Saehoon Kang

Activity policy-based service discovery for pervasive computing

Service discovery is an essential technique to provide applications with appropriate resources. However, user mobility and heterogeneous environments make the discovery of appropriate resources difficult. The execution environments will be rapidly changed, so developers cannot predict available resources exactly in design time. Thus, service discovery frameworks for pervasive computing must guarantee transparent development environments to application developers. In this paper, we introduce how to semantically describe and discover a variety of services in different environments. This approach helps applications to find appropriate services even though the required ones are not available or not found. For this, we propose a fine-grained effect ontology which is used to reasonably evaluate functional similarity among different services, and a policy-based query coordination which is used to dynamically apply different resource constraints according to human activities. Finally, we show with a feasible scenario how to find appropriate services in different environments. Our approach helps applications to seamlessly perform their tasks across a dynamic range of environments.

A Landmark-Based Scalable Semantic Resource Discovery Scheme

In this paper, we propose an efficient resource discovery scheme for large-scale ubiquitous computing environments, which supports scalable semantic searches and load balancing among resource discovery resolvers. Here, the resources are described based on the concepts defined in the ontological hierarchy. To semantically search the resources in a scalable manner, we propose a semantic vector space and semantic resource discovery network in which the resources are organized based on their respective semantic distances. Most importantly, landmarks are introduced for the first time to reduce the dimensionality of the vector space. Computer simulation with CAN verifies the effectiveness of the proposed scheme.

A Semantic Fragrance Trail based Scalable Service Discovery in MANET

The scale of MANET is expanding with the proliferation of mobile devices, and the number of services in MANET also increases. It raises the importance of scalability and semantic searching capability. However, existing service discovery protocols targeting MANET have been focused on either only scalability or only semantic matching. Some recent researches are concerning the scalable semantic service discovery, but they take into account only one semantic, which is not sufficient to find relevant services in case there are many different service instances in the same service type. In this paper, we propose a new scalable semantic service discovery utilizing rich semantic information based on a scheme, so called semantic fragrance trail (SFT). For advertising services, Service BBF (SBBF) is propagated in a scalable way. For discovery, query messages are routed following the most relevant semantic fragrance (SF) by comparing SSFs from neighbor nodes to the queries using our proposed equation comparing SF similarity. We prove that the proposed equation can compare SSF and semantic fragrance of a query (QSF) to find the most relevant service through evaluation.

HAIAN: Hash-Based Autoconfiguration by Integrating Addressing and Naming Resolution for Mobile Ad Hoc Networks

Addressing is a basic step for communications among network nodes, and a naming service can help users easily and conveniently use their applications. To the best of our knowledge, addressing is studied regardless of naming service and vice versa. In this paper, we suggest an autoconfiguration scheme that allocates an address using a hash value of node’s name. As we bound an address and the name at the joining phase, users use the naming service on their application without an additional name resolution step. Moreover, the total number of messages can be reduced by integrating addressing and naming resolution.