Prioritizing Privacy-Aware Intelligent Monitoring Systems for Remote Communities Using Fuzzy Decision-Making

Deploying smart surveillance systems in remote communities is not just a technical decision, but a multidimensional issue that is tied to sensitive issues such as privacy, digital infrastructure constraints, legal requirements and, most importantly, gaining the trust of the local community. In such circumstances, choosing the right architecture for these systems becomes strategically important, as any mismatch between the technology and the social context can challenge the system's efficiency and public acceptance. In this study, the fuzzy analytic hierarchy process (FAHP) method is used to address these complexities; an approach that allows for the consideration of uncertainty and expert judgments through fuzzy pairwise comparisons. In this framework, five different architectural options are evaluated based on six main criteria including privacy risk, model accuracy, computational burden, regulatory compliance, community trust and adaptability. The proposed framework is examined through an application focused on Calabria, southern Italy, where geographically dispersed communities and weak digital connectivity make the adoption of privacy-preserving monitoring architectures especially relevant. The results show that Edge-Based Local Processing (A2) and Differential Privacy-Enhanced Platforms (A5) were the best options with the highest ranking, respectively. Accordingly, it is suggested that remote communities should look for architectures that, while complying with legal requirements, also enhance operational efficiency and social trust.