Dynamic target classification in wireless sensor networks

Sun, Yanhua; Qi, Hairong

doi:10.1109/icpr.2008.4761292

Cited by 10 publications

(4 citation statements)

References 28 publications

(3 reference statements)

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“…Recently, several research works regarding in-network classification have been proposed. Sun et al [29] dynamically select the feature space in order to accelerate the classification process. A hybrid sensor networks is designed by Hu et al [4] for in-network and energy-efficient classification in order to monitor amphibian population.…”

Section: Related Workmentioning

confidence: 99%

A multi-view CNN-based acoustic classification system for automatic animal species identification

Zhang

Yao

et al. 2020

Ad Hoc Networks

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Automatic identification of animal species by their vocalization is an important and challenging task. Although many kinds of audio monitoring system have been proposed in the literature, they suffer from several disadvantages such as non-trivial feature selection, accuracy degradation because of environmental noise or intensive local computation. In this paper, we propose a deep learning based acoustic classification framework for Wireless Acoustic Sensor Network (WASN). The proposed framework is based on cloud architecture which relaxes the computational burden on the wireless sensor node. To improve the recognition accuracy, we design a multi-view Convolution Neural Network (CNN) to extract the short-, middle-, and long-term dependencies in parallel. The evaluation on two real datasets shows that the proposed architecture can achieve high accuracy and outperforms traditional classification systems significantly when the environmental noise dominate the audio signal (low SNR). Moreover, we implement and deploy the proposed system on a testbed and analyse the system performance in real-world environments. Both simulation and real-world evaluation demonstrate the accuracy and robustness of the proposed acoustic classification system in distinguishing species of animals.

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Section: Related Workmentioning

confidence: 99%

A multi-view CNN-based acoustic classification system for automatic animal species identification

Zhang

Yao

et al. 2020

Ad Hoc Networks

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“…For the purpose of maximizing network lifetime, several different mechanisms were investigated based on different assumption about the sensors including sensing area, network structure, transmission range and etc. Sun Ying and Qi Hairong [5] have investigated the dynamic target classification in wireless sensor networks. They dynamically select the optimal combination of features and classifiers, according to the probability that the target might belong to a certain category.…”

Section: Introductionmentioning

confidence: 99%

An energy-efficient approach for target recognition with synchronous sampling sensors

Hao

Liu

2014

2014 IEEE 5th International Conference on Software Engineering and Service Science

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With rapid improvements in computer and software engineering, collaborative target recognition has become an active area of research in wireless sensor network community. Typically, each sensor node is designed to work using its very restrict battery capacity and limited computational resources, while the base station usually has sufficient energy supply and computational power. Hence, minimizing energy consumption of sensors while maintaining a given classification accuracy is a key issue in this research area. This paper proposes an energyefficient approach for target recognition in synchronous sampling wireless sensor network, inspired by the autonomy of agents in the sensors. The experiments show that the energy consumption of the system can noticeably be reduced without losing much recognition rate.

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“…Recently, researchers have proposed classification methods for resource constrained environment according to these characteristics of WSNs. Sun [33] dynamically picks a part of the feature space rather than the entire one after feature selection to accelerate the classification. This method o↵ers a good accuracy.…”

Section: Classification On Wireless Sensor Networkmentioning

confidence: 99%

Real-time classification via sparse representation in acoustic sensor networks

Wei

Yang

Shen

et al. 2013

Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems

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Acoustic Sensor Networks (ASNs) have a wide range of applications in natural and urban environment monitoring, as well as indoor activity monitoring. In-network classification is critically important in ASNs because wireless transmission costs several orders of magnitude more energy than computation. The main challenges of in-network classification in ASNs include e↵ective feature selection, intensive computation requirement and high noise levels. To address these challenges, we propose a sparse representation based featureless, low computational cost, and noise resilient framework for in-network classification in ASNs. The key component of Sparse Approximation based Classification (SAC),`1 minimization, is a convex optimization problem, and is known to be computationally expensive. Furthermore, SAC algorithms assumes that the test samples are a linear combination of a few training samples in the training sets. For acoustic applications, this results in a very large training dictionary, making the computation infeasible to be performed on resource constrained ASN platforms. Therefore, we propose several techniques to reduce the size of the problem, so as to fit SAC for in-network classification in ASNs. Our extensive evaluation using two real-life datasets (consisting of calls from 14 frog species and 20 cricket species respectively) shows that the proposed SAC framework outperforms conventional approaches such as Support Vector Machines (SVMs) and k-Nearest Neighbor (kNN) in terms of classification accuracy and robustness. Moreover, our SAC approach can deal with multi-label classification which is common in ASNs. Finally, we explore the system design spaces and demonstrate the real-time feasibility of the proposed framework by the implementation and evaluation of an acoustic classification application on an embedded ASN testbed.

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Dynamic target classification in wireless sensor networks

Cited by 10 publications

References 28 publications

A multi-view CNN-based acoustic classification system for automatic animal species identification

A multi-view CNN-based acoustic classification system for automatic animal species identification

An energy-efficient approach for target recognition with synchronous sampling sensors

Real-time classification via sparse representation in acoustic sensor networks

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