  The surging demand for fresh information from various Internet of Things
(IoT) applications requires oceans of data sampled from the physical
environment to be transmitted and processed timely, which would lead to huge
energy consumption. We investigate the sleep-wake strategies of servers in
communication systems to reduce energy consumption while guaranteeing timely
delivery of fresh information to users. Specifically, we investigate a
multi-source single-server queueing system and propose a novel sleep-wake
strategy called the Conditional Sleep (CS) scheme. Our analysis reveals that
the CS scheme outperforms the widely-used Hysteresis Time (HT) and Bernoulli
Sleep (BS) schemes in terms of Age of Information (AoI), while retaining the
same energy consumption rate and Peak Age of Information (PAoI). We find that
increasing the sleep period length leads to a reduction in energy consumption
and an increase in PAoI, but it does not always increase AoI. Moreover, we show
that using PAoI as the information freshness metric in designing sleep-wake
strategies would make the server sleep infinitely long due to the PAoI being
determined by first-order statistics. We further numerically show that having
the bufferless system can achieve a better PAoI-energy tradeoff than the
infinite buffer system when having a large sampling rate.
