A Systematic Mapping Study on Self-Adaptation SDN based Iot Networks

Abstract

Background: The Internet of Things (IoT) has led to widespread adoption of software-defined networking (SDN) to manage IoT networks. However, challenges like scalability, performance, and security necessitates self-adaptation capabilities in SDN-based IoT networks. Objective: This study aimed to systematically map research on self-adaptation approaches, issues, and metrics in SDN-based IoT networks. Method: Following systematic mapping guidelines, an extensive literature search was conducted across scientific databases. After screening, 32 relevant studies were selected for analysis. Data extraction and synthesis was performed to identify adaptation approaches, publication trends, and key issues. Results: Machine learning and deep learning are the prevailing methods for adaptation. Most research findings have been disseminated through academic journal articles, with the highest number of 11 studies published in 2020, followed by a gradual decrease. These studies primarily address significant challenges, including scalability, congestion, energy efficiency, service quality, and security. Conclusion: This mapping study offers a current overview of research in the field and identifies areas where further investigation is needed, which can serve as a roadmap for future research. Expanding the focus beyond SDN-IoT, exploring novel adaptation approaches, and creating reusable frameworks are suggested as promising research avenues. These insights provide a basis for advancing research on self-adaptive SDN for IoT.