scispace - formally typeset
Proceedings ArticleDOI

Energy Efficient Quad Clustering based on K-means Algorithm for Wireless Sensor Network

TLDR
In this paper, the authors proposed energy efficient clustering i.e quad clustering based on K-means algorithm, which improves the performance of wireless sensor network in terms of network lifetime.
Abstract
A collection of sensor nodes are available in wireless sensor network for gathering the distinguish data from environment. This sensing process consumes more energy of the network which effects the whole network life time. So energy usage in efficient manner is the main issue to maintaining the network. Clustering is the process used for reducing the energy consumption. K-means is the post popular clustering algorithm to form the clusters. In this paper, propose energy efficient clustering i.e quad clustering based on K-means algorithm. This approach improves the performance of wireless sensor network in terms of network lifetime. As simulation shows the proposed work is better than single cluster in case of distance coverage as well as energy consumption.

read more

Citations
More filters
Journal ArticleDOI

Impairments-aware time slot allocation model for energy-constrained multi-hop clustered IoT nodes considering TDMA and DSSS MAC protocols

TL;DR: A multi-hop routing (MR) protocol in which the IoT network is divided into a number of virtual sections with lengths held below the cross-over communication distance, thereby ensuring minimal energy depletion is proposed.
Proceedings ArticleDOI

An Improved Energy Efficient Clustering Protocol for Wireless Sensor Networks

TL;DR: In this paper , the authors presented an Energy-Saving Clustering Algorithm (ESCA) to reduce energy consumption and increase the network's lifetime, which is based on cluster construction that is centralized and cluster heads that are distributed.
Journal ArticleDOI

A Competition-Based Unequal Clustering Multihop Approach for Wireless Sensor Networks

TL;DR: In this article, a competition-based unequal clustering multihop approach (CUCMA) is proposed to balance the energy consumption of CHs and reduce the energy usage of the whole network.
Proceedings ArticleDOI

Smart Urban Traffic Management System using Energy Efficient Optimized Path Discovery

P Agarwal, +1 more
TL;DR: In this article , the authors discuss about the methods for analyzing real-time traffic data, the algorithms that provide a realtime, energy-efficient, and optimal path from the source to the destination depending on the traffic.
References
More filters
Journal ArticleDOI

Wireless sensor networks: a survey

TL;DR: The concept of sensor networks which has been made viable by the convergence of micro-electro-mechanical systems technology, wireless communications and digital electronics is described.
Proceedings ArticleDOI

Energy-efficient communication protocol for wireless microsensor networks

TL;DR: The Low-Energy Adaptive Clustering Hierarchy (LEACH) as mentioned in this paper is a clustering-based protocol that utilizes randomized rotation of local cluster based station (cluster-heads) to evenly distribute the energy load among the sensors in the network.
Proceedings ArticleDOI

Wireless sensor networks for habitat monitoring

TL;DR: An in-depth study of applying wireless sensor networks to real-world habitat monitoring and an instance of the architecture for monitoring seabird nesting environment and behavior is presented.
Journal ArticleDOI

A survey on clustering algorithms for wireless sensor networks

TL;DR: A taxonomy and general classification of published clustering schemes for WSNs is presented, highlighting their objectives, features, complexity, etc and comparing of these clustering algorithms based on metrics such as convergence rate, cluster stability, cluster overlapping, location-awareness and support for node mobility.
Proceedings ArticleDOI

K-Means Clustering in Wireless Sensor Networks

TL;DR: This work implemented both centralized and distributed k-means clustering algorithm in network simulator and results show that distributed clustering is efficient than centralized clustering.
Related Papers (5)