Redefining IoT networks for improving energy and memory efficiency through compressive sensing paradigm
Abstract Wireless Sensor Networks (WSNs) have a wide range of applications across multiple platforms within the Internet of Things (IoT), yet face serious challenges like constrained resources, energy, and memory limitations. Current techniques often struggle to efficiently manage energy consumption...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-12278-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849344573489283072 |
|---|---|
| author | S. Balamurali M. Kathirvelu SatheeshKumar Palanisamy Ines Hilali Jaghdam |
| author_facet | S. Balamurali M. Kathirvelu SatheeshKumar Palanisamy Ines Hilali Jaghdam |
| author_sort | S. Balamurali |
| collection | DOAJ |
| description | Abstract Wireless Sensor Networks (WSNs) have a wide range of applications across multiple platforms within the Internet of Things (IoT), yet face serious challenges like constrained resources, energy, and memory limitations. Current techniques often struggle to efficiently manage energy consumption, resulting in faster battery drain and reduced network lifetimes. Additionally, memory limitations in sensor nodes can affect data storage, further reducing the productivity and scalability of WSNs. To overcome these issues, this research work presents NSPL-HCS (Novel Smoothed Projected Landweber based Hybrid Compressive Sensing), a novel framework that integrates enhanced Particle Swarm Optimization and Grey Wolf Optimization with the compressive sensing technique. NSPL-HCS enhances essential WSN operations, including the creation of cluster, cluster head selection, data compression, data transmission, and data reconstruction. Compared with the available standard and optimized techniques in this context, NSPL-HCS achieves improvements in throughput, residual energy, alive nodes, first and half dead nodes and, largely in network lifetime. Simulation results validated with relevant test functions revealed the potential of NSPL-HCS, showing its competency to improve WSN performance simultaneously retaining reliability and feasibility, as a result, setting the path for wider implementation of WSN in a number of applications of IoT. |
| format | Article |
| id | doaj-art-574375d648b64afa9be754b6e3ca00a8 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-574375d648b64afa9be754b6e3ca00a82025-08-20T03:42:38ZengNature PortfolioScientific Reports2045-23222025-07-0115111810.1038/s41598-025-12278-yRedefining IoT networks for improving energy and memory efficiency through compressive sensing paradigmS. Balamurali0M. Kathirvelu1SatheeshKumar Palanisamy2Ines Hilali Jaghdam3Department of Electronics and Communication Engineering, KPR Institute of Engineering and TechnologyDepartment of Electronics and Communication Engineering, KPR Institute of Engineering and TechnologySchool of Electronics Engineering, Vellore Institute of TechnologyDepartment of Computer Sci & IT, College of Applied, Princess Nourah bint Abdulrahman UniversityAbstract Wireless Sensor Networks (WSNs) have a wide range of applications across multiple platforms within the Internet of Things (IoT), yet face serious challenges like constrained resources, energy, and memory limitations. Current techniques often struggle to efficiently manage energy consumption, resulting in faster battery drain and reduced network lifetimes. Additionally, memory limitations in sensor nodes can affect data storage, further reducing the productivity and scalability of WSNs. To overcome these issues, this research work presents NSPL-HCS (Novel Smoothed Projected Landweber based Hybrid Compressive Sensing), a novel framework that integrates enhanced Particle Swarm Optimization and Grey Wolf Optimization with the compressive sensing technique. NSPL-HCS enhances essential WSN operations, including the creation of cluster, cluster head selection, data compression, data transmission, and data reconstruction. Compared with the available standard and optimized techniques in this context, NSPL-HCS achieves improvements in throughput, residual energy, alive nodes, first and half dead nodes and, largely in network lifetime. Simulation results validated with relevant test functions revealed the potential of NSPL-HCS, showing its competency to improve WSN performance simultaneously retaining reliability and feasibility, as a result, setting the path for wider implementation of WSN in a number of applications of IoT.https://doi.org/10.1038/s41598-025-12278-yWireless sensor networksInternet of thingsCompressive sensingOptimization algorithmsEnergy efficiencyMemory constraints |
| spellingShingle | S. Balamurali M. Kathirvelu SatheeshKumar Palanisamy Ines Hilali Jaghdam Redefining IoT networks for improving energy and memory efficiency through compressive sensing paradigm Scientific Reports Wireless sensor networks Internet of things Compressive sensing Optimization algorithms Energy efficiency Memory constraints |
| title | Redefining IoT networks for improving energy and memory efficiency through compressive sensing paradigm |
| title_full | Redefining IoT networks for improving energy and memory efficiency through compressive sensing paradigm |
| title_fullStr | Redefining IoT networks for improving energy and memory efficiency through compressive sensing paradigm |
| title_full_unstemmed | Redefining IoT networks for improving energy and memory efficiency through compressive sensing paradigm |
| title_short | Redefining IoT networks for improving energy and memory efficiency through compressive sensing paradigm |
| title_sort | redefining iot networks for improving energy and memory efficiency through compressive sensing paradigm |
| topic | Wireless sensor networks Internet of things Compressive sensing Optimization algorithms Energy efficiency Memory constraints |
| url | https://doi.org/10.1038/s41598-025-12278-y |
| work_keys_str_mv | AT sbalamurali redefiningiotnetworksforimprovingenergyandmemoryefficiencythroughcompressivesensingparadigm AT mkathirvelu redefiningiotnetworksforimprovingenergyandmemoryefficiencythroughcompressivesensingparadigm AT satheeshkumarpalanisamy redefiningiotnetworksforimprovingenergyandmemoryefficiencythroughcompressivesensingparadigm AT ineshilalijaghdam redefiningiotnetworksforimprovingenergyandmemoryefficiencythroughcompressivesensingparadigm |