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Conference Aug 2026

Energy Efficient Routing In Manet For Improved Communication Using Shrike Optimization Algorithm

Mobile Ad-hoc Networks (MANETs) are a useful means for communication in military and emergency situations, as well as for various types of smart sensors and other mobile applications, since they allow mobile nodes to interact with each other without relying on a fixed communication structure. However, fast-moving and unpredictable nodes result in frequent changes to the topology of the network which also result in inconsistent route selections and more energy used to send and receive packets, thus leading to lower overall performance on the network. This paper introduces an adaptive routing scheme based on the Shrike Optimization Algorithm (ShOA) for improving MANET performance by overcoming these issues. The proposed Scheme identifies the most efficient routing paths through the use of the predation and decision-making abilities of Shrike Birds. This Shrike-based routing design reduces the amount of information lost in the form of packet losses and routing overheads, while providing reliable transfers of data by properly balancing the trade-off between exploring and exploiting. Based on extensive simulation results, the proposed ShOA-based MANET exhibits substantially improved performance in the areas of packet delivery ratios, end-to-end delay, throughput, and overall durabilitys compared to both optimization techniques currently used and conventional routing protocols. Therefore,the results support the conclusion that the Shrike Optimization Algorithm offers aviable option for developing reliable and energy-efficient mobile ad - hoc networks for communication

V. Vishu, S. Sivagnanam, L. P. Suresh et al. · 0 citations
Conference Aug 2026

An Improved Power Factor Correction Converter With AI-Powered Predictive Maintenance for PMSM Drives

In this work, an advanced PFC converter was designed and AI-based predictive maintenance (PM) module was suggested to be included in complex PMSM drives.In this paper, an advanced PFC converter was designed and the AI based predictive maintenance (PM) module was proposed to be added in a complex PMSM drives system. The power quality of the PFC stage is good and reduces the generation of harmonic distortion caused by PWM based soft switching and effectively controls the power factor close to unity by using the capacitor bank controls, and noise suppression. The converter, motor drive, communication and data processing is controlled by a microcontroller. The use of a multi-sensor approach, which uses a three-axis accelerometer for vibration monitoring, a current sensor for electrical fault detection and a temperature sensor for thermal condition assessment, is used in the predictive maintenance system. These are then inputted into machine-learning algorithms, that detect unabnormal vibration, temperature and current fluctuations, which happen at the occurrence of faults such as bearing wear, rotor misalignment, inverter switching problems, thermal stress and so on. The integrated solution provides greater efficiency and reliability of the PMSM drives, increased equipment life and maintenance effectiveness, reduced unexpected downtime and operating costs.

Feba Shaji, D. Devanand, Abel Mathews et al. · 0 citations