Low-Power Status Updates via Sleep-Wake Scheduling
Ahmed M. Bedewy, Yin Sun, Rahul Singh, and Ness B. Shroff

TL;DR
This paper develops sleep-wake scheduling strategies for low-power sources to optimize information freshness and battery life, using convex optimization and reinforcement learning, with proven near-optimality and practical performance benefits.
Contribution
It introduces a low-complexity sleep-wake design for minimizing AoI under battery constraints and a reinforcement learning approach for unknown transmission times.
Findings
Solution extends battery lifetime while maintaining low AoI.
Proposed algorithms are near-optimal for practical sensing times.
Reinforcement learning effectively adapts to unknown transmission parameters.
Abstract
We consider the problem of optimizing the freshness of status updates that are sent from a large number of low-power sources to a common access point. The source nodes utilize carrier sensing to reduce collisions and adopt an asynchronized sleep-wake scheduling strategy to achieve a target network lifetime (e.g., 10 years). We use age of information (AoI) to measure the freshness of status updates, and design sleep-wake parameters for minimizing the weighted-sum peak AoI of the sources, subject to per-source battery lifetime constraints. When the sensing time (i.e., the time duration of carrier sensing) is zero, this sleep-wake design problem can be solved by resorting to a two-layer nested convex optimization procedure; however, for positive sensing times, the problem is non-convex. We devise a low-complexity solution to solve this problem and prove that, for practical sensing times…
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