**7. Conclusion**

By employing the Monte Carlo importance sampling technique, decision fusion algorithms have been provided for large-scale sensor networks with dependent observations and channel errors. The Bayesian cost function is approximated by the Monte Carlo cost function. The necessary conditions for the optimal sensor rules and the optimal fusion rule that minimize the Monte Carlo cost function have been obtained. Computationally efficient Monte Carlo Gauss-Seidel iterative algorithms have been proposed to search for the optimal sensor rules and the optimal fusion rule. These algorithms have been shown to converge after a finite number of iterations. The computational complexity of the new algorithm (i.e., *O LN* ð Þ) is much less than that of the previous algorithm based on Riemann sum approximation (i.e., *O LN<sup>L</sup>* ). For the *K*-out-of-*L* rule, an analytical solution has been presented for the optimal sensor rules. Simulations have demonstrated the effectiveness of Algorithms 1 and 2. Future work will include the decision fusion algorithms under the Monte Carlo framework for other networks such as tandem networks, tree networks, and sensor networks under Byzantine attack.
