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.

翻译：在智能物联网（IoT）系统中，网络内的边缘服务器与邻居交换信息并从传感器收集数据以完成交付的任务。本文提出了一种面向智能IoT系统的多人多臂赌博机模型，以促进数据收集并纳入公平性考量。在该模型中，我们建立了一种有效的通信协议，帮助服务器与邻居协同工作。随后，我们设计了一种分布式协作赌博机算法DC-ULCB，使服务器能够协作选择传感器，以最大化数据速率同时保持选择的公平性。我们对DC-ULCB的奖励遗憾和公平遗憾进行了分析，并证明两种遗憾均具有对数阶实例相关的上界。此外，通过广泛仿真，我们验证了DC-ULCB在最大化奖励和保障公平性方面优于现有算法。