A Novel Indexing Method for Scalable IoT Source Lookup

When dealing with a large number of devices, the existing indexing solutions for the discovery of Internet of Things (IoT) sources often fall short to provide an adequate scalability. This is due to the high computational complexity and communication overhead that is required to create and maintain the indices of the IoT sources particularly when their attributes are dynamic. This paper presents a novel approach for indexing distributed IoT sources and paves the way to design a data discovery service to search and gain access to their data. The proposed method creates concise references to IoT sources by using Gaussian mixture models. Furthermore, a summary update mechanism is introduced to tackle the change of sources availability and mitigate the overhead of updating the indices frequently. The proposed approach is benchmarked against a standard centralized indexing and discovery solution. The results show that the proposed solution reduces the communication overhead required for indexing by three orders of magnitude while depending on IoT network architecture it may slightly increase the discovery time.