4.6 Article

Energy Efficient Resource Allocation for UAV-Assisted Space-Air-Ground Internet of Remote Things Networks

Journal

IEEE ACCESS
Volume 7, Issue -, Pages 145348-145362

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2945478

Keywords

Energy efficiency; SAG-IoRT networks; UAV relays; Lagrangian dual decomposition; SCA

Funding

  1. State Key Laboratory of Networking and Switching Technology
  2. China Academy of Space Technology

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Internet of remote things (IoRT) networks are regarded as an effective approach for providing services to smart devices, which are often remote and dispersed over in a wide area. Due to the fact that the ground base station deployment is difficult and the power consumption of smart devices is limited in IoRT networks, the hierarchical Space-Air-Ground architecture is very essential for these scenarios. This paper aims to investigate energy efficient resource allocation problem in a two-hop uplink communication for Space-Air-Ground Internet of remote things (SAG-IoRT) networks assisted with unmanned aerial vehicle (UAV) relays. In particular, the optimization goal of this paper is to maximize the system energy efficiency by jointly optimizing sub-channel selection, uplink transmission power control and UAV relays deployment. The optimization problem is a mix-integer non-linear non-convex programming, which is hard to tackle. Therefore, an iterative algorithm that combines two sub-problems is proposed to solve it. First, given UAV relays deployment position, the optimal sub-channel selection and power control policy are obtained by the Lagrangian dual decomposition method. Next, based on the obtained sub-channel allocation and power control policy, UAV relays deployment is obtained by successive convex approximation (SCA). These two sub-problems are alternatively optimized to obtain the maximum system energy efficiency. Numerical results verify that the proposed algorithm has at least 21.9% gain in system energy efficiency compared to the other benchmark scheme.

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