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Journal of Sensors
Volume 2018, Article ID 1419843, 14 pages
https://doi.org/10.1155/2018/1419843
Research Article

BP Network Control for Resource Allocation and QoS Ensurance in UAV Cloud

1School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China
2School of Electronics and Control, Chang’an University, Xi’an 710064, China

Correspondence should be addressed to Ang Gao; nc.ude.upwn@gnaoag

Received 3 November 2017; Accepted 24 January 2018; Published 10 April 2018

Academic Editor: Raymond Swartz

Copyright © 2018 Ang Gao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Unmanned aerial vehicle (UAV) cloud can greatly enhance the intelligence of unmanned systems by dynamically unloading the compute-intensive applications to cloud. For the uncertain nature of UAV missions and fast-changing environment, different UAV applications may have different quality of service (QoS) requirements. This paper proposes a mixed QoS ensurance and energy-balanced (MQEB) architecture for UAV cloud from a view of control theory, which can support both hard and soft QoS ensurance with the consideration of energy saving. The hard and soft QoS requirements are decoupled by being normalized into a two-level cascaded feedback loop. The former is time slot loop (TS-Loop) to enforce the absolute QoS ensurance for real-time applications, and the latter is contention window loop (CW-Loop) to enforce the plastic QoS ensurance for non-real-time applications. Finally, the back propagating (BP) neuron network is used for parameters’ self-tuning and controller design. The hardware experiments demonstrate the feasibility of MQEB. In heavy load, MQEB has greater throughput and better energy efficiency, and in light load, MQBE has lower total power consumption.