Related papers: FCA - An Approach On LEACH Protocol Of Wireless Se…

We consider the problem of energy balancing in a clustered wireless sensor network (WSN) deployed randomly in a large field and aided by a mobile robot (MR). The sensor nodes (SNs) are tasked to monitor a region of interest (ROI) and report…

The present work considers the localization problem in wireless sensor networks formed by fixed nodes. Each node seeks to estimate its own position based on noisy measurements of the relative distance to other nodes. In a centralized batch…

Network congestion has become a critical issue for resource constrained Wireless Sensor Networks (WSNs), especially for Wireless Multimedia Sensor Networks (WMSNs)where large volume of multimedia data is transmitted through the network. If…

Wireless sensor nodes along with Base Station (BS) constitute a Wireless Sensor Network (WSN). Nodes comprise of tiny power battery. Nodes sense the data and send it to BS. WSNs need protocol for efficient energy consumption of the network.…

In this paper we introduce a fuzzy constraint linear discriminant analysis (FC-LDA). The FC-LDA tries to minimize misclassification error based on modified perceptron criterion that benefits handling the uncertainty near the decision…

Network lifetime and energy consumption of data transmission have been primary Quality of Service (QoS) obligations in Wireless Sensor Networks (WSNs). The environment of a WSN is often organized into clusters to mitigate the management…

The existence of large volumes of time series data in many applications has motivated data miners to investigate specialized methods for mining time series data. Clustering is a popular data mining method due to its powerful exploratory…

The clustering algorithm are considered as a kind of key technique used to reduce energy consumption. It can help in increasing the stability period and network life time. Routing protocol for efficient energy utilization should be designed…

Clustering is one of the major roles in data mining that is widely application in pattern recognition and image segmentation. Fuzzy C-means (FCM) is the most used clustering algorithm that proven efficient, fast and easy to implement,…

For some applications, we need to deploy a network of sensors in working field to sense the environment and send collected data to a base station for processing; these sensors depend on non-rechargeable batteries, so the routing protocols…

Objective: The main objective of this paper is to construct a distributed clustering algorithm based upon spatial data correlation among sensor nodes and perform data accuracy for each distributed cluster at their respective cluster head…

Energy preservation is one of the most important challenges in wireless sensor networks. In most applications, sensor networks consist of hundreds or thousands nodes that are dispersed in a wide field. Hierarchical architectures and data…

Wireless sensor/actuator networks (WSANs) are emerging rapidly as a new generation of sensor networks. Despite intensive research in wireless sensor networks (WSNs), limited work has been found in the open literature in the field of WSANs.…

Clustering algorithms are fundamental tools across many fields, with density-based methods offering particular advantages in identifying arbitrarily shaped clusters and handling noise. However, their effectiveness is often limited by the…

Clustering techniques create hierarchal network structures, called clusters, on an otherwise flat network. In a dynamic environment-in terms of node mobility as well as in terms of steadily changing device parameters-the clusterhead…

Feature selection is a vital technique in machine learning, as it can reduce computational complexity, improve model performance, and mitigate the risk of overfitting. However, the increasing complexity and dimensionality of datasets pose…

Balanced energy consumption is a major research concern in sensor networks. If some sensor nodes spent energy rapidly compared to other sensor groups, then the energy consumption is not evenly distributed and the lifetime of the network is…

Federated Learning (FL) is a setting where multiple parties with distributed data collaborate in training a joint Machine Learning (ML) model while keeping all data local at the parties. Federated clustering is an area of research within FL…

In this paper, we aim to improve the robustness of Keyword Spotting (KWS) systems in noisy environments while keeping a small memory footprint. We propose a new convolutional neural network (CNN) called FCA-Net, which combines mixer…

Objective-The main purpose of this paper is to construct a distributed clustering algorithm such that each distributed cluster can perform the data accuracy at their respective cluster head node before data aggregation and transmit the data…