A Survey-Driven Hybrid MCDM Framework for Prioritizing and Ranking Critical Internet of Things Applications - Nawani Sreeja (International Journal of Intelligent Systems and Applications in Engineering, 2025)
The rapid advancement of the Internet of Things (IoT), driven by high-speed connectivity, edge intelligence, and data-centric decision-making, has enabled its widespread deployment across domains such as transportation, healthcare, smart homes, governance, and environmental monitoring; however, the growing diversity and scale of IoT applications have made systematic prioritization increasingly challenging under competing technical, economic, and societal constraints. To address this challenge, this study proposes a survey-driven hybrid multi-criteria decision-making (MCDM) framework that integrates the Analytic Hierarchy Process (AHP) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to prioritize and rank critical IoT application domains. Ten representative IoT applications and seven evaluation criteria—functional performance, affordability, scalability, privacy and security, user experience, interoperability, and environmental sustainability—are identified through a comprehensive literature review and expert consultation. Judgments from fifteen IoT professionals are synthesized using Saaty’s scale to construct a consistent AHP model, revealing Quality of Life, Affordability, and Scalability as the most influential criteria, supported by strong consistency metrics (CI ≈ 0.030, CR ≈ 0.027). The validated weights are then incorporated into TOPSIS using a 10×5 decision matrix to compute closeness coefficients and establish a quantitative ranking of IoT applications. The robustness of the proposed framework is further confirmed through Monte Carlo–based sensitivity analysis with ±20% perturbations in criteria weights, yielding an average Spearman rank correlation of approximately 0.94. Overall, the findings demonstrate that the proposed approach is transparent, reproducible, and resilient to uncertainty, offering a reliable decision-support tool for IoT investment, system design, and policy formulation in emerging areas such as smart cities, Industry 5.0 ecosystems, healthcare digital twins, and technology governance.