Version 1
: Received: 30 June 2023 / Approved: 3 July 2023 / Online: 3 July 2023 (10:52:07 CEST)
How to cite:
Qamar, M.S.; Haq, I.U. Wireless Sensors Networks Energy Optimization using LEACH and ANNs. Preprints2023, 2023070053. https://doi.org/10.20944/preprints202307.0053.v1
Qamar, M.S.; Haq, I.U. Wireless Sensors Networks Energy Optimization using LEACH and ANNs. Preprints 2023, 2023070053. https://doi.org/10.20944/preprints202307.0053.v1
Qamar, M.S.; Haq, I.U. Wireless Sensors Networks Energy Optimization using LEACH and ANNs. Preprints2023, 2023070053. https://doi.org/10.20944/preprints202307.0053.v1
APA Style
Qamar, M.S., & Haq, I.U. (2023). Wireless Sensors Networks Energy Optimization using LEACH and ANNs. Preprints. https://doi.org/10.20944/preprints202307.0053.v1
Chicago/Turabian Style
Qamar, M.S. and Ihsan ul Haq. 2023 "Wireless Sensors Networks Energy Optimization using LEACH and ANNs" Preprints. https://doi.org/10.20944/preprints202307.0053.v1
Abstract
Applications for Wireless Sensor Networks (WSNs) range from monitoring the environment to automating factories. However, sustained and effective functioning is made more difficult by Sensor Nodes (SNs) limited energy supplies in which optimization is the main issue. So with the aim of increasing the lifespan by decreasing the energy consumption of WSN, Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol with Deep Learning (DL) algorithm is analyzed in this paper. LEACH is a hierarchical mechanism that elects Cluster Heads (CHs) and regularly rotates their positions in order to allocate energy use effectively by using the same amount of energy. However, Deep Learning (DL) method is used to further improve energy optimization. In many applications, the types of Deep Learning methods like Artificial Neural Networks (ANNs) have shown to be very useful. Using this method, WSNs may make more efficient decisions that reduce energy consumption. Data aggregation, duty cycling, and transmission protocols may all be optimized by Deep Learning model's ability to recognize patterns and forecast network behavior. This results in lower energy consumption, a longer lifespan for the network, and better overall performance.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
