自适应温度补偿无线传感器网络时间同步方法
|
摘要
时间同步是多智能体网络协同工作的基础,具有重要意义。低成本无线传感器网络节点时钟易受到环境因素的影响,导致节点时间同步误差增大,网络信道负载增加。针对上述问题,提出了一种低时钟再同步周期、自适应温度补偿的无线传感器网络时间同步方法。首先,基于双向通信时间同步模型,提出了温度补偿的节点频移量动态估计模型;然后,采用Almon函数加权求和的方式对温度和频移量数据进行融合,解决数据采样率不匹配和模型维度高的问题;其次,为进一步提高时间同步精度,采用Kalman滤波器对频移量和相移量估计值进行滤波,并采用系统状态后验估计值对节点本地时间进行补偿;最后,参照IEEE 802.15.4标准对时间同步精度的要求,设计了一种失效风险最小化的再同步决策函数,最大限度提高节点再同步周期,减少信道负载。在高低温箱、室内和室外3种环境下进行实验以验证所提的方法。试验结果表明,与洪泛时钟同步协议(FTSP)时间同步协议相比,自适应温度补偿的时间同步(ATCTS)算法平均时间同步误差降低了97.4%,室外环境下平均时间同步周期为324 min。
Time synchronization is the foundation for the cooperative work of multi-agent networks and has great significance. The clock of a low-cost wireless sensor network node is subject to the influence of the environment factors, which leads to the increasing of the node time synchronization error and network channel overhead. Aiming at this problem, a novel network time synchronization method with low clock re-synchronization interval and adaptive temperature compensation for WSNs is proposed. Firstly, a node frequency shift dynamic estimation model with temperature compensation is proposed based on the two-way message exchange time synchronization model. Then, the Almon function weighted summation method is adopted to fuse the temperature and frequency shift data, and solve the problems of data sampling rate un-matching and high model dimension; next, in order to further improve the time synchronization accuracy, a Kalman filter is used to filter the estimation values of the frequency shift and phase shift, and the system state posteriori estimation value is used to compensate the node local time. At last, an adaptive re-synchronization decision function with minimized failure risk is designed according to the time synchronization accuracy requirement of the IEEE 802.15.4 standard, which improves the node re-synchronization interval and decreases the channel overhead to the maximum extent. Experiments in high, low temperature chamber, indoor and outdoor environments were conducted to verify the proposed method. The experiment results show that compared with that of FTSP time synchronization protocol, the average time synchronization error of the proposed ATCTS method is reduced by 97.4%, and the average time synchronization interval is 324 minutes in outdoor environment.
Time synchronization is the foundation for the cooperative work of multi-agent networks and has great significance. The clock of a low-cost wireless sensor network node is subject to the influence of the environment factors, which leads to the increasing of the node time synchronization error and network channel overhead. Aiming at this problem, a novel network time synchronization method with low clock re-synchronization interval and adaptive temperature compensation for WSNs is proposed. Firstly, a node frequency shift dynamic estimation model with temperature compensation is proposed based on the two-way message exchange time synchronization model. Then, the Almon function weighted summation method is adopted to fuse the temperature and frequency shift data, and solve the problems of data sampling rate un-matching and high model dimension; next, in order to further improve the time synchronization accuracy, a Kalman filter is used to filter the estimation values of the frequency shift and phase shift, and the system state posteriori estimation value is used to compensate the node local time. At last, an adaptive re-synchronization decision function with minimized failure risk is designed according to the time synchronization accuracy requirement of the IEEE 802.15.4 standard, which improves the node re-synchronization interval and decreases the channel overhead to the maximum extent. Experiments in high, low temperature chamber, indoor and outdoor environments were conducted to verify the proposed method. The experiment results show that compared with that of FTSP time synchronization protocol, the average time synchronization error of the proposed ATCTS method is reduced by 97.4%, and the average time synchronization interval is 324 minutes in outdoor environment.
引文
[1] 汪付强,曾鹏,于海斌.一种低开销的双向时间同步算法[J]. 仪器仪表学报,2011,32(6):1357-1363.WANG F Q, ZENG P, YU H B. Low overhead two-way time synchronization algorithm[J]. Journal of Scientific Instrument,2011,32(6):1357-1363.
[2] 张晖,赵鹏.泛在协同环境下最大似然估计时间同步算法[J].仪器仪表学报,2016,37(10):2373- 2381.ZHANG H, ZHAO P. Time synchronization algorithm based on maximum likelihood estimation in ubiquitous cooperative environment[J]. Journal of Scientific Instrument,2016,37(10):2373- 2381.
[3] 王頲,万羊所,唐晓铭,等.不可靠WSN时钟同步网络化输出反馈MPC量化分析[J].仪器仪表学报,2017, 38(7):1798-1808.WANG T, WAN Y S, TANG X M, et al. Unreliable WSN clock synchronization networked output feedback model predictive control quantitative analysis[J]. Journal of Scientific Instrument,2017,38(7):1798-1808.
[4] 郑家宁,张宁,咸竞天.基于1553B与RS- 422总线的时间同步系统的设计与实现[J].电子测量技术,2018, 41(10): 86-90.ZHENG J N, ZHANG N, XIAN J T. Design and implementation of time synchronization system of 1553B and RS- 422 bus[J]. Electronic Measurement Technology, 2018,41(10):86-90.
[5] 李研,黄凤辰,严锡君.基于无线传感器网络的粮库监测系统[J].国外电子测量技术,2018,37(4):54- 58.LI Y, HUANG F CH, YAN X J. Grain depot monitoring system based on WSN[J]. Foreign Electronic Measurement Technology, 2018,37(4):54- 58.
[6] 刘功亮,康文静.基于压缩感知的水下稀疏传感网信息获取技术[J].仪器仪表学报,2014,35(2):253- 260.LIU G L, KANG W J. Information acquisition technology for sparse underwater sensor networks based on compressed sensing[J]. Journal of Scientific Instrument, 2014, 35(2):253- 260.
[7] 周剑,魏广涛,张胜东,等.基于多种交互方式的分布式空气质量监测系统设计与实现[J].电子测量与仪器学报,2018, 32(3):119-126.ZHOU J, WEI G T, ZHANG SH D, et al. Design and implementation of distributed air quality monitoring system based on multiple interactions[J]. Journal of Electronic Measurement and Instrumentation, 2018, 32(3):119-126.
[8] 孙宇嘉,王晓鸣,于纪言.低信标节点密度传感器网络的启发式定位算法[J].仪器仪表学报,2018,39(1):225- 233.SUN Y J, WANG X M, YU J Y. Heuristic localization algorithm for low density of anchor nodes in wireless sensor networks[J]. Journal of Scientific Instrument, 2018, 39(1):225- 233.
[9] QIU T, ZHANG Y SH, QIAO D J, et al. A robust time synchronization scheme for industrial internet of things[J]. IEEE Transactions on Industrial Informatics, 2018, 14(8): 3570-3580.
[10] KAZEMINEZHAD S K. A study on clock synchronization methods in wired networks and WSN[J]. International Journal of Engineering and Technology, 2017, 9(1): 23- 26.
[11] CASTILLO-SECILLA J M, PALOMARES J M, LEON F, et al. Homomorphic filtering for improving time synchronization in wireless networks[J]. Sensors, 2017, 17(4):1- 24.
[12] KAZEMINEZHAD S K. Environment-aware clock skew estimation and synchronization for wireless sensor networks[C]. IEEE International Conference on Computer Communications, 2012: 1017-1025.
[13] CASTILLO-SECILLA J M, PALOMARES J M, OLIVARES J. Temperature-compensated clock skew adjustment[J]. Sensors, 2013, 13(8): 10981-11006.
[14] JUAN J, PEREZ S, SANTIAGO F C. Adaptive time window linear regression algorithm for accurate time synchronization in wireless sensor networks[J]. Ad Hoc Networks, 2015, 24(A): 92-108.
[15] ZHANG J F, CHRISTOFIDES P D, HE X, et al. Event-triggered filtering and intermittent fault detection for time-varying systems with stochastic parameter uncertainty and sensor saturation[J]. International Journal of Robust and Nonlinear Control, 2018, 28(16): 4666- 4680.
[2] 张晖,赵鹏.泛在协同环境下最大似然估计时间同步算法[J].仪器仪表学报,2016,37(10):2373- 2381.ZHANG H, ZHAO P. Time synchronization algorithm based on maximum likelihood estimation in ubiquitous cooperative environment[J]. Journal of Scientific Instrument,2016,37(10):2373- 2381.
[3] 王頲,万羊所,唐晓铭,等.不可靠WSN时钟同步网络化输出反馈MPC量化分析[J].仪器仪表学报,2017, 38(7):1798-1808.WANG T, WAN Y S, TANG X M, et al. Unreliable WSN clock synchronization networked output feedback model predictive control quantitative analysis[J]. Journal of Scientific Instrument,2017,38(7):1798-1808.
[4] 郑家宁,张宁,咸竞天.基于1553B与RS- 422总线的时间同步系统的设计与实现[J].电子测量技术,2018, 41(10): 86-90.ZHENG J N, ZHANG N, XIAN J T. Design and implementation of time synchronization system of 1553B and RS- 422 bus[J]. Electronic Measurement Technology, 2018,41(10):86-90.
[5] 李研,黄凤辰,严锡君.基于无线传感器网络的粮库监测系统[J].国外电子测量技术,2018,37(4):54- 58.LI Y, HUANG F CH, YAN X J. Grain depot monitoring system based on WSN[J]. Foreign Electronic Measurement Technology, 2018,37(4):54- 58.
[6] 刘功亮,康文静.基于压缩感知的水下稀疏传感网信息获取技术[J].仪器仪表学报,2014,35(2):253- 260.LIU G L, KANG W J. Information acquisition technology for sparse underwater sensor networks based on compressed sensing[J]. Journal of Scientific Instrument, 2014, 35(2):253- 260.
[7] 周剑,魏广涛,张胜东,等.基于多种交互方式的分布式空气质量监测系统设计与实现[J].电子测量与仪器学报,2018, 32(3):119-126.ZHOU J, WEI G T, ZHANG SH D, et al. Design and implementation of distributed air quality monitoring system based on multiple interactions[J]. Journal of Electronic Measurement and Instrumentation, 2018, 32(3):119-126.
[8] 孙宇嘉,王晓鸣,于纪言.低信标节点密度传感器网络的启发式定位算法[J].仪器仪表学报,2018,39(1):225- 233.SUN Y J, WANG X M, YU J Y. Heuristic localization algorithm for low density of anchor nodes in wireless sensor networks[J]. Journal of Scientific Instrument, 2018, 39(1):225- 233.
[9] QIU T, ZHANG Y SH, QIAO D J, et al. A robust time synchronization scheme for industrial internet of things[J]. IEEE Transactions on Industrial Informatics, 2018, 14(8): 3570-3580.
[10] KAZEMINEZHAD S K. A study on clock synchronization methods in wired networks and WSN[J]. International Journal of Engineering and Technology, 2017, 9(1): 23- 26.
[11] CASTILLO-SECILLA J M, PALOMARES J M, LEON F, et al. Homomorphic filtering for improving time synchronization in wireless networks[J]. Sensors, 2017, 17(4):1- 24.
[12] KAZEMINEZHAD S K. Environment-aware clock skew estimation and synchronization for wireless sensor networks[C]. IEEE International Conference on Computer Communications, 2012: 1017-1025.
[13] CASTILLO-SECILLA J M, PALOMARES J M, OLIVARES J. Temperature-compensated clock skew adjustment[J]. Sensors, 2013, 13(8): 10981-11006.
[14] JUAN J, PEREZ S, SANTIAGO F C. Adaptive time window linear regression algorithm for accurate time synchronization in wireless sensor networks[J]. Ad Hoc Networks, 2015, 24(A): 92-108.
[15] ZHANG J F, CHRISTOFIDES P D, HE X, et al. Event-triggered filtering and intermittent fault detection for time-varying systems with stochastic parameter uncertainty and sensor saturation[J]. International Journal of Robust and Nonlinear Control, 2018, 28(16): 4666- 4680.