A novel Hadamard matrix based hybrid compressive sensing technique for enhancing energy efficiency and network...

Wireless Sensor Networks (WSN)-based Internet of Things (IoT) are made up of many tiny sensor nodes that are assigned specific tasks to sense, data process, communicate and control in predetermined areas. These networks are used in many different domains, including military operations, security, disaster relief, and habitat monitoring. However, there are various challenges and design issues in WSN like node deployment, routing, energy consumption, computational power, bandwidth, clustering, fault tolerance, coverage, connectivity and QoS. Such issues complicating protocol design and reducing network efficiency. While various clustering protocols exist, a critical gap still remains in effectively balancing these limitations, to optimize energy consumption, memory efficiency, data accuracy, and network longevity. To address these challenges, integrating excellent data compression and reconstruction methods with high-quality clustering algorithms can be the ideal solution. The proposed approach, NHM-HCS (Novel Hadamard Matrix-based Hybrid Compressive Sensing) introduces a collaborative method incorporating data compression, efficient cluster head selection, and optimal routing. By adopting improved versions of Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and novel Hadamard matrix-based hybrid compressed sensing techniques, NHM-HCS enhances the network's lifespan and improves other performance metrics. Compared to the existing and traditional methods, the proposed NHM-HCS approach improves the network lifetime by 13%, percentage of alive nodes by 17%, residual energy by 16% and the throughput by 43%. It also reduces energy consumption by half and End-To-End delay by 39%. The simulation results also reveal that the proposed strategy can reduce energy costs while ensuring reliable performance. Furthermore, the strategy can be easily implemented with existing hardware, making it a viable option for WSNs.