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Adaptive Power Management of IoT Systems by Reinforcement Learning

通过强化学习实现物联网系统的自适应电源管理

基本信息

批准号：
18J20946
负责人：
SHRESTHAMALI SHASWOT
金额：
$ 1.41万
依托单位：
The University of Tokyo
依托单位国家：
日本
项目类别：
Grant-in-Aid for JSPS Fellows
财政年份：
2018
资助国家：
日本
起止时间：
2018-04-25 至 2021-03-31
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-18J20946/
关键词：
Multi-objective RL Wireless Sensor Nodes Reinforcement Learning Internet of Things Distributed Learning Deep Q Networks Wireless Sensor Networks Machine Learning Edge Intelligence Distributed Reinforcement Learning Energy Harvesting Wi

项目摘要

The research achievement for this year was two fold - 1)developing a Multi-Objective Reinforcement Learning (MORL) approach for Energy Neutral Operation (ENO) of Energy Harvesting Wireless Sensor Nodes (EHWSNs), and 2) preparing the PhD thesis.My previous research efforts concentrated on achieving ENO using a single reward function. However, one cannot optimize energy scheduling between various tasks in EHWSNs with this approach. So, I investigated into MORL methods to optimize energy scheduling over multiple tasks that modern EHWSNs execute.Since previous RL methods could not perform runtime tradeoffs along the Pareto-space and/or require prohibitively large amounts resources, I developed a novel multi-objective RL framework for EHWSNs. This framework can optimize over multiple objectives and tradeoff dynamically. It consumes much less resources compared to direct multi-objective RL methods making them suitable for resource constrained EHWSNs. I remodeled the EHWSN system, developed a multi-objective Markov Decision Process (MDP) and proposed two novel MORL algorithms that enables EHWSNs to learn tradeoff policies in shorter time periods while making lesser mistakes. Our paper on this novel method is currently under review.I also consolidated the different results from my previous research papers to prepare my PhD thesis and defense presentation. My research (supported by this KAKENHI) was also met with high enthusiasm in the research community and I was invited to be the publicity chair of AIChallenge IoT 2020 Workshop that was co-located (virtually) with SenSys 2020.

今年的研究成果有两个方面：1)开发能量收集无线传感器节点（EHWSNs）能量中性操作（ENO）的多目标强化学习（MORL）方法，2)准备博士论文。我之前的研究主要集中在使用单一奖励函数实现ENO。然而，这种方法无法优化EHWSNs中不同任务之间的能量调度。因此，我研究了MORL方法来优化现代ehwsn执行的多个任务的能量调度。由于以前的强化学习方法不能沿着帕累托空间进行运行时权衡和/或需要大量的资源，我为EHWSNs开发了一个新的多目标强化学习框架。该框架可以对多个目标进行优化，并进行动态权衡。与直接多目标RL方法相比，它消耗的资源少得多，使其适合于资源受限的ehwnn。我重塑了EHWSN系统，开发了一个多目标马尔可夫决策过程（MDP），并提出了两种新的MORL算法，使EHWSN能够在更短的时间内学习权衡策略，同时犯更少的错误。我们关于这种新方法的论文目前正在审查中。我还整合了我之前研究论文的不同结果来准备我的博士论文和答辩。我的研究（由这个KAKENHI支持）在研究界也受到了很高的热情，我被邀请担任与SenSys 2020（虚拟）位于同一地点的aicchallenge IoT 2020研讨会的宣传主席。