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New Online DV-Hop Algorithm via Mobile Anchor for Wireless Sensor Network Localization

Tsinghua Science and Technology 2023年第5期28卷 940-951页

作者： Oumaima Liouane Smain Femmam Toufik Bakir Abdessalem Ben Abdelali EμE laboratory University of MonastirMonastir 5000Tunisia Department of Networks and Communications University of Haute-AlsaceMulhouse 68100France imvia laboratory University of Burgundy Franche-ComtéDijon 21078France

In many applications of Wireless Sensor Networks(WSNs),event detection is the main purpose of ***,determining where and when that event occurs is crucial;thus,the positions of nodes must be ***,in a range-free case,the Distance Vector-Hop(DV-Hop)heuristic is the commonly used localization algorithm because of its simplicity and low *** DV-Hop algorithm consists of a set of reference nodes,namely,anchors,to periodically broadcast their current positions and assist nearby unknown nodes during *** potential solution includes the use of only one mobile anchor instead of these sets of *** solution presents a new challenge in the localization of rang-free WSNs because of its favorable results and reduced *** this paper,we propose an analytical probabilistic model for multi-hop distance estimation between mobile anchor nodes and unknown *** derive a non-linear analytic function that provides the relation between the hop counts and distance ***,based on the recursive least square algorithm,we present a new formulation of the original DV-Hop localization algorithm,namely,online DV-Hop localization,in ***,different scenarios of path planning and simulation results are conducted.

关键词： Wireless Sensor Networks(WSNs) mobile anchor online localization path planning

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A deep semantic segmentation-based algorithm to segment crops and weeds in agronomic color images

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Information Processing in Agriculture 2022年第3期9卷 355-364页

作者： Sovi Guillaume Sodjinou Vahid Mohammadi Amadou Tidjani Sanda Mahama Pierre Gouton Institute of Mathematics and Physical Sciences University of Aboney CalaviDangboBenin imvia UFR Sciences et TechniquesUniversite de BourgogneFranche-ComteDijonFrance

In precision agriculture,the accurate segmentation of crops and weeds in agronomic images has always been the center of *** methods have been proposed but still the clean and sharp segmentation of crops and weeds is a challenging issue for the images with a high presence of *** work proposes a segmentation method based on the combination of semantic segmentation and K-means algorithms for the segmenta-tion of crops and weeds in color *** images of two different databases were used for the segmentation *** the thresholding technique,everything except plants was removed from the ***,semantic segmentation was applied using U-net followed by the segmentation of crops and weeds using the K-means subtractive *** comparison of segmentation performance was made for the proposed method and K-Means clustering and superpixels *** proposed algorithm pro-vided more accurate segmentation in comparison to other methods with the maximum accuracy of equivalent to 99.19%.Based on the confusion matrix,the true-positive and true-negative values were 0.9952 and 0.8985 representing the true classification rate of crops and weeds,*** results indicated that the proposed method successfully provided accurate and convincing results for the segmentation of crops and weeds in the images with a complex presence of weeds.

关键词： Weed coverage Semantic segmentation Convolutional neural network Subtractive clustering algorithm Simple Linear Iterative Clustering (SLIC) K-means

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