Wi-Fi sensor networks (WSNs) are extensively deployed in various programs and have an increasing significance within the net of things (Iota). However, their connectivity remains a chief challenge, especially in large-scale networks, as it's far crucial for reliable communication and green statistics aggregation. To address this project, this paper presents a unique method to optimize the connectivity of WSNs through utilizing mathematical modeling techniques. Mainly, nonlinear optimization methods are hired to acquire a surest deployment sample for a given community configuration that maximizes the connectivity metric. The proposed method is proven to provide better connectivity outcomes than conventional solutions and is analyzed each theoretically and empirically with extensive simulations. Simulation outcomes additionally show that the method is robust to various node deployment techniques. Furthermore, it is evaluated using exceptional styles of environments and a huge variety of community configurations. The offered outcomes demonstrate the versatility and scalability of the proposed optimization method in optimizing the connectivity of WSNs.