The rapid growth of Internet of Things (IoT) devices has heightened the need for robust cryptographic algorithms to safeguard sensitive data. Selecting the most appropriate algorithm, however, requires a systematic decision-making framework capable of addressing uncertainty and imprecision in expert evaluations. This study proposes a novel distance measure for $p,q$-quasirung orthopair fuzzy sets ( $p,q$-QOFSs), inspired by the Jensen–Shannon divergence, and examines its fundamental properties, including non-degeneracy, symmetry, boundedness, and compliance with the triangle inequality. Building on this measure, we develop an integrated multi-criteria group decision-making (MCGDM) model that combines the analytical hierarchy process (AHP) for criteria weighting with the compromise ranking of alternatives from distance to ideal solution (CRADIS) method for alternative ranking in a $p,q$-QOF environment. The proposed approach is applied to the selection of cryptographic algorithms for securing IoT devices, involving four domain experts, six candidate algorithms, and eight evaluation criteria. Results show that RSA (i.e., the alternative $A_2$) is identified as the most suitable algorithm. Comparative analysis against CRADIS with other distance measures and established decision-making methods confirms the enhanced ranking accuracy and robustness of the proposed framework. These findings offer practical guidance for cybersecurity practitioners and policymakers in selecting cryptographic solutions tailored to IoT security needs.