Enhancing Graph Routing Algorithm of Industrial Wireless Sensor Networks Using the Covariance-Matrix Adaptation Evolution Strategy

The emergence of the Industrial Internet of Things (IIoT) has accelerated the adoption of Industrial Wireless Sensor Networks (IWSNs) for numerous applications. Effective communication in such applications requires reduced end-to-end transmission time, balanced energy consumption and increased communication reliability. Graph routing, the main routing method in IWSNs, has a significant impact on achieving effective communication in terms of satisfying these requirements. Graph routing algorithms involve applying the first-path available approach and using path redundancy to transmit data packets from a source sensor node to the gateway. However, this approach can affect end-to-end transmission time by creating conflicts among transmissions involving a common sensor node and promoting imbalanced energy consumption due to centralised management. The characteristics and requirements of these networks encounter further complications due to the need to find the best path on the basis of the requirements of IWSNs to overcome these challenges rather than using the available first-path. Such a requirement affects the network performance and prolongs the network lifetime. To address this problem, we adopt a Covariance-Matrix Adaptation Evolution Strategy (CMA-ES) to create and select the graph paths. Firstly, this article proposes three best single-objective graph routing paths according to the IWSN requirements that this research focused on. The sensor nodes select best paths based on three objective functions of CMA-ES: the best Path based on Distance (PODis), the best Path based on residual Energy (POEng) and the best Path based on End-to-End transmission time (POE2E). Secondly, to enhance energy consumption balance and achieve a balance among IWSN requirements, we adapt the CMA-ES to select the best path with multiple-objectives, otherwise known as the Best Path of Graph Routing with a CMA-ES (BPGR-ES). A simulation using MATALB with different configurations and parameters is applied to evaluate the enhanced graph routing algorithms. Furthermore, the performance of PODis, POEng, POE2E and BPGR-ES is compared with existing state-of-the-art graph routing algorithms. The simulation results reveal that the BPGR-ES algorithm achieved 87.53% more balanced energy consumption among sensor nodes in the network compared to other algorithms, and the delivery of data packets of BPGR-ES reached 99.86%, indicating more reliable communication.

Experimental Investigation and Comparative Analysis of Aluminium Hybrid Metal Matrix Composites Reinforced with Silicon Nitride, Eggshell and Magnesium

In today's scenario, composite materials play a vital role in automobile, aerospace, and defence sectors because of their higher strength, light weight and other mechanical properties. Aluminium alloy Al6082 is a medium strength alloy with good corrosion resistance properties; hence, it is used for high-stress applications, bridges, cranes, etc. The present work focuses on comparing the mechanical properties of Al6082 and Al6082 with the addition of silicon nitride, magnesium, and bio waste of eggshells. Samples of Al6082 reinforced with 2% of silicon nitride (Si₃N₄), 5% of eggshell, and 1% magnesium reinforcements were prepared using the crucible casting process. Mechanical properties were evaluated through hardness test, tensile test and compressive tests, which varied with the additives of reinforcement materials. The results showed that the reinforced materials could increase mechanical properties. Further, it will be analysed by the machining parameters involved through the CNC turning process. Analysis of variance from optimisation technique shows a noteworthy increment of material removal rate (MRR) and significant decrement in surface roughness (Ra) and machining time compared to standard aluminium. Additionally, the analysis of mechanical testing has been predicted with the outcomes of stresses and displacements using the ANSYS platform.