Efficiency improvement in multi-sensor wireless network based estimation algorithms for distributed parameter systems with application at the heat transfer

Abstract

This paper gives a technical solution to improve the efficiency in multi-sensor wireless network based estimation for distributed parameter systems. A complex structure based on some estimation algorithms, with regression and autoregression, implemented using linear estimators, neural estimators and ANFIS estimators, is developed for this purpose. The three kinds of estimators are working with precision on different parts of the phenomenon characteristic. A comparative study of three methods -linear and nonlinear based on neural networks and adaptive neuro-fuzzy inference system -to implement these algorithms is made. The intelligent wireless sensor networks are taken in consideration as an efficient tool for measurement, data acquisition and communication. They are seen as a "distributed sensor", placed in the desired positions in the measuring field. The algorithms are based on regression using values from adjacent and also on auto-regression using past values from the same sensor. A modelling and simulation for a case study is presented. The quality of estimation is validated using a quadratic criterion. A practical implementation is made using virtual instrumentation. Applications of this complex estimation system are in fault detection and diagnosis of distributed parameter systems and discovery of malicious nodes in wireless sensor networks.

A Wireless Sensor Network with Soft Computing Localization Techniques for Track Cycling Applications

“…ANFIS is a technique for the evolution of self-organizing neuro-fuzzy systems [ 48 ]. ANFIS is employed to perform non-linear estimation algorithms, which in this case is the collected RSSI data.…”

Introductory chapter: ANFIS for modeling multivariable nonlinear functions

“…Researchers in ANFIS develop new concepts and tools which enhance human understanding and improve the specialist's ability to design and implement high-performance systems. The researches cover aspects of ANFIS architectures, optimization techniques, applications of ANFIS as a universal estimator in modeling, and prediction in different domains of activity [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Thus, several applications can be listed: in power plants and power distribution networks, in metallurgy in the design of aluminum alloys, in the chemical industry for specific wear rate modeling of poly-tetra-flouro-ethylene composites, in electrical engineering, for inductance profile estimation for switched reluctance motors, for the definition of focal length for zoom lens for digital cameras, an adaptive filtering application as channel equalizer for mobile cellular channels, in medicine, as an application in health monitoring based on multisensor, data fusion and 2D wavelet transform, in civil engineering for prediction of compressive strength of manufactured sand concrete, in tracking disasters in the environment for spatial prediction of landslide susceptibility, in computer science, for data classification, application to identifying the online bearing fault, by filtering impulse noise, or for improving estimation algorithms for distributed parameter systems using sensor networks.…”

Measurement and Prediction of Centrical/Peripheral Network Properties based on Regression Analysis - A Parametric Foundation for Performance Self-Management in WSNs

“…Autonomic networking that builds upon the same principles within the context of the networking domain in order to tackle the increasing complexity of evergrowing distributed (wireless) network systems, has always played an important role to the same degree [9], [10]. Whereas measurement-based estimation of metrics bears good prospects in general [11], [12], its consideration within the context of autonomic self-management constitutes an even more promising approach. However, the estimation of performance metrics in multi-hop networks like WSNs is not a straight-forward task even for approved methodologies [13], [14].…”