Hierarchical Decision Making for Remote IoT Data Processing in Space Edge-Cloud Continuum

The Internet of Things (IoT) is one of the most important applications in a distributed edge-cloud continuum. In this study, we aim to solve the data offloading and buffering policy selection problem for the remote IoT devices to process the data over the space edge cloud continuum formed by the non-terrestrial Edge Computing (EC) facilities, deployed over Low Earth Orbit (LEO) satellites, and the terrestrial cloud infrastructure. In particular, we have developed a constrained optimization problem to minimize the joint latency and energy costs associated with the data processing operation over LEO satellite nodes and cloud facilities. The problem is then decomposed into a hierarchy of decision-making subproblems and addressed using a Hierarchical Reinforcement Learning (HRL) approach. The proposed HRL solution is then implemented within a Python-based simulator, and its performance is compared with several other benchmark solutions. Performance analysis indicates the gain in terms of latency, energy, and resource utilization with adaptive utilization of space computing resources based on the devices’ demands.