Green Data-Collection from Geo-Distributed IoT Networks through Low-Earth-Orbit Satellites

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

57 Scopus Citations
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Author(s)

  • Huawei Huang
  • Song Guo
  • Weifa Liang
  • Kun Wang
  • Albert Y. Zomaya

Detail(s)

Original languageEnglish
Article number8681409
Pages (from-to)806-816
Journal / PublicationIEEE Transactions on Green Communications and Networking
Volume3
Issue number3
Online published4 Apr 2019
Publication statusPublished - Sept 2019
Externally publishedYes

Abstract

As a critical supplementary to terrestrial communication networks, low-Earth-orbit (LEO) satellite-based communication networks have been gaining growing attention in recent years. In this paper, we focus on data collection from geo-distributed Internet-of-Things (IoT) networks via LEO satellites. Normally, the power supply in IoT data-gathering gateways is a bottleneck resource that constrains the overall amount of data upload. Thus, the challenge is how to collect the data from IoT gateways through LEO satellites under time-varying uplinks in an energy-efficient way. To address this problem, we first formulate a novel optimization problem, and then propose an online algorithm based on Lyapunov optimization theory to aid green data-upload for geo-distributed IoT networks. 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 of the proposed algorithm through simulations using both real-world and synthetic data traces. Simulation results demonstrate that the proposed approach can achieve high efficiency on energy consumption and significantly reduce queue backlogs compared with an offline formulation and a greedy 'Big-Backlog-First' algorithm.

Research Area(s)

  • Green data-collection, Internet-of-Things (IoT), LEO satellite

Citation Format(s)