Redefining IoT networks for improving energy and memory efficiency through compressive sensing paradigm

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.