Fair Distributed Cooperative Bandit Learning on Networks for Intelligent Internet of Things Systems (Technical Report)
This work addresses fairness and efficiency in data collection for intelligent IoT systems, representing an incremental improvement with specific gains in cooperative bandit learning.
The paper tackles the problem of data collection in intelligent IoT systems by proposing a multiplayer multi-armed bandit model with fairness considerations, resulting in a distributed cooperative algorithm (DC-ULCB) that achieves logarithmic regret bounds for both reward and fairness, and outperforms existing algorithms in simulations.
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.