MCDM: an ML-based Multi-Criteria Decision Making solution for UAVs Service Selection

The rapid proliferation of Unmanned Aerial Vehicles (UAVs) has opened up new horizons in various application domains. However, the optimal selection of UAV services remains a complex challenge, necessitating a nuanced approach considering multiple criteria. This article proposes a novel framework that integrates machine learning (ML) techniques with multi-criteria decision-making (MCDM) methods to address this challenge. Our framework comprehensively evaluates UAV services based on key metrics such as delay, packet loss, throughput, and residual energy, thereby providing a holistic perspective for decision-making. We present experimental results from a comparative study involving Random Forest (RF), Decision Tree (DT), Naive Bayes (NB), and Support Vector Machine (SVM) classifiers. Our findings reveal the effectiveness of our proposed approach, which achieved a classification accuracy of 99.9% with RF. These results underscore the capability of our framework to optimize UAV service delivery, thus enhancing overall performance and user satisfaction. By coupling ML and MCDM, our framework offers a robust and efficient solution for UAV service selection, catering to the diverse needs of users and service providers in the evolving landscape of UAV applications.