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

Ang Gao,Xu Li,Lixin Li,Yansu Hu

doi:10.1155/2018/1419843

Ang Gao, Xu Li + Show 2 more

Open Access

https://doi.org/10.1155/2018/1419843

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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.

Highlights

In the networked unmanned aerial vehicle (UAV) system, UAVs, known as drones, share the information and cooperate with each other by a decentralized wireless network, which enhances the performance and mission effectiveness
The vector S k = s1 k, s2 k, ... , sI k acts as the output of the controller, which can be adjusted according to the deviation of the actual traffic delay L k to the preferred quality of service (QoS) metrics Lr, that is, E k = Lr − L k, L k = l1 k, l2 k, ... , l1 k, Lr = Li, ... , LI
The BP neural network calculates the self-tuning parameters KPi, KIi, KDi based on pretrained weights, which is simultaneous trained by back propagating the derivation to weights

Summary

Introduction

In the networked unmanned aerial vehicle (UAV) system, UAVs, known as drones, share the information and cooperate with each other by a decentralized wireless network, which enhances the performance and mission effectiveness. Other resources like data rate and power allocation can be adaptive with both time-varying channel and user’s quality requirement [18] These approaches have the common feature of QoS metric feedback. For resource allocation in cloud and wireless communication, most researches commonly formulate the issue into optimization problems with the subjects of resource limitations, for example, energy consumption [18, 20, 21], bandwidth [22], financial cost [23], processing time [24], utility function [25], secrecy outage probability [26], and caching [27] The assumption of these approaches is that the resource requirements of different clients or traffic should be explicit, while the summation of total resource is beyond the limitation. They contend for channel medium in the same time duration with s slots Their QoS constraints are elastic and different with the traffic types, so the accessing probability of them should be precisely selected to balance the energy and preferred QoS. Model, which takes the utility function to quantify the “satisfaction” of traffic’s QoS [22, 25, 28]

Time Slot Isolation Adaption for HQ Ensurance

Accessing Probability Adaption for SQ Ensurance

Experiment and Results

Conclusion