Machine learning for coverage optimization in wireless sensor networks: a comprehensive review
In the context of wireless sensor networks (WSNs), the utilization of artificial intelligence (AI)-
based solutions and systems is on the ascent. These technologies offer significant potential …
based solutions and systems is on the ascent. These technologies offer significant potential …
Strategies to measure soil moisture using traditional methods, automated sensors, remote sensing, and machine learning techniques: review, bibliometric analysis …
This review provides a detailed synthesis of various in-situ, remote sensing, and machine
learning approaches to estimate soil moisture. Bibliometric analysis of the published …
learning approaches to estimate soil moisture. Bibliometric analysis of the published …
Deep learning and data fusion to estimate surface soil moisture from multi-sensor satellite images
We propose a new architecture based on a fully connected feed-forward Artificial Neural
Network (ANN) model to estimate surface soil moisture from satellite images on a large …
Network (ANN) model to estimate surface soil moisture from satellite images on a large …
AutoML-GWL: Automated machine learning model for the prediction of groundwater level
Predicting groundwater levels is pivotal in curbing overexploitation and ensuring effective
water resource governance. However, groundwater level prediction is intricate, driven by …
water resource governance. However, groundwater level prediction is intricate, driven by …
A deep learning approach to predict the number of k-barriers for intrusion detection over a circular region using wireless sensor networks
Abstract Wireless Sensor Networks (WSNs) is a promising technology with enormous
applications in almost every walk of life. One of the crucial applications of WSNs is intrusion …
applications in almost every walk of life. One of the crucial applications of WSNs is intrusion …
[HTML][HTML] A deep learning based feed forward artificial neural network to predict the K-barriers for intrusion detection using a wireless sensor network
Abstract Wireless Sensor Networks (WSN) has a wide range of opportunities and can be
applied to almost every part of our life. Intrusion detection and monitoring across disputed …
applied to almost every part of our life. Intrusion detection and monitoring across disputed …
P2CA-GAM-ID: Coupling of probabilistic principal components analysis with generalised additive model to predict the k− barriers for intrusion detection
Drastic advancement in computing technology and the dramatic increase in the usage of
explainable machine learning algorithms provide a promising platform for developing robust …
explainable machine learning algorithms provide a promising platform for developing robust …
A machine learning approach to predict the k-coverage probability of wireless multihop networks considering boundary and shadowing effects
Network coverage is a pivotal performance metric of wireless multihop networks (WMNs)
determining the quality of service rendered by the network. Earlier, a few studies have …
determining the quality of service rendered by the network. Earlier, a few studies have …
Design of advanced intrusion detection systems based on hybrid machine learning techniques in hierarchically wireless sensor networks
Wireless sensor networks (WSNs) are an emerging military and civilian technology that uses
sensors. Sensor networks are hierarchical and chaotic in remote, unmonitored sites …
sensors. Sensor networks are hierarchical and chaotic in remote, unmonitored sites …
Intelligent IDS in wireless sensor networks using deep fuzzy convolutional neural network
S Subramani, M Selvi - Neural Computing and Applications, 2023 - Springer
The intrusion detection systems (IDSs) developed based on classification algorithms for
securing wireless sensor networks (WSNs) are unable to attain the required detection …
securing wireless sensor networks (WSNs) are unable to attain the required detection …