Poster abstract: Towards adaptive wireless sensor networks

IEEE Signal Processing Magazine, 2008

I n this issue, "Best of the Web" focuses on adaptive filtering or, more generally, adaptive signal processingthe design of time-varying (adaptive) digital filters that would tune themselves to optimally process nonstationary signals in nonstationary environments. Much of what is found today in adaptive filtering algorithms can be traced back to two seminal articles that were published in 1960. The first article, "Adaptive Switching Circuits," was published by Bernard Widrow and Marcian Hoff, and described the least mean square (LMS) algorithm. This algorithm is widely used in adaptive signal processing, and is the most well-understood approach to training a linear system to minimize the mean square error. Appearing in 1960, the second article, "A New Approach to Linear Filtering and Prediction Problems," was authored by R.E. Kalman and described a recursive solution to the discrete-data linear filtering problem. Since that time, the Kalman filter has been the subject of extensive research and application. The area of adaptive signal processing has had a significant impact on a wide variety of signal processing applications. These include inverse filtering, signal modeling, prediction, channel equalization, echo cancellation, noise cancellation, system identification and control, line enhancement,

2008 IEEE International Symposium on Signal Processing and Information Technology, 2008

Wireless sensor networking (WSN) is an emerging technology that has a wide range of potential applications including environment monitoring, surveillance, medical systems, and robotic exploration. These networks consist of large numbers of distributed nodes that organize themselves into a multihop wireless network. Each node is equipped with one or more sensors, embedded processors, and lowpower radios, and is normally battery operated. Reporting constant measurement updates incurs high communication costs for each individual node, resulting in a significant communication overhead and energy consumption.

2004

An improved distributed coding and signal processing approach is proposed to reduce energy consumption in wireless sensor networks. The proposed scheme exploits the inherent correlations among sensor data in order to reduce the transmission requirements. The energy consumption at data collection sensor nodes is reduced by implementing the correlation tracking algorithm at a data gathering node. The proposed algorithm extends the scheme proposed in [1] by improving the adaptive correlation tracking algorithm. Our numerical results based on sample real sensor data show that the proposed scheme can provide significant energy savings.

2013

InTech eBooks, 2013

Wireless sensor networks allow fine-grained obser-vations of real-world phenomena. However, providing constant measurement updates incurs high communication costs for each individual node, resulting in increased energy depletion in the network. Data reduction strategies aim at reducing the amount of data sent by each node, for example by predicting the measured values both at the source and the sink node, thus only requiring nodes to send the readings that deviate from the prediction. While effectively reducing power consumption, such techniques so far needed to rely on a-priori knowledge to correctly model the expected values. Our approach instead employs an algorithm that requires no prior modeling, allowing nodes to work independently and without using global model parameters. Using the Least-Mean-Square (LMS) adaptive algorithm on a publicly available, real-world (office environment) temperature data set, we have been able to achieve up to 92% communication reduction while maint...

2007

A physical data (such as astrophysical, geophysical, meteorological etc.) may appear as an output of an experiment or it may come out as a signal from a dynamical system or it may contain some sociological, economic or biological information. Whatever be the source of a time series data some amount of noise is always expected to be embedded in it. Analysis of such data in presence of noise may often fail to give accurate information. The method of filtering a time series data is a tool to clean these errors as possible as we can just to make the data compatible for further analysis. Here we made an attempt to develop an adaptive approach of filtering a time series and we have shown analytically that the present model can fight against the propagation of error and can maintain the positional importance in the time series very efficiently.

Adaptive Filtering Applications, 2011

InTech eBooks, 2011

Lino García Morales has graduated in Automatic Control Engineering at Polytechnic Institute "José A. Echeverría". He has received a master's degree in Systems and Communications Networks at

IEEE Transactions on Signal Processing, 2004

Most adaptive filtering algorithms couple performance with complexity. Over the last 15 years, a class of algorithms, termed "affine projection" algorithms, have given system designers the capability to tradeoff performance with complexity. By changing parameters and the size/scale of data used to update the coefficients of an adaptive filter but without fundamentally changing the algorithm structure, a system designer can radically change the performance of the adaptive algorithm. This paper discusses low-complexity data reusing algorithms that are closely related to affine projection algorithms. This paper presents various low-complexity and highly flexible schemes for improving convergence rates of adaptive algorithms that utilize data reusing strategies. All of these schemes are unified by a row projection framework in existence for more than 65 years. This framework leads to the classification of all data reusing and affine projection methods for adaptive filtering into two categories: the Kaczmarz and Cimmino methods. Simulation and convergence analysis results are presented for these methods under a number of conditions. They are compared in terms of convergence rate performance and computational complexity.