Application of community detection algorithms on social internet-of-things networks

The Internet-of-things (IoT) networks are witnessing a drastic increase over the years. Twenty billion devices connected to the Internet are expected in 2022. The need for identifying communities within such networks can serve as a strong complexity reduction mean for many discovery and identification services. The idea of communities in IoT networks is also motivated by the emerging concept of socializing IoT devices. In this paper, we investigate the application of two community detection algorithms, namely Louvain and Bron-Kerbosch algorithms, on IoT networks usually represented by large-scale graphs. The objective is to convert the complex IoT network into multiple overlapping and non-overlapping communities where its elements share common characteristics. Starting from a real-world IoT networks, we use its dataset to extract community-structured IoT network based on different types of relationships such as co-location, owner social relationships, and autonomously build object relationships among objects. Our analysis showcases how community detection algorithms structure the IoT network into communities based on the different relationships established between objects.