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Wireless sensor networks (WSNs) have become a crucial technology because they are used in several contexts, such as public safety, environmental monitoring, healthcare, military operations, and surveillance systems. The applications of WSNs have increased because they can be deployed in areas where cable and power supply are difficult to use. However, a variety of security assaults can easily penetrate these networks. To solve this problem, a Firebug Optimized Modified Bee Colony algorithm (FOMBC) has been proposed, which achieves the maximum network security goal with lower energy consumption in WSN. The proposed method is carried out into two phases namely Secure node selection and Orthogonal Routing (OR). In the first phase, secure node among the WSN's initial nodes is selected based on characteristics such as trust, delay, and Quality of Services. In the second phase, Firebug Optimized Modified Bee Colony (FOMBC) Optimization technique is used to find optimal route based on factors like trust, distance, latency, and quality of service. The effectiveness of the proposed FOMBC approach is evaluated against existing techniques like Trust-Aware Routing Framework (TARF), SOAR (Simple Opportunistic Adaptive Routing Protocol), Particle Swarm Optimization, and Whale Optimization Algorithm for Secure Routing (PSWOSR). According to the experimental findings, the proposed FOMBC technique achieves high detection, maximum throughput, least latency, and minimum range. The proposed method achieves network lifetime of 4.7%, 5.8%, and 9.87% than the existing TARF, SOAR, and PSWOSR, respectively. [ABSTRACT FROM AUTHOR]

This manuscript proposes a hybrid method for drive control using a multilevel inverter (MLI) for electric vehicle (EV) applications. The proposed technique is the joint execution of spiking neural network (SNN), and sand cat swarm optimization (SCSO), hence it is named as SCSO-SNN technique. The primary goal of the proposed technique is to improve the speed tracking performance and, energy efficiency and lessen the torque ripple of the EV. The set of control signals is generated by SCSO and the SNN is used to forecast the optimal control signal of the cascaded multilevel inverter (CMLI). The proposed technique is implemented in the MATLAB software and is evaluated their performance with various present methods. The results show that the proposed method can achieve better performance than current techniques based on speed tracking, energy efficiency, and torque ripple. The proposed method enhanced the total efficiency of the system (96%) and reduced the torque ripple (0.5%) when contrasted with current techniques like Genetic Algorithm (GA), Spotted Hyena Optimizer (SHO), Particle Swarm Optimization (PSO). The proposed method shows a mean of 1.269, a median of 1.212, and a standard deviation of 0.015, indicating high stability and consistency. It also demonstrates lower switching losses (0.159 W) and conduction losses (0.370 W) compared to existing methods, underscoring its superior energy efficiency and effectiveness. [ABSTRACT FROM AUTHOR]