In this research work, a comprehensive statistical analysis is performed on the performance of the Encounter Count and Interaction Time-based (ECIT) routing protocol of Opportunistic Networks (OppNETs), using the Design of Experiments (DoE) methodology. The Full Factorial \((L_{16})\) , Plackett-Burman \((L_{12})\) , and Taguchi \((L_8)\) are utilized to assess the impact of the four control factors (i.e., time-to-live, node density, range, and message generation interval) on the four performance metrics (i.e., delivery probability, delay, overhead, and buffer time) using the MINITAB tool. The probability plots support findings that the distribution of performance metrics for overhead and delivery probability follows a uniform distribution. Analysis of Variance (ANOVA) identifies time-to-live and range as statistically significant factors with substantial contributions to performance variations. The regression models provide accurate predictions of performance metrics, significantly reducing the computational resources required for OppNETs simulations. These insights offer valuable guidance for optimizing routing protocols in OppNETs, ultimately enhancing their efficiency and reliability. The findings show that the range factor significantly impacts most performance metrics, such as delivery probability, delay, and buffer time, for all DoE methods. Notably, the regression analysis shows that delivery probability exhibits a better generalization model, with R-squared and adjusted R-squared values indicating a high model fit, particularly for the Full Factorial (94.18%, 92.07%), Plackett-Burman (95.76%, 93.34%), and Taguchi (95.5%, 89.5%) methods.
