In intelligent Internet of Things (IoT) systems, edge servers within a
network exchange information with their neighbors and collect data from sensors
to complete delivered tasks. In this paper, we propose a multiplayer
multi-armed bandit model for intelligent IoT systems to facilitate data
collection and incorporate fairness considerations. In our model, we establish
an effective communication protocol that helps servers cooperate with their
neighbors. Then we design a distributed cooperative bandit algorithm, DC-ULCB,
enabling servers to collaboratively select sensors to maximize data rates while
maintaining fairness in their choices. We conduct an analysis of the reward
regret and fairness regret of DC-ULCB, and prove that both regrets have
logarithmic instance-dependent upper bounds. Additionally, through extensive
simulations, we validate that DC-ULCB outperforms existing algorithms in
maximizing reward and ensuring fairness.

该论文提出了一种用于智能物联网系统的多人多臂赌博模型，以促进数据收集并纳入公平考虑，通过设计一种分布式合作赌博算法，有效地帮助服务器与其邻居合作选择传感器，最大化数据速率同时保持公平，在奖励和公平方面，实验证明该算法优于现有算法。