Lightweight feature extraction method for efficient acoustic-based animal recognition in wireless acoustic sensor networks

Al-Quayed, Fatima; Soudani, Adel; Al-Ahmadi, Saad

doi:10.1186/s13638-020-01878-z

Cited by 3 publications

(3 citation statements)

References 31 publications

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“…Prior to localization estimation, the acoustic target is assumed to be detected in the sensor field (i.e., cluster) according to a threshold-based event detection algorithm [11]. During network operation, member sensor nodes are responsible for periodic sensing of the surveillance area to detect the presence of an acoustic event within their sensing range.…”

Section: The General Acoustic-based Target Localization Approachmentioning

confidence: 99%

“…In this paper, we propose a new localization scheme for a single acoustic target that appears in the area monitored by a set of homogeneous, conventional, and stationary acoustic sensors. The acoustic target is assumed to be detected in the sensor field (i.e., cluster) using the threshold-based event detection algorithm [11]. The nodes, which have detected the new acoustic source, will report their local RSS observations to a fusion center of the cluster in a centralized manner.…”

Section: Introductionmentioning

confidence: 99%

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Design of a New Lightweight Accurate Grid-Based Localization Method for WASN

2021

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Existing solutions designed for acoustic target localization based on the received signal energy in WSN suffer either from low localization accuracy due to suboptimal convergence or high computations requirements due to the application of complex algorithms. The main contribution of this paper is the design of a new lightweight energy-based acoustic source localization solution intended for implementation in wireless sensors. The novelty of this solution resides in devising lightweight computation of the cost function in the location estimator algorithm and the application of the Tabu metaheuristic method to enhance the localization accuracy. In terms of localization accuracy, the proposed scheme outperforms many existing solutions with reduced localization error by up to 88% while using only three energy measurements, which saves around 85% of node energy. Furthermore, the performance analysis results showed a stable behavior of the proposed algorithm across a wide range of parameters variations of the energy-decay model, which demonstrates the robustness of the scheme.

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Section: The General Acoustic-based Target Localization Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design of a New Lightweight Accurate Grid-Based Localization Method for WASN

2021

Self Cite

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“…However, factors such as energy consumption of the nodes and other parameters must be taken into account. The use of large vehicles such as trucks or helicopters incurs high costs and requires highly specialized human intervention [15][16][17]. Moreover, the natural habitats of animals are likely to be disturbed.…”

Section: Introductionmentioning

confidence: 99%

Phase-Type Distributions of Animal Trajectories with Random Walks

Vera-Amaro,

Rivero-Ángeles,

Luviano-Juárez

2023

Mathematics

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Animal monitoring systems often rely on expensive and challenging GPS-based systems to obtain accurate trajectories. However, an alternative approach is to generate synthetic trajectories that exhibit similar statistical properties to real trajectories. These synthetic trajectories can be used effectively in the design of surveillance systems such as wireless sensor networks and drone-based techniques, which aid in data collection and the delineation of areas for animal conservation and reintroduction efforts. In this study, we propose a data generation method that utilizes simple phase-type distributions to produce synthetic animal trajectories. By employing probability distribution functions based on the exponential distribution, we achieve highly accurate approximations of the movement patterns of four distinct animal species. This approach significantly reduces processing time and complexity. The research primarily focuses on generating animal trajectories for four endangered species, comprising two terrestrial and two flying species, in order to demonstrate the efficacy of the proposed method.

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Method of feature extraction of abnormal communication signal in network based on nonlinear technology

Gong

2024

Nonlinear Engineering

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Accurate extraction of abnormal communication signal features in the network is the basis to ensure the completion of network communication. Therefore, a method of feature extraction of abnormal communication signal in network based on nonlinear technology is proposed. In this method, wavelet transform is adopted to decompose the abnormal network communication signals in the high- and low-frequency bands. According to the distribution characteristics of noise and signal in the frequency band, the corresponding parameters are selected for phase space reconstruction and nonlinear dimension reduction of local tangent space mainstream shape recognition algorithm, and the decomposition coefficients of wavelet packet after noise reduction are reconstructed to realize the nonlinear noise reduction of abnormal signal; the denoised abnormal communication signal in network is mapped to the high-dimensional feature space. The principal component is analyzed in accordance with the nonlinear function in the mapped feature space, and the nonlinear function is solved by self-organizing neural network to output the principal component extraction result. According to test results, this method has a significant signal noise reduction effect, results are more than 92% for different abnormal communication signals, and the features of abnormal signals are accurately extracted.

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Lightweight feature extraction method for efficient acoustic-based animal recognition in wireless acoustic sensor networks

Cited by 3 publications

References 31 publications

Design of a New Lightweight Accurate Grid-Based Localization Method for WASN

Design of a New Lightweight Accurate Grid-Based Localization Method for WASN

Phase-Type Distributions of Animal Trajectories with Random Walks

Method of feature extraction of abnormal communication signal in network based on nonlinear technology

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