International Research Journal of Multidisciplinary Technovation

Hybrid BIRCH-ACO and PSO-MST Strategies for Energy-Aware Data Aggregation in Software-Defined WSNs

2025-10-25T07:28:11+00:00

In a Wireless Sensor Network (WSN), dozens or hundreds of battery-driven sensors communicate with one another. Batteries have to be replaced frequently when nodes are deployed in unattended environments. Internet of Things (IoT) applications are becoming increasingly scalable and energy-efficient, making energy-efficient data aggregation a critical research focus. As part of this study, two hybrid data aggregation frameworks are presented and evaluated in order to optimize energy consumption and network performance. In the first framework, hierarchical clustering is performed using BIRCH (Balanced Iterative Reduction and Clustering Using Hierarchies), while mobile base station shunting is performed using Ant Colony Optimization (ACO). Using Particle Swarm Optimization (PSO), optimal cluster heads and base stations can be placed, and routing paths can be optimized using the Minimum Spanning Tree (MST) algorithm. Software-defined WSNs reduce computational overhead and improve adaptability by utilizing a software-defined architecture. According to a comparison of energy efficiency, network lifetime, control overhead, and data latency metrics, both approaches outperform traditional static clustering methods significantly; however, the BIRCH and ACO model excels in adaptive clustering and load distribution, while the PSO and MST model provides the best path optimization and the least amount of delay in data transmission.

BN+SiC Hybrid Nanofluid in Enhanced Microchannels: Numerical Heat Transfer Augmentation Study

2025-06-24T09:46:22+00:00

This research article numerically investigated the performance of non-oxide hybrid Boron Nitride Silicon Carbide (BN+SiC/water) hybrid nanofluid in high heat flux miniaturized electronic hardware. 3d-CFD model validated with established experimental data on a triangular section, oblique microchannel geometry is used to explore the influence of total particle loading (0.5 to 1.5 %), relative particle proportion, on the Nusselt number (Nu), friction factor (f) and thermal performance factor (TPF). The results compared with the benchmark conventional oxide hybrid nanofluid system (Al₂O₃+CuO/water) and found superior on overall thermo-hydraulic performance. Heat transfer enhancement by 50% with respect to base fluid water, is quite an improvement in thermal enhancement, if we compare with the benchmark oxide system reference of around 37%. It is also observed that relative SiC proportion increases the performance of this system. Nanoparticle size and morphology effects on the thermo-hydraulic performance is also studied in this work. Smaller size particles are found beneficial in a quantitative analysis in the range of 10nm to 90nm average particle diameter. Non spherical high aspect ratio shapes nanoparticles enhance the performance of the nanofluid observed in this study. This study not only introduced a novel advanced heat transfer fluid but also allow the design customization insight for this BN+SiC/water hybrid nanofluid system.