Simulation Models of Sensor Network Nodes Placement Based on Various Distribution Laws

The effectiveness of wireless sensor networks (WSNs) is significantly influenced by the spatial placement of sensor nodes. This paper presents a comparative study of simulation models based on different node distribution laws, including uniform, normal (Gaussian), and exponential distributions. Using these models, we investigate how node placement strategies affect network performance parameters such as coverage, connectivity, energy consumption, and data transmission efficiency. Simulation scenarios were implemented in a controlled environment, and statistical analysis was conducted to evaluate the behavior and effectiveness of each distribution law. The results show that each distribution exhibits unique advantages depending on the intended application. Uniform distribution provides consistent coverage, while normal distribution offers improved central density, enhancing data aggregation. Exponential distribution, on the other hand, favors scenarios with gradient-based node density requirements. The insights derived from this study are instrumental for network designers aiming to optimize WSN deployment in various real-world scenarios, such as smart agriculture, environmental monitoring, and industrial automation. Future work may include real-time testing with hardware implementations and hybrid distribution models. This study contributes to the ongoing development of intelligent and adaptive sensor network infrastructures by providing a foundational understanding of how placement strategies affect overall network performance.