无线传感器节点工作模式转换策略优化模型

计算机科学 ›› 2019, Vol. 46 ›› Issue (5): 44-49.doi: 10.11896/j.issn.1002-137X.2019.05.006

无线传感器节点工作模式转换策略优化模型

赵宁博¹, 刘伟¹, 罗嵘², 胡顺仁^1,3

(重庆理工大学电气与电子工程学院重庆400054)¹
(清华大学电子工程系北京100084)²
(重庆大学光电技术及系统教育部重点实验室重庆400044)³

收稿日期:2018-04-02 修回日期:2018-08-03 发布日期:2019-05-15
作者简介:赵宁博(1987-),男,硕士生,主要研究方向为无线传感器网络;刘伟(1981-),男,博士,讲师,CCF会员,主要研究方向为无线通信与网络、嵌入式系统等,E-mail:liu-wei@cqut.edu.cn(通信作者);罗嵘(1970-),女,博士,副教授,主要研究方向为嵌入式系统;胡顺仁(1971-),男,教授,主要研究方向为网络通信技术、智能信息处理等。
基金资助:
国家自然科学基金(61601069),重庆市教委科学技术研究项目(KJ1600935)资助。

Optimization Model of Working Mode Transformation Strategies for Wireless Sensor Nodes

ZHAO Ning-bo¹, LIU Wei¹, LUO Rong², HU Shun-ren^1,3

(School of Electrical and Electronic Engineering,Chongqing University of Technology,Chongqing 400054,China)¹
(Department of Electronic Engineering,Tsinghua University,Beijing 100084,China)²
(Key Lab of Optoelectronic Technology & Systems(Chongqing University),Ministry of China,Chongqing 400044,China)³

Received:2018-04-02 Revised:2018-08-03 Published:2019-05-15

摘要/Abstract

摘要： 控制无线传感器节点的工作模式转换可提高能效,但现有控制策略人工干预较多,且缺少评估能效的指标。结合有限状态机和强化学习算法建立了对模式转换进行控制决策的模型;在此基础上,使用能耗、单位能耗的数据吞吐量两个量化指标,构建了收益差分矩阵以评价转换策略的优劣,构造特征函数并描述其能效,建立了优化模型。通过同样数量的工作模式组合和不同数量的工作模式组合两个层次,对不同转换策略进行了评价。与一般控制策略相比,该模型在降低约57%能耗的同时,只损失了约14%的数据吞吐量,相对于其他研究,其降低了更多能耗,能够延长节点寿命,为节点工作模式控制提供模型支持和理论指导。

关键词: 差分矩阵, 工作模式转换策略, 能效, 无线传感器节点, 优化模型

Abstract: It’s efficient to transform the working modes of wireless sensor network for raising energy efficiency,while the current control strategies reach a plateau with much manual intervention but few indicators.Combing the finite state machine algorithm and the reinforcement learning algorithm,this paper established a working mode transformation model.Based on this model,while adopting energy consumption and data throughput as two indicators,this paper used difference matrix to evaluate transformation strategy,constructed characteristic function to estimate the energy efficiency,and then established an optimization model to judge the strategies by two steps.Compared with the common control strategy,the model reduces energy consumption by about 57%,but only lost about 14% of data throughput.It significantly improves energy efficiency,and provides model support and theoretical guidance for node work mode control.

Key words: Difference matrix, Energy efficiency, Optimization model, Wireless sensor node, Work mode transformation strategy

中图分类号:

TP274

赵宁博, 刘伟, 罗嵘, 胡顺仁. 无线传感器节点工作模式转换策略优化模型[J]. 计算机科学, 2019, 46(5): 44-49. https://doi.org/10.11896/j.issn.1002-137X.2019.05.006

ZHAO Ning-bo, LIU Wei, LUO Rong, HU Shun-ren^1,3. Optimization Model of Working Mode Transformation Strategies for Wireless Sensor Nodes[J]. Computer Science, 2019, 46(5): 44-49. https://doi.org/10.11896/j.issn.1002-137X.2019.05.006

参考文献

[1]DESAI S S,NENE M J.DANES-Distributed Algorithm forNode Energy-management for Self-organizing Wireless Sensor Networks[C]∥IEEE International Conference on Recent Trends in Electronics,Information & Communication Techno-logy.New York:IEEE Press,2017:1296-1301.
[2]LI Z M,CHEN X G.Research and Application of WSN Node Communication Energy Consumption Model Based on Asynchronous MAC Protocol[J].Transactions of Beijing Institute of Technology,2015,35(2):171-175.(in Chinese)李智敏,陈祥光.基于异步MAC协议的WSN节点通信能耗模型的研究及应用[J].北京理工大学学报,2015,35(2):171-175.
[3]RANDRIANARISAINA A,PASQUIER O,CHARGE P.Energy Consumption Modeling of Smart Nodes with a Function Approach[C]∥Conference on Design and Architectures for Signal and Image Processing.New York:IEEE Press,2015:1-6.
[4]MOKRENKO O,LESECQ S,LOMBARDI W,et al.DynamicPower Management in a Wireless Sensor Network using Predictive Control[C]∥Annual Conference of the IEEE Industrial Electronics Society.New York:IEEE Press,2014:4756-4761.
[5]DARGIE W.Dynamic Power Management in Wireless Sensor Networks:State-of-the-Art[J].IEEE Sensors Journal,2012,12(5):1518-1528.
[6]BANU G M.MPSoC based Dynamic Power Management inWireless Sensor Networks[C]∥International Conference on Information Communication and Embedded Systems.New York:IEEE Press,2015:1-6.
[7]PRASAD Y R V,PACHAMUTHU R.Neural Network Based Short Term Forecasting Engine to Optimize Energy and Big Data Storage Resources of Wireless Sensor Networks[C]∥IEEE Annual Computer Software and Applications Conference.New York:IEEE Press,2015:511-516.
[8]RODWAY J,MUSILEK P.Wireless Sensor Networks withPressure based Energy Forecasting:A Simulation Study[C]∥IEEE Canadian Conference on Electrical and Computer Engineering.New York:IEEE Press,2016:1-4.
[9]WEI Z C,XU X W,FENG L,et al.Task Scheduling Algorithm Based on Q-Learning and Programming for Sensor Nodes[J].Pattern Recognition and Artificial Intelligence,2016,29(11):1028-1036.(in Chinese)魏振春,徐祥伟,冯琳,等.基于Q学习和规划的传感器节点任务调度算法[J].模式识别与人工智能,2016,29(11):1028-1036.
[10]HONG Z,WANG R,LI X L.A Clustering-tree Topology Control Based on the Energy Forecast for Heterogeneous Wireless Sensor Networks[J].IEEE/CAA Journal of Automatica Sinica,2016,3(1):68-77.
[11]VINUTHA C B,NALINI N.Energy Aware Optimal Clustering and Reliable Routing based on Markov Model in Wireless Sensor Networks[C]∥International Conference on Wireless Communications,Signal Processing and Networking.New York:IEEE Press,2016:420-425.
[12]LIN W T,LAI I W,LEE C H.Distributed Energy Cooperation for Energy Harvesting Nodes using Reinforcement Learning[C]∥Annual International Symposium on Personal,Indoor,and Mobile Radio Communications.New York:IEEE Press,2015:1584-1588.
[13]MAO S,TANG H,ZHOU L,et al.An Energy ConservationOptimization Strategy for Wireless Sensor Network Node based on Q-learning[C]∥Asian Control Conference.New York:IEEE Press,2011:938-943.
[14]KIANPISHEH S,CHARKARI N M.A New Approachfor PowerManagement in Sensor Node based on Reinforcement?Learning[C]∥International Symposium on Computer Networks and Distributed Systems.New York:IEEE Press,2011:158-163.
[15]ALSHEIKH M A,LIN S W,NIYATO D,et al.Machine Lear-ning in Wireless Sensor Networks:Algorithms,Strategies and Applications[J].IEEE Communications Surveys & Tutorials,2014,16(4):1996-2018.

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