Energy-Efficient Satellite IoT Optical Downlinks Using Weather-Adaptive Reinforcement Learning

TL;DR

The paper tackles energy-efficient downlink scheduling for satellite IoT using free-space optical links that are vulnerable to weather. It proposes a weather-adaptive reinforcement learning approach based on Deep Q-Networks to select transmission contacts using forecast data, aiming to preserve delivery ratios while improving energy efficiency; a multi-agent DRL setup handles different data-volume regimes. The study compares the DRL method with simple cloud-cover thresholds and a DTN routing baseline, showing that DRL can achieve higher energy efficiency without sacrificing delivery performance, and generalizes across varying data volumes and ground-station configurations. The work also includes a realistic Ottawa-Calgary case study with historical weather data, highlighting the practical potential and flexibility of onboard, forecast-informed decision-making for optical satellite IoT downlinks.

Abstract

Internet of Things (IoT) devices have become increasingly ubiquitous with applications not only in urban areas but remote areas as well. These devices support industries such as agriculture, forestry, and resource extraction. Due to the device location being in remote areas, satellites are frequently used to collect and deliver IoT device data to customers. As these devices become increasingly advanced and numerous, the amount of data produced has rapidly increased potentially straining the ability for radio frequency (RF) downlink capacity. Free space optical communications with their wide available bandwidths and high data rates are a potential solution, but these communication systems are highly vulnerable to weather-related disruptions. This results in certain communication opportunities being inefficient in terms of the amount of data received versus the power expended. In this paper, we propose a deep reinforcement learning (DRL) method using Deep Q-Networks that takes advantage of weather condition forecasts to improve energy efficiency while delivering the same number of packets as schemes that don't factor weather into routing decisions. We compare this method with simple approaches that utilize simple cloud cover thresholds to improve energy efficiency. In testing the DRL approach provides improved median energy efficiency without a significant reduction in median delivery ratio. Simple cloud cover thresholds were also found to be effective but the thresholds with the highest energy efficiency had reduced median delivery ratio values.

Figures (7)

• Figure 1: System architecture.
• Figure 2: Delivery ratio- data volume 10 % of system capacity.
• Figure 3: Mean contact efficiency- data volume 10 % of system capacity.
• Figure 4: Delivery ratio- data volume 100 % of system capacity.
• Figure 5: Mean contact efficiency- data volume 100% of system capacity.