基于Q学习和规划的传感器节点任务调度算法  

Task Scheduling Algorithm Based on Q-Learning and Programming for Sensor Nodes

作　　者：魏振春[1,2] 徐祥伟 冯琳[1,2] 丁蓓[1] 

机构地区：[1]合肥工业大学计算机与信息学院,合肥230009 [2]合肥工业大学安全关键工业测控技术教育部工程研究中心,合肥230009

出　　处：《模式识别与人工智能》2016年第11期1028-1036,共9页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.61502142;61370088);国家国际科技合作专项项目(No.2014 DFB10060)资助~~

摘　　要：为了改善节点的学习策略,提高节点的应用性能,以数据收集为应用建立任务模型,提出基于Q学习和规划的传感器节点任务调度算法,包括定义状态空间、延迟回报、探索和利用策略等基本元素.根据无线传感器网络(WSN)特性,建立基于优先级机制和过期机制的规划过程,使节点可以有效利用经验知识,改善学习策略.实验表明,文中算法具备根据当前WSN环境进行动态任务调度的能力.相比其它任务调度算法,文中算法能量消耗合理且获得较好的应用性能.To improve the learning policy and obtain better application performance of sensor nodes, a task scheduling algorithm based on Q-learning and programming （QP） for sensor nodes is proposed with the task model of data collection applications. Specifically, some basic learning elements, such as state space, delayed reward and the exploration-exploitation policy, are defined in QP as well. Moreover, according to the characteristics of wireless sensor network （ WSN）, the programming process based on the expired mechanism and the priority mechanism is established to improve the learning policy by making full use of empirical knowledge. Experimental results show that QP has the ability to perform task scheduling dynamically according to current WSN environments. Compared with other task scheduling algorithms, QP achieves better application performance with reasonable energy consumption.

关 键 词：无线传感器网络(WSN) 传感器节点 任务调度 Q学习 规划过程 

分 类 号：TP212.9[自动化与计算机技术—检测技术与自动化装置] TN929.5[自动化与计算机技术—控制科学与工程]