SkABNet: A Data Structure for Efficient Discovery of Streaming Data for IoT

Abstract

Applications in the Internet of Things often make use of large networks of independent sensor nodes that generate streams of volatile data. A major challenge in these decentralized networks is to efficiently discover relevant data providers, which might be characterized by properties such as their data type, location, or ownership. Most existing approaches use distributed data structures, such as distributed hash tables, for the organization of sensor nodes. However, these systems lack the ability to consider contextual properties when identifying relevant data sources. SkipNet a prominent architecture for data storage and retrieval, provides a scalable overlay network composed of doubly-linked rings. While the data structure allows to locate individual nodes in logarithmic complexity, it fails to identify groups of nodes that share similar characteristics. Thus, in this paper, we propose SkABNet, an attribute-based extension for SkipNet which enhances the semantics of the node identifiers in the network. We introduce additional operators that allow SkABNet to accept complex search queries including multi-attribute selections, ranges, and wildcards to find relevant data providers in its decentralized data structure. Further, we define a search algorithm that performs searches with significantly less messages than comparable searches in SkipNet.