Supporting Self-Organization with Logical-Clustering Towards Autonomic Management of Internet-of-Things

Abstract

Abstract-One of the challenges for autonomic management in Future Internet is to bring about self-organization in a rapidly changing environment and enable participating nodes to be aware and respond to changes. The massive number of participating nodes in Internet-of-Things calls for a new approach in regard of autonomic management with dynamic selforganization and enabling awareness to context information changes in the nodes themselves. To this end, we present new algorithms to enable self-organization with logical-clustering, the goal of which is to ensure that logical-clustering evolves correctly in the dynamic environment. The focus of these algorithms is to structure logical-clustering topology in an organized way with minimal intervention from outside sources. The correctness of the proposed algorithm is demonstrated on a scalable IoT platform, MediaSense. Our algorithms sanction 10 nodes to organize themselves per second and afford high accuracy of nodes discovery. Finally, we outline future research challenges towards autonomic management of IoT.