Context-Aware Smart City Geospatial Web Service Composition

One of the challenges to build a smart city is to create intelligent and ubiquitous geospatial Web services, and provide a composition of these services available to users. These geospatial Web services must be better-tuned to a given context. Using AI planning techniques and semantic enhancement, this paper presents a dynamic, context-aware service composition method which is achieved by transforming the service composition problem into a planning problem described in a standardized fashion using PDDL. Semantic representation of a geospatial Web service is modeled by extending OWL-S ontology with GeoContext class, GeoContextPrecondition class, GeoContextEffect class and GeoContextBinding class, which support geo-context and geo-context adaptation. Semantic information is used for the enhancement of the composition process as well as for approximating the optimal composite service when exact solutions are not found. Independence from specific planners is maintained. The generating plan is transformed to a WS-BPEL compatible representation, which is executable on the business process execution engine. A case study about smart travel is also presented to demonstrate the functionality, effectiveness and potential of the approach.