摘要
针对求解无线传感器网络目标覆盖问题过程中存在的节点激活策略机理不明确、可行解集存在冗余等问题,提出一种基于深度Q学习的目标覆盖算法,学习无线传感器网络中节点的调度策略.首先,算法将构建可行解集抽象成Markov决策过程,智能体根据网络环境选择被激活的传感器节点作为离散动作;其次,奖励函数从激活节点的覆盖能力和自身剩余能量考虑,评价智能体选择动作的优劣.仿真实验结果表明,该算法在不同规模的网络环境下均有效,网络生命周期均优于3种贪婪算法、最大寿命覆盖率算法和自适应学习自动机算法.
Aiming at the uncertain mechanism of node activation strategies and redundancy of feasible solution sets in the process of solving target coverage problem in wireless sensor networks,we proposed a deep Q learning based target coverage algorithm to learn the scheduling strategies of nodes in wireless sensor networks.Firstly,the algorithm abstracted the construction of feasible solution sets into Markov decision process,and intelligently selected activated sensor nodes as discrete actions according to the network environment.Secondly,a reward function evaluated the performance of the intelligent agent in selecting actions based on the coverage capacity and its residual energy of the active node.The simulation experiment result shows that the algorithm is effective in different network environments,and the network lifecycle is superior to the three greedy algorithms,the maximum lifetime coverage algorithm and the adaptive learning automaton algorithm.
作者
高思华
顾晗
贺怀清
周钢
GAO Sihua;GU Han;HE Huaiqing;ZHOU Gang(College of Computer Science and Technology,Civil Aviation University of China,Tianjin 300300,China;College of Computer Science and Technology,Jilin University,Changchun 130012,China;Department of Science and Technology Management,TravelSky Technology Limited,Beijing 101300,China)
出处
《吉林大学学报（理学版）》
CAS
北大核心
2023年第6期1432-1440,共9页
Journal of Jilin University:Science Edition
基金
国家自然科学基金面上项目(批准号:62173332)
中央高校基本科研业务费专项基金(批准号:3122019118)。
关键词
目标覆盖问题
深度Q学习
无线传感器网络
强化学习
target coverage problem
deep Q learning
wireless sensor networks
reinforcement learning
