Related papers: Improved Soft-k-Means Clustering Algorithm for Bal…
Federated clustering, an integral aspect of federated machine learning, enables multiple data sources to collaboratively cluster their data, maintaining decentralization and preserving privacy. In this paper, we introduce a novel federated…
Sensor network has been recognized as the most significant technology for next century. Despites of its potential application, wireless sensor network encounters resource restriction such as low power, reduced bandwidth and specially…
In WSN (Wireless Sensor Network) every sensor node sensed the data and transmit it to the CH (Cluster head) or BS (Base Station). Sensors are randomly deployed in unreachable areas, where battery replacement or battery charge is not…
The use of mobile phones has exploded over the past years,abundantly through the introduction of smartphones and the rapidly expanding use of mobile data. This has resulted in a spiraling problem of ensuring quality of service for users of…
Clustering is a popular form of unsupervised learning for geometric data. Unfortunately, many clustering algorithms lead to cluster assignments that are hard to explain, partially because they depend on all the features of the data in a…
Energy efficiency is a crucial performance metric in sensor networks, directly determining the network lifetime. Consequently, a key factor in WSN is to improve overall energy efficiency to extend the network lifetime. Although many…
In this paper, we provide an energy efficient self- healing mechanism for Wireless Sensor Networks. The proposed solution is based on our probabilistic sentinel scheme. To reduce energy consumption while maintaining good connectivity…
Kernel-based clustering algorithm can identify and capture the non-linear structure in datasets, and thereby it can achieve better performance than linear clustering. However, computing and storing the entire kernel matrix occupy so large…
Clustering is a widely used and powerful machine learning technique, but its effectiveness is often limited by the need to specify the number of clusters, k, or by relying on thresholds that implicitly determine k. We introduce k*-means, a…
A wireless sensor network has a wide application domain which is expanding everyday and they have been deployed pertaining to their application area. An application independent approach is yet to come to terms with the ongoing exploitation…
In this paper, we investigate the learning-augmented $k$-median clustering problem, which aims to improve the performance of traditional clustering algorithms by preprocessing the point set with a predictor of error rate $\alpha \in [0,1)$.…
From energy conservation perspective inWireless Sensor Networks (WSNs), clustering of sensor nodes is a challenging task. Clustering technique in routing protocols play a key role to prolong the stability period and lifetime of the network.…
Energy efficient routing protocols are consistently cited as efficient solutions for Wireless Sensor Networks (WSNs) routing. The area of WSNs is one of the emerging and fast growing fields which brought low cost, low power and…
This paper presents a clustering technique that reduces the susceptibility to data noise by learning and clustering the data-distribution and then assigning the data to the cluster of its distribution. In the process, it reduces the impact…
In order to understand the underlying mechanisms that lead to certain network properties (i.e. scalability, energy efficiency) we apply a complex systems science approach to analyze clustering in Wireless Sensor Networks (WSN). We represent…
One of the applications of center-based clustering algorithms such as K-Means is partitioning data points into K clusters. In some examples, the feature space relates to the underlying problem we are trying to solve, and sometimes we can…
The widespread adoption of smart meters for monitoring energy consumption has generated vast quantities of high-resolution time series data which remains underutilised. While clustering has emerged as a fundamental tool for mining smart…
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…
K-means clustering, as a classic unsupervised machine learning algorithm, is the key step to select the interpolation sampling points in interpolative separable density fitting (ISDF) decomposition. Real-valued K-means clustering for…
Wireless Sensor Networks (WSNs), with growing applications in the environment which are not within human reach have been addressed tremendously in the recent past. For optimized working of network many routing algorithms have been proposed,…