The Satellite Internet of Things (S-IoT) enables global connectivity for remote sensing devices that must operate energy-efficiently over long time spans. We consider an S-IoT system consisting of a sender-receiver pair connected by a data channel and a feedback channel and capture its dynamics using a Markov Decision Process (MDP). To extend battery life, the sender has to decide on deep-sleep durations. Deep-sleep scheduling is the primary lever to reduce energy consumption, since sleeping devices consume only a fraction of their idle power. By choosing its deep-sleep duration online, the sender has to find a trade-off between energy consumption and data quality degradation at the receiver, captured by a weighted sum of costs. We quantify data quality degradation via the recently introduced Goal-Oriented Tensor (GoT) metric, which can take both age and content of delivered data into account. We assume a Markovian observed process and Markov channels with time-varying delay and erasure rates. The challenge is that content awareness of the GoT metric makes periodic transmissions inherently inefficient. Additionally, optimal sleep durations depends on the (unknown) future states of the observed process and the channels, both of which must be inferred online. We propose a novel algorithm using probabilistic simulation-based optimization (PSBO). With PSBO, the sensor forecasts future states based on estimated transition probabilities, and uses these forecasts to select the optimal deep-sleep duration. Extensive simulations and experiments with S-IoT hardware demonstrate superior performance of PSBO under diverse conditions.

翻译：卫星物联网（S-IoT）为需在长时间跨度内高效运行的遥感设备提供了全球连接能力。我们考虑一个由数据信道和反馈信道连接的收发对组成的S-IoT系统，并使用马尔可夫决策过程（MDP）刻画其动态特性。为延长电池寿命，发送端需决策深度休眠时长。深度休眠调度是降低能耗的主要手段，因为休眠设备仅消耗空闲功率的一小部分。通过在线选择深度休眠时长，发送端必须在能耗与接收端数据质量下降之间寻求平衡，该平衡通过加权成本总和来表征。我们采用近期提出的目标导向张量（GoT）度量来量化数据质量下降，该度量可同时考虑传输数据的时效性与内容价值。我们假设观测过程为马尔可夫过程，信道为具有时变延迟和擦除率的马尔可夫信道。核心挑战在于：GoT度量的内容感知特性使得周期性传输本质上效率低下。此外，最优休眠时长取决于观测过程和信道（两者均需在线推断）的未知未来状态。我们提出一种基于概率仿真优化（PSBO）的新算法。通过PSBO，传感器基于估计的转移概率预测未来状态，并利用这些预测选择最优深度休眠时长。在S-IoT硬件上开展的广泛仿真与实验表明，PSBO在多样化条件下均表现出优越性能。