<p>Distributed estimation offers a promising solution for in-network signal processing challenges in Wireless Sensor Networks (WSNs). This paper introduces a novel sparse diffusion recursive least squares (RLS) algorithm, specifically designed to enhance distributed estimation in WSNs. The algorithm exploits the inherent sparsity of certain WSN systems through Kronecker product decomposition, enabling a more efficient reformulation of the distributed network model. By decomposing the parameter vector into a summation of Kronecker products of shorter vectors, the model significantly reduces the number of unknown parameters, leading to improved computational efficiency, enhanced estimation accuracy, and accelerated convergence. Simulation results demonstrate the superior performance of the proposed algorithm compared to traditional diffusion RLS approaches in WSN environments, highlighting its potential for energy-efficient and reliable data processing.</p>