machines, sensors, and
devices of diversified quality of service (QoS) requirements coexist. To
optimize network resource usage, stakeholders in the IIoT network are
encouraged to take pragmatic steps towards resource sharing. However, resource
sharing is only attractive if the entities involved are able to settle on a
fair exchange of resource for remuneration in a win-win situation. In this
paper, we design an economic model that analyzes the multilateral strategic
trading interactions between sliced tenants in IIoT networks. We formulate the
resource pricing and purchasing problem of the seller and buyer tenants as a
cooperative Stackelberg game. Particularly, the cooperative game enforces
collaboration among the buyer tenants by coalition formation in order to
strengthen their position in resource price negotiations as opposed to acting
individually, while the Stackelberg game determines the optimal policy
optimization of the seller tenants and buyer tenant coalitions. To achieve a
Stackelberg equilibrium (SE), a multi-agent deep reinforcement learning (MADRL)
method is developed to make flexible pricing and purchasing decisions without
prior knowledge of the environment. Simulation results and analysis prove that
the proposed method achieves convergence and is superior to other baselines, in
terms of utility maximization.
