Energy-Efficient Signal Classification in <i>Ad hoc</i> Wireless Sensor Networks

Pianegiani, F.; Hu, Mingyou; Boni, Andrea; Petri, Dario

doi:10.1109/tim.2007.908344

Cited by 26 publications

(19 citation statements)

References 13 publications

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“…This data set includes the raw time series data observed at each individual sensor node and a set of acoustic feature vectors extracted from each sensors microphone. This data set has also been used in [20].…”

Section: Methodsmentioning

confidence: 99%

An Improved Reduced Set Method to Control the Run-time Complexity of SVM in Wireless Sensor Networks

Boni

2006

2006 IEEE Conference on Emerging Technologies and Factory Automation

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One prominent disadvantage of SVM when implemented in wireless sensor networks (WSNs) is the run-time complexity of classifier, which linearly increases with the number of support vectors (SVs). This disadvantage prevents applying SVM in some applications. In this paper we propose an improved reduced set method to find solutions characterized by few number of vectors and having good generalization properties. The idea behind our improved method is to combine finding patterns with maximum absolute margin and performing gradient-descent to find new patterns in new decision function. Our method can partially overcome the non-convexity difficulty. The application context is that of WSNs, where a general sensor node is equipped with fixed point CPU. The performance of fixed point implementation of our algorithm is also provided.

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Section: Methodsmentioning

confidence: 99%

An Improved Reduced Set Method to Control the Run-time Complexity of SVM in Wireless Sensor Networks

Boni

2006

2006 IEEE Conference on Emerging Technologies and Factory Automation

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“…At higher transmission rate, it performs better than B-MAC due to more efficient contention resolution. Pianegiani' 2008 Uses signals classification to reduce amount of transmitted data based upon computational latency overhead [33].…”

Section: Fixed Lpl Schemes B-macmentioning

confidence: 99%

Power consumption in wireless sensor networks

Aslam

Farooq

Sarwar

2009

Proceedings of the 7th International Conference on Frontiers of Information Technology

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In wireless sensor networks (WSNs), long lifetime requirement of different applications and limited energy storage capability of sensor nodes has led us to find out new horizons for reducing power consumption upon nodes. To increase sensor node's lifetime, circuit and protocols have to be energy efficient so that they can make a priori reactions by estimating and predicting energy consumption. The goal of this study is to present and discuss several strategies such as power-aware protocols, cross-layer optimization, and harvesting technologies used to alleviate power consumption constraint in WSNs.

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“…Due to the limited resources for sensing, communication and computation in WSNs, energy-efficient collaborative signal processing algorithms are needed [4,6]. The localization problem has received considerable attention recently [7,8,9,10].…”

Section: Introductionmentioning

confidence: 99%

Energy-Based Acoustic Source Localization Methods: A Survey

Meng

Xiao

2017

Sensors

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Abstract:Energy-based source localization is an important problem in wireless sensor networks (WSNs), which has been studied actively in the literature. Numerous localization algorithms, e.g., maximum likelihood estimation (MLE) and nonlinear-least-squares (NLS) methods, have been reported. In the literature, there are relevant review papers for localization in WSNs, e.g., for distance-based localization. However, not much work related to energy-based source localization is covered in the existing review papers. Energy-based methods are proposed and specially designed for a WSN due to its limited sensor capabilities. This paper aims to give a comprehensive review of these different algorithms for energy-based single and multiple source localization problems, their merits and demerits and to point out possible future research directions.

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Energy-Efficient Signal Classification in Ad hoc Wireless Sensor Networks

Cited by 26 publications

References 13 publications

An Improved Reduced Set Method to Control the Run-time Complexity of SVM in Wireless Sensor Networks

An Improved Reduced Set Method to Control the Run-time Complexity of SVM in Wireless Sensor Networks

Power consumption in wireless sensor networks

Energy-Based Acoustic Source Localization Methods: A Survey

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