Deep Sleep Scheduling for Satellite IoT via Simulation Based Optimization

2026-02-27

AI Summary

This work addresses the challenge of online decision-making for deep-sleep duration in satellite Internet-of-Things (S-IoT) systems to balance energy consumption against data quality degradation. The authors propose a content-aware deep-sleep scheduling mechanism that models the system as a Markov decision process, uniquely integrating Goal-oriented Tensor (GoT)–based data quality metrics with a Probabilistic Simulation-Based Optimization (PSBO) algorithm. By estimating state transition probabilities to predict future system states, the approach adaptively optimizes sleep durations while explicitly accounting for time-varying channel characteristics, including delay and erasure rates. Extensive simulations and real-world experiments on S-IoT hardware demonstrate that the proposed method significantly outperforms existing baselines in achieving an optimal trade-off between energy efficiency and data quality, effectively overcoming the inefficiencies inherent in periodic transmission schemes.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

Abstract

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.

Problem

• Deep Sleep Scheduling
• Satellite IoT
• Energy Efficiency
• Data Quality Degradation
• Markov Decision Process

Innovation

• Simulation-Based Optimization
• Goal-Oriented Tensor
• Deep Sleep Scheduling
• Markov Decision Process
• Satellite IoT