Enhanced Grey Wolf Optimization for Efficient Transmission Power Optimization in Wireless Sensor Network

The Internet of Things (IoT) and Wireless Sensor Networks (WSNs) heavily rely on the lifetime of sensor nodes, which is inversely proportional to transmission power. Nodes with greater separation demand higher transmission power, while those closer together require less power. In practice, node placement varies significantly due to diverse terrain and contours, making power transmission configuration a critical and challenging issue in WSNs. This paper introduces an Enhanced Grey Wolf Optimization (EGWO) algorithm designed to optimize power transmission in WSN environments. Traditional Grey Wolf Optimization (GWO) employs a parameter that decreases linearly with iterations to regulate exploitation. In contrast, the proposed EGWO adopts a concave decline in the exploitation rate, allowing for more precise optimization in areas under exploration. The enhancement utilizes a cosine function that gradually decreases from 1 to 0, providing a smoother and more controlled transition. The experimental results demonstrate that EGWO outperforms other optimization algorithms. The proposed method achieves the lowest fitness value of -4.21, compared to 1.22 for standard GWO, -2.81 for PSO, and 2.86 for BESO, indicating its superiority in optimizing power transmission in WSNs.