A virtual layer of measure based on soft sensors

In this paper it is proposed a method to design and train a layer of soft sensors based on neural networks in order to constitute a virtual layer of measure in a wireless sensor network. Each soft sensor of the layer esteems the missing values of some hardware sensors by using the values obtained from some other sensors. In so doing, we perform a spatial forecasting. The correlation analysis for all parameter taken into account is used to define a cluster of real sensors used as sources of measure to esteem missing values. An application concerning the fire prevention field is used as a test case and results evaluation.

[1]  James L. McClelland,et al.  Finite State Automata and Simple Recurrent Networks , 1989, Neural Computation.

[2]  I. Noda,et al.  A learning method for recurrent networks based on minimization of finite automata , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[3]  Umberto Maniscalco.  Virtual Sensors to Support the Monitoring of Cultural Heritage Damage , 2004, WIRN.

[4]  Giovanni Pilato,et al.  Soft Sensor based on E-αNETs , 2010, WIRN.

[5]  Romano Fantacci,et al.  The communication infrastructure for emergency management: the In.Sy.Eme. vision , 2009, IWCMC.

[6]  Antonio Chella,et al.  Neural Networks as Soft Sensors: a Comparison in a Real World Application. , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[7]  Umberto Maniscalco,et al.  VALIDATION OF SOFT SENSORS IN MONITORING AMBIENT PARAMETERS , 2006 .

[8]  Raúl Rojas,et al.  Neural Networks - A Systematic Introduction , 1996 .

[9]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[10]  Bogdan Gabrys,et al.  Data-driven Soft Sensors in the process industry , 2009, Comput. Chem. Eng..

[11]  Samira Moussaoui,et al.  Disaster Management Projects Using Wireless Sensor Networks: An Overview , 2014, 2014 28th International Conference on Advanced Information Networking and Applications Workshops.

[12]  P. Ciarlini,et al.  Wavelets and Elman Neural Networks for monitoring environmental variables , 2008 .

[13]  W. Tobler.  A Computer Movie Simulating Urban Growth in the Detroit Region , 1970 .

[14]  Luigi Fortuna,et al.  Soft Sensors for Monitoring and Control of Industrial Processes (Advances in Industrial Control) , 2006 .

[15]  Shuyee D. Lee,et al.  Employing First Principles Model-Based Soft Sensors for Superior Process Control and Optimisation , 2013 .

[16]  Dan Paulsson,et al.  A Soft Sensor for Bioprocess Control Based on Sequential Filtering of Metabolic Heat Signals , 2014, Sensors.

[17]  Mengjie Zhang,et al.  Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction , 2012, Neurocomputing.

[18]  Jialin Liu.  Developing Soft Sensors Based on Data-Driven Approach , 2010, 2010 International Conference on Technologies and Applications of Artificial Intelligence.

[19]  Lyle H. Ungar,et al.  Using radial basis functions to approximate a function and its error bounds , 1992, IEEE Trans. Neural Networks.

[20]  Jianzhou Wang,et al.  Forecasting wind speed using empirical mode decomposition and Elman neural network , 2014, Appl. Soft Comput..