A dominance-based stepwise approach for sensor placement optimization

Ko, Albert Hung-Ren; Jousselme, Anne-Laure; Sabourin, Robert; Gagnon, François

doi:10.1016/j.asoc.2014.11.051

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…Our main objective is to drastically reduce the number of antennas (measurements points) for reaching real-time operation. In [3], sensor placement is optimized in a wireless sensor network with a step-wise optimization approach; but optimizing the placement of the radar sensors independently of each other is a brand new outcome.…”

Section: Introductionmentioning

confidence: 99%

GWO-Based Joint Optimization of Millimeter-Wave System and Multilayer Perceptron for Archaeological Application

Marot,

Zidane,

El-Abed

et al. 2024

Sensors

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Recently, low THz radar-based measurement and classification for archaeology emerged as a new imaging modality. In this paper, we investigate the classification of pottery shards, a key enabler to understand how the agriculture was introduced from the Fertile Crescent to Europe. Our purpose is to jointly design the measuring radar system and the classification neural network, seeking the maximal compactness and the minimal cost, both directly related to the number of sensors. We aim to select the least possible number of sensors and place them adequately, while minimizing the false recognition rate. For this, we propose a novel version of the Binary Grey Wolf Optimizer, designed to reduce the number of sensors, and a Ternary Grey Wolf Optimizer. Together with the Continuous Grey Wolf Optimizer, they yield the CBTGWO (Continuous Binary Ternary Grey Wolf Optimizer). Working with 7 frequencies and starting with 37 sensors, the CBTGWO selects a single sensor and yields a 0-valued false recognition rate. In a single-frequency scenario, starting with 217 sensors, the CBTGWO selects 2 sensors. The false recognition rate is 2%. The acquisition time is 3.2 s, outperforming the GWO and adaptive mixed GWO, which yield 86.4 and 396.6 s.

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Section: Introductionmentioning

confidence: 99%