Deep Reinforcement Learning for Delay-Oriented IoT Task Scheduling in SAGIN

In this article, we investigate a computing task scheduling problem in space-air-ground integrated network (SAGIN) for delay-oriented Internet of Things (IoT) services. In the considered scenario, an unmanned aerial vehicle (UAV) collects computing tasks from IoT devices and then makes online offloa...

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Vydáno v:	IEEE transactions on wireless communications Ročník 20; číslo 2; s. 911 - 925
Hlavní autoři:	Zhou, Conghao, Wu, Wen, He, Hongli, Yang, Peng, Lyu, Feng, Cheng, Nan, Shen, Xuemin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.02.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Computation Computer aided scheduling constrained MDP Constraints Delay Delays edge computing Energy consumption Heuristic algorithms Internet of Things IoT Machine learning Markov processes Optimization Processor scheduling reinforcement learning Risk Scheduling Space-air-ground integrated network Task analysis Task scheduling Unmanned aerial vehicles US Department of Transportation
ISSN:	1536-1276, 1558-2248
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Abstract	In this article, we investigate a computing task scheduling problem in space-air-ground integrated network (SAGIN) for delay-oriented Internet of Things (IoT) services. In the considered scenario, an unmanned aerial vehicle (UAV) collects computing tasks from IoT devices and then makes online offloading decisions, in which the tasks can be processed at the UAV or offloaded to the nearby base station or the remote satellite. Our objective is to design a task scheduling policy that minimizes offloading and computing delay of all tasks given the UAV energy capacity constraint. To this end, we first formulate the online scheduling problem as an energy-constrained Markov decision process (MDP). Then, considering the task arrival dynamics, we develop a novel deep risk-sensitive reinforcement learning algorithm. Specifically, the algorithm evaluates the risk, which measures the energy consumption that exceeds the constraint, for each state and searches the optimal parameter weighing the minimization of delay and risk while learning the optimal policy. Extensive simulation results demonstrate that the proposed algorithm can reduce the task processing delay by up to 30% compared to probabilistic configuration methods while satisfying the UAV energy capacity constraint.
AbstractList	In this article, we investigate a computing task scheduling problem in space-air-ground integrated network (SAGIN) for delay-oriented Internet of Things (IoT) services. In the considered scenario, an unmanned aerial vehicle (UAV) collects computing tasks from IoT devices and then makes online offloading decisions, in which the tasks can be processed at the UAV or offloaded to the nearby base station or the remote satellite. Our objective is to design a task scheduling policy that minimizes offloading and computing delay of all tasks given the UAV energy capacity constraint. To this end, we first formulate the online scheduling problem as an energy-constrained Markov decision process (MDP). Then, considering the task arrival dynamics, we develop a novel deep risk-sensitive reinforcement learning algorithm. Specifically, the algorithm evaluates the risk, which measures the energy consumption that exceeds the constraint, for each state and searches the optimal parameter weighing the minimization of delay and risk while learning the optimal policy. Extensive simulation results demonstrate that the proposed algorithm can reduce the task processing delay by up to 30% compared to probabilistic configuration methods while satisfying the UAV energy capacity constraint.
Author	Zhou, Conghao He, Hongli Yang, Peng Shen, Xuemin Lyu, Feng Cheng, Nan Wu, Wen
Author_xml	– sequence: 1 givenname: Conghao orcidid: 0000-0002-5727-2432 surname: Zhou fullname: Zhou, Conghao email: c89zhou@uwaterloo.ca organization: Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada – sequence: 2 givenname: Wen orcidid: 0000-0002-0458-1282 surname: Wu fullname: Wu, Wen email: w77wu@uwaterloo.ca organization: Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada – sequence: 3 givenname: Hongli orcidid: 0000-0002-1283-2168 surname: He fullname: He, Hongli email: hongli_he@zju.edu.cn organization: School of Information Engineering, Zhejiang University, Hangzhou, China – sequence: 4 givenname: Peng orcidid: 0000-0001-8964-0597 surname: Yang fullname: Yang, Peng email: yangpeng@hust.edu.cn organization: School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China – sequence: 5 givenname: Feng orcidid: 0000-0002-2990-5415 surname: Lyu fullname: Lyu, Feng email: fenglyu@csu.edu.cn organization: School of Computer Science and Engineering, Central South University, Changsha, China – sequence: 6 givenname: Nan orcidid: 0000-0001-7907-2071 surname: Cheng fullname: Cheng, Nan email: nancheng@xidian.edu.cn organization: Key State Laboratory of ISN, Xidian University, Xi'an, China – sequence: 7 givenname: Xuemin orcidid: 0000-0002-4140-287X surname: Shen fullname: Shen, Xuemin email: sshen@uwaterloo.ca organization: Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada
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Snippet	In this article, we investigate a computing task scheduling problem in space-air-ground integrated network (SAGIN) for delay-oriented Internet of Things (IoT)...
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SubjectTerms	Algorithms Computation Computer aided scheduling constrained MDP Constraints Delay Delays edge computing Energy consumption Heuristic algorithms Internet of Things IoT Machine learning Markov processes Optimization Processor scheduling reinforcement learning Risk Scheduling Space-air-ground integrated network Task analysis Task scheduling Unmanned aerial vehicles US Department of Transportation
Title	Deep Reinforcement Learning for Delay-Oriented IoT Task Scheduling in SAGIN
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