Efficient Learning-based Scheduling for Information Freshness in Wireless Networks
This work is significant for IoT network operators and designers seeking to optimize information freshness and decision-making in wireless networks with diverse sensing sources, offering an incremental improvement in scheduling policy.
This paper addresses the problem of scheduling packets from multiple IoT sensing sources to a central controller over a wireless network, where sources have varying importance. The authors propose a parameterized maximum-weight scheduling policy that combines Age of Information (AoI) and Upper Confidence Bound (UCB) estimates, demonstrating a running average total age of O(N^2η) and a cumulative regret of O(NT/η + sqrt(NT log T)).
Motivated by the recent trend of integrating artificial intelligence into the Internet-of-Things (IoT), we consider the problem of scheduling packets from multiple sensing sources to a central controller over a wireless network. Here, packets from different sensing sources have different values or degrees of importance to the central controller for intelligent decision making. In such a setup, it is critical to provide timely and valuable information for the central controller. In this paper, we develop a parameterized maximum-weight type scheduling policy that combines both the AoI metrics and Upper Confidence Bound (UCB) estimates in its weight measure with parameter $η$. Here, UCB estimates balance the tradeoff between exploration and exploitation in learning and are critical for yielding a small cumulative regret. We show that our proposed algorithm yields the running average total age at most by $O(N^2η)$. We also prove that our proposed algorithm achieves the cumulative regret over time horizon $T$ at most by $O(NT/η+\sqrt{NT\log T})$. This reveals a tradeoff between the cumulative regret and the running average total age: when increasing $η$, the cumulative regret becomes smaller, but is at the cost of increasing running average total age. Simulation results are provided to evaluate the efficiency of our proposed algorithm.