Related Experiment Video
Updated: Mar 16, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
Published on: September 8, 2023
Clustering and Beamforming for Efficient Communication in Wireless Sensor Networks
Francisco Porcel-Rodríguez1, Juan Valenzuela-Valdés2, Pablo Padilla3
1Department of Signal Theory, Telematics and Communications-CITIC, University of Granada, 18071 Granada, Spain. franciscoporcel@correo.ugr.es.
Abstract:
Energy efficiency is a critical issue for wireless sensor networks (WSNs) as sensor nodes have limited power availability. In order to address this issue, this paper tries to maximize the power efficiency in WSNs by means of the evaluation of WSN node networks and their performance when both clustering and antenna beamforming techniques are applied. In this work, four different scenarios are defined, each one considering different numbers of sensors: 50, 20, 10, five, and two nodes per scenario, and each scenario is randomly generated thirty times in order to statistically validate the results. For each experiment, two different target directions for transmission are taken into consideration in the optimization process (φ = 0° and θ = 45°; φ = 45°, and θ = 45°). Each scenario is evaluated for two different types of antennas, an ideal isotropic antenna and a conventional dipole one. In this set of experiments two types of WSN are evaluated: in the first one, all of the sensors have the same amount of power for communications purposes; in the second one, each sensor has a different amount of power for its communications purposes. The analyzed cases in this document are focused on 2D surface and 3D space for the node location. To the authors' knowledge, this is the first time that beamforming and clustering are simultaneously applied to increase the network lifetime in WSNs.
Related Concept Videos
Beams with Symmetric Loadings
The M/EI...
Beams with Unsymmetric Loadings
The first moment-area theorem determines the slope at any point on the beam. This theorem indicates that the change in slope between two points on a beam...
Cluster Sampling Method
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

