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Determining node duty cycle using q-learning and linear regression for WSN

Frontiers of Computer Science 2021年第1期15卷 17-23页

作者： Han Yao HUANG Kyung Tae KIM Hee Yong YOUN College of Software Sungkyunkwan UniversitySuwon 440-746Korea

Wireless sensor network(WSN)is effective for monitoring the target environment,which consists of a large number of sensor nodes of limited *** efficient medium access control(MAC)protocol is thus imperative to maximize the energy efficiency and performance of *** most existing MAC protocols are based on the scheduling of sleep and active period of the nodes,and do not consider the relationship between the load condition and *** this paper a novel scheme is proposed to properly determine the duty cycle of the WSN nodes according to the load,which employs the q-leaming technique and function approximation with linear *** allows low-latency energy-efficient scheduling for a wide range of traffic conditions,and effectively overcomes the limitation of q-learning with the problem of continuous state-action ***3 simulation reveals that the proposed scheme significantly improves the throughput,latency,and energy efficiency compared to the existing fully active scheme and S-MAC.

关键词： wireless sensor network media access control duty-cycle scheduling q-leaming linear regression

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Distance-directed Target Searching for a Deep Visual Servo SMA Driven Soft Robot Using Reinforcement Learning

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Journal of Bionic Engineering 2020年第6期17卷 1126-1138页

作者： Wuji Liu Zhongliang Jing Han Pan Lingfeng qiao Henry Leung Wujun Chen School of Aeronautics and Astronautics Shanghai Jiao Tong UniversityShanghai 200240China Deparment of Elecrical and Computer Engineering University of CalgaryCalgary AB 72N 1N4Canada School of Naval Architecture Ocean&Civil EngineeringShanghai Jiao Tong UniversityShanghai 200240China

Performing complex tasks by soft robots in constrained environment remains an enormous challenge owing to the limitations of flexible mechanisms and control *** this paper,a novel biomimetic soft robot driven by Shape Memory Alloy(SMA)with light weight and multi-motion abilities is *** adapt deep learning to perceive irregular targets in an unstructured *** at the target searching task,an intelligent visual servo control algorithm based on q-leaming is proposed to generate distance-directed end effector *** particular,a threshold reward system for the target searching task is proposed to enable a certain degree of tolerance for pointing *** addition,the angular velocity and working space of the end effector with load and without load based on the established coupling kinematic model are *** framework enables the trained soft robot to take actions and perform target *** experiments under different conditions demonstrate the convergence of the learning process and effectiveness of the proposed algorithm.

关键词： biomimetic soft robot SMA deep visual servo q-leaming

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