Escaping the Streetlight Effect: Semantic Hypermedia Search Enhances Autonomous Behavior in the Web of Things

The integration of systems of autonomous agents in Web of Things (WoT) environments is a promising approach to provide and distribute intelligence in world-wide pervasive systems. A central problem then is to enable autonomous agents to discover heterogeneous resources in large-scale, dynamic WoT environments. This is true in particular if an environment relies on open-standards and evolves rapidly requiring agents to adapt their behavior to achieve their goals. To this end, we developed a search engine for the WoT that allows autonomous agents to perform approximate search queries in order to find relevant resources in their environments in (weak) real time. The search engine crawls dynamic WoT environments to discover and index device metadata described with the W3C WoT Thing Description, and exposes a SPARQL endpoint that agents can use for approximate search. To demonstrate the feasibility of our approach, we implemented a prototype application for the maintenance of industrial robots in world-wide manufacturing systems. The prototype demonstrates that our semantic hypermedia search engine enhances the flexibility and agility of autonomous agents in the WoT.