Low-Power Status Updates via Sleep-Wake Scheduling

• Published in: IEEE/ACM transactions on networking Vol. 29; no. 5; pp. 2129 - 2141
• Main Authors: Bedewy, Ahmed M., Sun, Yin, Singh, Rahul, Shroff, Ness B.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Access control; Age of information; Algorithms; Batteries; certainty equivalent; Computational geometry; Convexity; data freshness; Decision analysis; Design parameters; Freshness; Information sources; lifetime; low-power; Machine learning; Monitoring; Optimization; Packet transmission; Power management; Power sources; Random variables; Scheduling; Sensors; Sleep; sleep-wake scheduler; Wireless networks; Wireless sensor networks
• ISSN: 1063-6692; 1558-2566
• DOI: 10.1109/TNET.2021.3081102

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 that are short, the solution is within a small gap from the optimum AoI performance. When the mean transmission time of status-update packets is unknown, we devise a reinforcement learning algorithm that adaptively performs the following two tasks in an "efficient way": a) it learns the unknown parameter, b) it also generates efficient controls that make channel access decisions. We analyze its performance by quantifying its "regret", i.e., the sub-optimality gap between its average performance and the average performance of a controller that knows the mean transmission time. Our numerical and NS-3 simulation results show that our solution can indeed elongate the batteries lifetime of information sources, while providing a competitive AoI performance.