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

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.

Autoconfiguração de rotas em redes ad-hoc de VANTs

“…Em [Singh and Verma 2015] os autores realizam simulac ¸ões do protocolo OLSR em FANET com diferentes modelos de movimentac ¸ão e mobilidade e concluem que o protocolo precisa ser melhorado para atender a essas redes, entretanto sem apontar os pontos chaves de melhora. Pesquisas com o propósito de melhorar as FANETs estão disponíveis em trabalhos como em [Chen et al 2019], [Sliwa et al 2019], [Singh and Verma 2015] e [Gao et al 2018]. Contudo, o foco desses trabalhos é na melhoria de aspectos específicos dos protocolos como reduc ¸ão de overhead na disseminac ¸ão de pacotes de controle e reduc ¸ão de latência e não em analisar por quê esses protocolos de roteamento não atendem às demandas das FANETs.…”

An Adaptive Task Scheduling Method for Networked UAV Combat Cloud System Based on Virtual Machine and Task Migration

“…In the relevant research field about UAV cloud systems, literature [9] proposed a mixed QoS (quality of service) reassurance and energy-balanced architecture for UAV cloud system from a view of control theory. is UAV cloud system architecture intended to address the limitations of high complexity of UAV cluster and low intelligence of individual UAVs.…”

The Lyapunov Optimization for Two-Tier Hierarchical-Based MAC in Cloud Robotics

“…It adopts a linear model for system identification which is not precisely enough and without taking energy consumption into consideration. On this basis, CSFD-MAC [9] proposes a cascade selftuning architecture as well as active power management, and MQEB-MAC [10] adopts a back propagating (BP) neural network to develop an all-in-one mixed QoS 2 Wireless Communications and Mobile Computing insurance and energy-balanced scheme. Although the algorithms above are verified to be valid by experiments, they still have the following limitations: Firstly, they only focus on the difference in frames but neglect the heterogeneity of the robots themselves.…”