A Scalable IoT Service Search Based on Clustering and Aggregation

With the Internet of Things (IoT), we are facing a proliferation of connected devices distributed over a collection of geographical locations and offering new services to users referred to as IoT services. These services are highly heterogeneous and their number is growing fast. To facilitate IoT service search, some solutions rely on the use of Semantic Web technologies to create homogenous service descriptions to reason on and support accurate and flexible search. However, most existing approaches are centralized and fail to scale with respect to the number of advertised services. To address this scalability issue, we present in this paper a distributed, semantic-based IoT service search system. It relies on a hierarchical network of semantic gateways that represent geographical locations and host semantic service descriptions. To answer a service query, our approach is based on request matching and forwarding though the use of routing tables and similarity thresholds. Based on defined metrics, recursive clustering and information aggregation are performed over the hierarchy of gateways to construct the routing tables. We prove theoretically that our method ensures an accurate search of all matching services within the system. Experimental results show that our method enables to reduce greatly search cost with comparison to a centralized approach and has an acceptable search path length.