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.

翻译：工业物联网（IIoT）与网络切片（NS）范式被视为工业4.0中实现灵活智能制造的关键使能技术，在此场景下，海量互联的机器、传感器及具有多样化服务质量（QoS）需求的设备共存。为优化网络资源利用，IIoT网络中的利益相关者被鼓励采取务实措施实现资源共享。然而，只有参与方能够在共赢情境下通过公平资源交换获取报酬时，资源共享才具有吸引力。本文设计了一个经济模型，分析IIoT网络中切片租户间的多边战略交易互动。我们将卖方与买方租户的资源定价与购买问题建模为合作斯塔克尔伯格博弈。具体而言，合作博弈通过联盟形成机制强制买方租户间协作，使其在资源价格谈判中相比独立行动时更具优势，而斯塔克尔伯格博弈则决定卖方租户与买方租户联盟的最优策略优化。为达到斯塔克尔伯格均衡（SE），我们提出了一种多智能体深度强化学习（MADRL）方法，该方法无需环境先验知识即可做出灵活的定价与购买决策。仿真结果与分析证明，所提方法在效用最大化方面实现收敛且优于其他基线方法。