IEEE Project Abstract

As a critical supplementary to terrestrial communication networks, low-earth-orbit (LEO) satellite based communicationnetworks have been gaining growing attention in recent years. In this paper, we focus on data collection from geo-distributedInternet-of-Things (IoT) networks via LEO satellites. Normally, the power supply in IoT data-gathering gateways is a bottleneckresource that constrains the overall amount of data upload. Thus, the challenge is how to collect the data from IoT gateways throughLEO satellites under time-varying uplinks in an energy-efficient way. To address this problem, we first formulate a novel optimizationproblem, and then propose an online algorithm based on Lyapunov optimization theory to aid green data-upload for geo-distributed IoTnetworks. The proposed approach is to jointly maximize the overall amount of data uploaded and minimize the energy consumption,while maintaining the queue stability even without the knowledge of arrival data at IoT gateways. We finally evaluate the performance ofthe proposed algorithm through simulations using both real-world and synthetic data traces. Simulation results demonstrate that theproposed approach can achieve high efficiency on energy consumption and significantly reduce queue backlogs compared with anoffline formulation and a greedy Big-Backlog-First algorithm.