WH ² D ² N ² : Distributed AI-enabled OK-ASN Service for Web of Things

Abstract

Model data-driven ontology and knowledge presentation for evolving semantic Asian social networks (OK-ASN) is a critical strategy for web of things (WoT) services. Meanwhile, Deep Neural Network (DNN) based OK-ASN service in WoT is growing rapidly. However, most DNN-based services can not utilize the potential of WoT fully, as heterogeneity exists in WoT. Therefore, this paper proposes a novel framework called Web-Based Heterogeneous Hierarchical Distributed Deep Neural Network ( WH 2 D 2 N 2 ) to deploy the DNNs for OK-ASN services on WoT, overcoming the heterogeneity. The architecture of the system and the designed Edge-Cloud-Joint execute scheme utilize heterogeneous devices to make DNN inference ubiquitous and output two types of results to meet various requirements. To bring robustness to OK-ASN services, a global scheduling is designed to arrange the workflow dynamically. The results of our experiments prove the efficiency of the execute scheme and the global scheduling in the system.