Satellite Edge Computing for the Internet of Things in Aerospace

Area of Science:

• Computer Science
• Aerospace Engineering
• Network Engineering

Background:

• The Internet of Things (IoT) is a key future direction for the information industry, facing network pressure due to long distances between processing platforms and terminals.
• Edge computing offers a new paradigm for IoT, but many IoT devices are in remote locations inaccessible via terrestrial networks, necessitating satellite communication.
• Traditional satellites have limitations: highly customized, with on-board resources designed for specific applications, not universal computing.

Purpose of the Study:

• To propose and investigate the transformation of traditional satellites into space edge computing nodes.
• To enable dynamic software loading, flexible resource sharing, and cloud-coordinated services in orbit.
• To evaluate the performance of this space edge computing system against traditional satellite constellations.

Main Methods:

• Presentation of the hardware and software architecture for the space edge computing satellite.
• Modeling analysis and simulation experiments based on application scenarios.
• Evaluation of system performance metrics including time, energy consumption, and quality of service.

Main Results:

• The proposed space edge computing system demonstrates reduced processing time compared to traditional satellite constellations.
• The system exhibits lower energy consumption in comparison to traditional satellite constellation approaches.
• Quality of service is found to be dependent on the number of satellites, individual satellite performance, and task offloading strategies.

Conclusions:

• Transforming satellites into space edge computing nodes offers a more efficient and flexible solution for remote IoT applications.
• The developed system provides a viable alternative to traditional satellite constellations, optimizing resource utilization and service delivery.
• Future research should focus on optimizing satellite configurations and task offloading strategies to further enhance quality of service.