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Efficient Learning-based Scheduling for Information Freshness in Wireless Networks
Performance · Submitted 1 Jan 2021
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim This reveals a tradeoff between the cumulative regret and the running average total age: when increasing $\eta$, the cumulative regret becomes smaller, but is at the cost of increasing running average total age.
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The abstract, sentence by sentence
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 $\eta$. 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 $O(N^2\eta)$. We also prove that our proposed algorithm achieves the cumulative regret over time horizon $T$ at most by $O(NT/\eta+\sqrt{NT\log T})$. This reveals a tradeoff between the cumulative regret and the running average total age: when increasing $\eta$, 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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | 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. | background 0.99 | 0.24 | 0.02 | asserted 0.79 |
| s1 | Here, packets from different sensing sources have different values or degrees of importance to the central controller for intelligent decision making. | background 1.00 | 0.08 | 0.01 | asserted 1.00 |
| s2 | In such a setup, it is critical to provide timely and valuable information for the central controller. | background 0.99 | 0.04 | 0.02 | asserted 1.00 |
| s3 | 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 $\eta$. | method 0.80 | 0.95 | 0.10 | asserted 1.00 |
| s4 | Here, UCB estimates balance the tradeoff between exploration and exploitation in learning and are critical for yielding a small cumulative regret. | method 0.48 | 0.12 | 0.04 | asserted 1.00 |
| s5 | We show that our proposed algorithm yields the running average total age at most by $O(N^2\eta)$. | result 1.00 | 0.96 | 0.24 | asserted 1.00 |
| s6 | We also prove that our proposed algorithm achieves the cumulative regret over time horizon $T$ at most by $O(NT/\eta+\sqrt{NT\log T})$. | result 1.00 | 0.96 | 0.20 | asserted 1.00 |
| s7 | This reveals a tradeoff between the cumulative regret and the running average total age: when increasing $\eta$, the cumulative regret becomes smaller, but is at the cost of increasing running average total age. | interpretation 0.99 | 0.92 | 0.50 | asserted 0.98 |
| s8 | Simulation results are provided to evaluate the efficiency of our proposed algorithm. | method 0.40 | 0.93 | 0.03 | asserted 0.76 |
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Original abstract
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 $\eta$. 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 $O(N^2\eta)$. We also prove that our proposed algorithm achieves the cumulative regret over time horizon $T$ at most by $O(NT/\eta+\sqrt{NT\log T})$. This reveals a tradeoff between the cumulative regret and the running average total age: when increasing $\eta$, 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.
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1 arXiv paper cite this in 1 sentence
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The derived upper bound on the cumulative regret consists of two terms: (i) $2√(6N|S|_maxTlogT)+N(1+5π^2/12)$ has the same order $O(√(NTlogT))$ as the instance-independent upper bound for the classical UCB algorithm (see [ref] ) and thus this term is attributed to the cost involved in the exploration/exploitation process in online learning; (ii) $NT/η$ decreases as parameter $η$ increases.
Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems · Algorithm Design and Performance Analysis · 0.98
Cites as background 2 papers
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This key difference has spurred AoI research in several aspects in recent years, e.g., AoI analysis and optimization (e.g., [ref] ), AoI in vehicular networks (e.g., [ref] ), online sampling and remote estimation (e.g., [ref] ), AoI and energy harvesting (e.g., [ref] ), just to name a few.
Sampling and Remote Estimation for the Ornstein-Uhlenbeck Process through Queues: Age of Information and Beyond · Related Work and Context · 0.86
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This key difference has spurred AoI research in several aspects in recent years, e.g., AoI analysis and optimization (e.g., [ref] ), AoI in vehicular networks (e.g., [ref] ), online sampling and remote estimation (e.g., [ref] ), AoI and energy harvesting (e.g., [ref] ), just to name a few.
Experimental Analysis of Safety Application Reliability in V2V Networks · Related Work and Context · 0.86
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