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Deep Sleep Scheduling for Satellite IoT via Simulation Based Optimization

Wanja de Sombre, Monika Tomová, Marek Galinski, Anja Klein, Andrea Ortiz

TL;DR

A novel algorithm using probabilistic simulation-based optimization (PSBO) is proposed, with PSBO, the sensor forecasts future states based on estimated transition probabilities, and uses these forecasts to select the optimal deep-sleep duration.

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.

Deep Sleep Scheduling for Satellite IoT via Simulation Based Optimization

TL;DR

A novel algorithm using probabilistic simulation-based optimization (PSBO) is proposed, with PSBO, the sensor forecasts future states based on estimated transition probabilities, and uses these forecasts to select the optimal deep-sleep duration.

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.
Paper Structure (15 sections, 19 equations, 8 figures, 3 tables, 4 algorithms)

This paper contains 15 sections, 19 equations, 8 figures, 3 tables, 4 algorithms.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORK
  3. SYSTEM MODEL
  4. PROBLEM FORMULATION
  5. Probabilistic Simulation-based Optimization
  6. Belief Update
  7. Cost Prediction
  8. PSBO
  9. HARDWARE IMPLEMENTATION
  10. EVALUATION
  11. Simulation Setup
  12. Energy Measurements
  13. Simulation Results
  14. Experimental Results
  15. CONCLUSIONS

Figures (8)

  • Figure 1: System Model
  • Figure 2: Overview of the device’s operation schedule. Top: Alternation between deep-sleep and awake mode across multiple time steps. Bottom: Detailed view of a single time step.
  • Figure 3: Overview of the interaction between Alg. 1 to 4.
  • Figure 4: Physical layout of the experimental setup consisting of the control station, sensor node, and BLE gateway.
  • Figure 5: Average cost of PSBO and baselines for varying parameters ( A)
  • ...and 3 more figures

Theorems & Definitions (1)

  • Example 1