机械振动WSNs最小二乘发射功率自适应控制方法
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摘要
机械振动无线传感器节点为了保证数据传输的可靠性采用最大发射功率,导致部分传感器节点传输能耗浪费。针对此问题,提出了一种无线传感器节点最小二乘发射功率自适应控制方法。首先,传感器节点在机械振动监测中进行簇内通信获得发射功率与链路质量的离散关系;其次,采用最小二乘法对离散数据进行线性拟合,建立数据可靠、节能传输的最小二乘发射功率自适应数学模型;最后,结合链路质量指示阈值计算出节点间数据传输的最优发射功率。对比实验结果表明,采用最小二乘发射功率自适应控制方法能有效降低机械振动无线传感器节点的传输功耗。
Machine vibration wireless sensor networks node often works at the wireless radio frequency phase where the transmitted energy is the largest to ensure the reliability of transmission,but resulting in a large transmission power consumption of sensor nodes,especially in mass raw data transmission requirements,In the light of this problem,a least squares adaptive transmission power control(LSATPC)method for wireless sensor networks nodes is proposed.First,the discrete relationship between transmission power and link quality indicator(LQI)is studied through inner-cluster sensor nodes communication in the mechanical vibration monitoring in laboratory environment.Then,the least squares method is utilized to fit the discrete data,thus to construct the least squares adaptive transmission power mathematical model for reliable and energy-efficient data transmission.Finally,combined with the threshold value of LQI,the optimal wireless transmission power of the nodes at different communication conditions is calculated,satisfied with the result of the reliable data transmission.Experimental results indicate that the transmission energy consumption of mechanical vibration wireless sensor network nodes can be effectively reduced by this method.
Machine vibration wireless sensor networks node often works at the wireless radio frequency phase where the transmitted energy is the largest to ensure the reliability of transmission,but resulting in a large transmission power consumption of sensor nodes,especially in mass raw data transmission requirements,In the light of this problem,a least squares adaptive transmission power control(LSATPC)method for wireless sensor networks nodes is proposed.First,the discrete relationship between transmission power and link quality indicator(LQI)is studied through inner-cluster sensor nodes communication in the mechanical vibration monitoring in laboratory environment.Then,the least squares method is utilized to fit the discrete data,thus to construct the least squares adaptive transmission power mathematical model for reliable and energy-efficient data transmission.Finally,combined with the threshold value of LQI,the optimal wireless transmission power of the nodes at different communication conditions is calculated,satisfied with the result of the reliable data transmission.Experimental results indicate that the transmission energy consumption of mechanical vibration wireless sensor network nodes can be effectively reduced by this method.
引文
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[15]曾超,邓蕾,张又进,等.机械振动无线传感器网络数据分块大小研究[J].仪器仪表学报,2016,37(7):1556-1563.Zeng Chao,Deng Lei,Zhang Youjin,et al.Research on the data block size of mechanical vibration wireless sensor networks[J].Chinese Journal of Scientific Instrument,2016,37(7):1556-1563.(in Chinese)
[2]Guo Peng,Jiang Tao,Zhang Kui.Novel 2-hop coloring algorithm for time-slot assignment of newly deployed sensor nodes without ID in wireless sensor and robot networks[J].Computer Communications,2012,35(9):1125-1131.
[3]Raza M,Ahmed G,Khan N M.Experimental evaluation of transmission power control strategies in wireless sensor networks[C]∥International Conference on Emerging Technologies.Islamabad,Pakista:IEEE,2012:1-4.
[4]Meghji M,Habibi D.Transmission power control in multihop wireless sensor networks[C]∥International Conference on Ubiquitous and Future Networks.Dalian,China:IEEE,2011:25-30.
[5]汤宝平,邓兵,邓蕾,等.无线传感器网络多级融合的机械故障诊断方法[J].振动、测试与诊断,2016,36(1):92-96.Tang Baoping,Deng Bing,Deng Lei,et al.A mechanical fault diagnosis method based on multi-level fusion in wireless sensor networks[J].Journal of Vibration,Measurement&Diagnosis,2016,36(1):92-96.(in Chinese)
[6]Xu Lina,Delaney D T,Ohare G M P,et al.The Impact of transmission power control in wireless sensor networks[C]∥IEEE International Symposium on Network Computing and Applications.Cambridge,MA,USA:IEEE,2013:255-258.
[7]Cotuk H,Bicakci K,Tavli B,et al.The impact of transmission power control strategies on lifetime of wireless sensor networks[J].IEEE Transactions on Computers,2014,63(11):2866-2879.
[8]Qiao Junfeng,Liu Sanyang,Qi Xiaogang,et al.Transmission power control in wireless sensor networks under the minimum connected average node degree constraint[J].International Journal on Smart Sensing&Intelligent Systems,2015,8(1):801-821.
[9]Hu Qian,Tang Zhenzho.ATPM:an energy efficient MAC Protocol with adaptive transmit power scheme for wireless sensor networks[J].Journal of Multimedia,2011,6(6):122-128.
[10]Fu Yong,Sha Mo,Hackmann G,et al.Practical control of transmission power for Wireless Sensor Networks[C]∥IEEE International Conference on Network Protocols.Austin,TX,USA:IEEE Computer Society,2012:1-10.
[11]李小敏,臧英,罗锡文,等.基于发射功率自适应的稻田无线传感器网络节点设计[J].农业工程学报,2014,30(7):140-146.Li Xiaomin,Zang Ying,Luo Xiwen,et al.Design of WSN node with adaptive transmitting power for rice field[J].Transactions of the Chinese Society of Agricultural Engineering,2014,30(7):140-146.(in Chinese)
[12]Kotian R,Exarchakos G,Liotta A.Data driven transmission power control for wireless sensor networks[M]∥Internet and Distributed Computing Systems.Windsor,UK:Springer International Publishing,2015:75-87.
[13]阮啟东.面向机械振动监测的无线传感器网络可靠传输协议的研究[D].重庆:重庆大学,2012.
[14]Shao Qike,Feng Shuna,Mao Keji.A dynamic adaptive fuzzy algorithm for power control in wireless sensor networks[J].Chinese Journal of Sensors&Actuators,2015,28(4):563-571.
[15]曾超,邓蕾,张又进,等.机械振动无线传感器网络数据分块大小研究[J].仪器仪表学报,2016,37(7):1556-1563.Zeng Chao,Deng Lei,Zhang Youjin,et al.Research on the data block size of mechanical vibration wireless sensor networks[J].Chinese Journal of Scientific Instrument,2016,37(7):1556-1563.(in Chinese)