Drones as Sound Sensors for Energy-Based Acoustic Tracking on Wildfire Environments

Correia, Sérgio D.; Fé, João; Tomic, Slaviša; Beko, Marko

doi:10.1007/978-3-030-96466-5_8

Cited by 6 publications

(2 citation statements)

References 31 publications

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“…There are many applications in which efficiently (accurately and promptly) solving the localization problem is crucial, such as navigation [21], underwater networks [22], surveillance [23,24], or power systems [25,26]. When considering the fourth industrial transformation and the fundamental advanced digital changes-known as Industry 4.0robust and precise localization can be seen as a key feature in pervasive systems in future industry and factory applications.…”

Section: Introductionmentioning

confidence: 99%

Swarm Optimization for Energy-Based Acoustic Source Localization: A Comprehensive Study

Fé

Correia

Tomic

et al. 2022

Sensors

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In the last decades, several swarm-based optimization algorithms have emerged in the scientific literature, followed by a massive increase in terms of their fields of application. Most of the studies and comparisons are restricted to high-level languages (such as MATLAB®) and testing methods on classical benchmark mathematical functions. Specifically, the employment of swarm-based methods for solving energy-based acoustic localization problems is still in its inception and has not yet been extensively studied. As such, the present work marks the first comprehensive study of swarm-based optimization algorithms applied to the energy-based acoustic localization problem. To this end, a total of 10 different algorithms were subjected to an extensive set of simulations with the following aims: (1) to compare the algorithms’ convergence performance and recognize novel, promising methods for solving the problem of interest; (2) to validate the importance (in convergence speed) of an intelligent swarm initialization for any swarm-based algorithm; (3) to analyze the methods’ time efficiency when implemented in low-level languages and when executed on embedded processors. The obtained results disclose the high potential of some of the considered swarm-based optimization algorithms for the problem under study, showing that these methods can accurately locate acoustic sources with low latency and bandwidth requirements, making them highly attractive for edge computing paradigms.

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Section: Introductionmentioning

confidence: 99%

Swarm Optimization for Energy-Based Acoustic Source Localization: A Comprehensive Study

Fé

Correia

Tomic

et al. 2022

Sensors

Self Cite

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“…Examples of successful applications can include simple domestic tasks [1], border control [2], and coast patrol [3], among several other examples that could be mentioned. In addition, UAVs can work in conditions unsuitable for a human, such as emergencies [4]. Thus, drones are more effective and require fewer resources in the context of territory monitoring.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization Embedded in UAV as a Method of Territory-Monitoring Efficiency Improvement

et al. 2022

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Unmanned aerial vehicles have large prospects for organizing territory monitoring. To integrate them into this sphere, it is necessary to improve their high functionality and safety. Computer vision is one of the vital monitoring aspects. In this paper, we developed and validated a methodology for terrain classification. The overall classification procedure consists of the following steps: (1) pre-processing, (2) feature extraction, and (3) classification. For the pre-processing stage, a clustering method based on particle swarm optimization was elaborated, which helps to extract object patterns from the image. Feature extraction is conducted via Gray-Level Co-Occurrence Matrix calculation, and the output of the matrix is turned into the input for a feed-forward neural network classification stage. The developed computer vision system showed 88.7% accuracy on the selected test set. These results can provide high quality territory monitoring; prospectively, we plan to establish a self-positioning system based on computer vision.

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Convolutional Neural Networks for Autonomous UAV Navigation in GPS-Denied Environments

Santos,

Matos-Carvalho,

Tomic

et al. 2024

IFIP Advances in Information and Communication Technology

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Drones as Sound Sensors for Energy-Based Acoustic Tracking on Wildfire Environments

Cited by 6 publications

References 31 publications

Swarm Optimization for Energy-Based Acoustic Source Localization: A Comprehensive Study

Swarm Optimization for Energy-Based Acoustic Source Localization: A Comprehensive Study

Particle Swarm Optimization Embedded in UAV as a Method of Territory-Monitoring Efficiency Improvement

Convolutional Neural Networks for Autonomous UAV Navigation in GPS-Denied Environments

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