Reliable Data Fusion of Hierarchical Wireless Sensor Networks With Asynchronous Measurement for Greenhouse Monitoring

This paper investigates the data fusion problem of wireless sensor networks (WSNs) for the greenhouse monitoring system. Considering the characteristics of local consistency and slow change of the greenhouse environmental information, the hierarchical structure of WSNs is proposed for the greenhouse monitoring system, and the two-stage data fusion scheme is presented for the hierarchical network. In the first stage, the weighted data fusion algorithm of WSNs on local state estimation is designed for the cluster, which would improve the fusion accuracy and the ability of anti-interference of the system. Moreover, the multirate measurement mode is proposed to reduce the energy consumption of WSNs under the premise of satisfying the information sensing performance of the system. In the second stage, the data fusion at the sink node is conducted on the support function with the consistency analysis of data from different clusters. The simulation analysis on the greenhouse temperature information is provided to show the effectiveness of the proposed data fusion scheme.

[1]  Steven Liu,et al.  Fusion Estimation for Sensor Networks With Nonuniform Estimation Rates , 2014, IEEE Transactions on Circuits and Systems I: Regular Papers.

[2]  Chongquan Zhong,et al.  A Weighted Fusion Algorithm of Multi-sensor Based on Optimized Grouping , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[3]  T. Gomes,et al.  GreenMon: An efficient wireless sensor network monitoring solution for greenhouses , 2015, 2015 IEEE International Conference on Industrial Technology (ICIT).

[4]  Zidong Wang,et al.  Pinning controllability of autonomous Boolean control networks , 2016, Science China Information Sciences.

[5]  T. Ahonen,et al.  Greenhouse Monitoring with Wireless Sensor Network , 2008, 2008 IEEE/ASME International Conference on Mechtronic and Embedded Systems and Applications.

[6]  Hao Jiang,et al.  Fast, Accurate Event Classification on Resource-Lean Embedded Sensors , 2011, TAAS.

[7]  Tingwen Huang,et al.  An Event-Triggered Approach to State Estimation for a Class of Complex Networks With Mixed Time Delays and Nonlinearities , 2016, IEEE Transactions on Cybernetics.

[8]  Daniel W. C. Ho,et al.  Fault Reconstruction and State Estimator Design for Distributed Sensor Networks in Multitarget Tracking , 2015, IEEE Transactions on Industrial Electronics.

[9]  Derui Ding,et al.  Distributed recursive filtering for stochastic systems under uniform quantizations and deception attacks through sensor networks , 2017, Autom..

[10]  Huijun Gao,et al.  Distributed Filtering for a Class of Time-Varying Systems Over Sensor Networks With Quantization Errors and Successive Packet Dropouts , 2012, IEEE Transactions on Signal Processing.

[11]  Orazio Mirabella,et al.  A Hybrid Wired/Wireless Networking Infrastructure for Greenhouse Management , 2011, IEEE Transactions on Instrumentation and Measurement.

[12]  Donghua Zhou,et al.  Estimation Fusion with General Asynchronous Multi-Rate Sensors , 2010, IEEE Transactions on Aerospace and Electronic Systems.

[13]  Haifen Li,et al.  State Estimation of Macromotion Positioning Tables Based on Switching Kalman Filter , 2017, IEEE Transactions on Control Systems Technology.

[14]  Shuai Liu,et al.  Extended Kalman filtering for stochastic nonlinear systems with randomly occurring cyber attacks , 2016, Neurocomputing.

[15]  Zidong Wang,et al.  A Constrained Optimization Approach to Dynamic State Estimation for Power Systems Including PMU and Missing Measurements , 2013, IEEE Transactions on Control Systems Technology.

[16]  Chang Hong.  Multi-sensor Data Fusion Based on Correlation Function and Least Square , 2006 .

[17]  Zidong Wang,et al.  Inferring nonlinear lateral flow immunoassay state-space models via an unscented Kalman filter , 2016, Science China Information Sciences.

[18]  J.E. Gray,et al.  Theory of distributed estimation using multiple asynchronous sensors , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[19]  Roop Pahuja,et al.  A Wireless Sensor Network for Greenhouse Climate Control , 2013, IEEE Pervasive Computing.

[20]  Fuad E. Alsaadi,et al.  H ∞ Control for Two-Dimensional Fuzzy Systems with Interval Time-Varying Delays and Missing Measurements , 2016 .

[21]  Lei Guo,et al.  Optimal control for networked control systems with disturbances: a delta operator approach , 2017 .

[22]  Qing-Long Han,et al.  On Designing a Novel Self-Triggered Sampling Scheme for Networked Control Systems With Data Losses and Communication Delays , 2016, IEEE Transactions on Industrial Electronics.

[23]  Fuad E. Alsaadi,et al.  Robust ${\mathscr {H}}_{\infty }$ Filtering for a Class of Two-Dimensional Uncertain Fuzzy Systems With Randomly Occurring Mixed Delays , 2017, IEEE Transactions on Fuzzy Systems.

[24]  Fuad E. Alsaadi,et al.  A new framework for output feedback controller design for a class of discrete-time stochastic nonlinear system with quantization and missing measurement , 2016, Int. J. Gen. Syst..

[25]  L.P. Yan,et al.  Asynchronous multirate multisensor information fusion algorithm , 2007, IEEE Transactions on Aerospace and Electronic Systems.

[26]  Wenfeng Cheng,et al.  Data fusion of the wireless sensor network in greenhouse , 2012, 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet).

[27]  Daniel W. C. Ho,et al.  Observer-Based Event-Triggering Consensus Control for Multiagent Systems With Lossy Sensors and Cyber-Attacks , 2017, IEEE Transactions on Cybernetics.

[28]  Zidong Wang,et al.  Event-based security control for discrete-time stochastic systems , 2016 .

[29]  Chenglin Wen,et al.  A reduced-order approach to filtering for systems with linear equality constraints , 2016, Neurocomputing.

[30]  Shu-li Sun,et al.  Multi-sensor optimal information fusion Kalman filters with applications , 2004 .

[31]  Ronald R. Yager,et al.  The power average operator , 2001, IEEE Trans. Syst. Man Cybern. Part A.

[32]  Zidong Wang,et al.  Event-based state estimation for a class of complex networks with time-varying delays: A comparison principle approach , 2017 .

[33]  Sun Yong.  Consistent and Reliable Fusion of Multi-Sensor Based on Support Degree , 2005 .

[34]  Steven X. Ding,et al.  An ${\oldstyle{H_{\infty}}}$ Fault Estimation Scheme of Wireless Networked Control Systems for Industrial Real-Time Applications , 2014, IEEE Transactions on Control Systems Technology.

[35]  Yurong Liu,et al.  Passivity analysis for discrete-time neural networks with mixed time-delays and randomly occurring quantization effects , 2016, Neurocomputing.

[36]  Biswanath Mukherjee,et al.  Wireless sensor network survey , 2008, Comput. Networks.

[37]  G. Bot,et al.  An overview of climate and crop yield in closed greenhouses , 2012 .

[38]  Francisco Rodríguez,et al.  Multiobjective hierarchical control architecture for greenhouse crop growth , 2012, Autom..

[39]  Wang Xian-jie.  Robust bounds of stable controllers for linear uncertain singular perturbation systems , 2003 .

[40]  Jung-Chuan Chou,et al.  Weighted Data Fusion Use for Ruthenium Dioxide Thin Film pH Array Electrodes , 2009, IEEE Sensors Journal.

[41]  Wen-an Zhang,et al.  Hybrid Sequential Fusion Estimation for Asynchronous Sensor Network-Based Target Tracking , 2017, IEEE Transactions on Control Systems Technology.

[42]  Yurong Liu,et al.  Exponential stability of Markovian jumping Cohen-Grossberg neural networks with mixed mode-dependent time-delays , 2016, Neurocomputing.

[43]  Bao Jie Ji,et al.  Design and analysis of intelligent greenhouse environment control system based on multi-sensor information fusion , 2011, 2011 IEEE 2nd International Conference on Computing, Control and Industrial Engineering.

[44]  Wang Jing,et al.  Application of Kalman filtering algorithm in greenhouse environment monitoring , 2013, 2013 2nd International Symposium on Instrumentation and Measurement, Sensor Network and Automation (IMSNA).