Studies On The Key Techniques Of Context-Aware Smart City GeoSpatial Information Service

Geographical Information Systems (GISs) are greatly promoted by each significant improvement of IT. With the advent of pervasive computing, the era of Cibercity has evolved to smart city, the era of WebGIS has been developing to pervasive computing, and GISs are moving from GISystems to intelligent GIServices. Intelligent GIServices can be thought of as new capabilities provided in distributed service environments that help users perceive environmental surroundings, reason according to models, learn and understand from experience, and act on geospatial tasks. Compared to existing GIServices focusing on delivering any information to any people in any place at any time, shortly known as 4A services, intelligent GIServices improve 4A services to 4R services, which can provide right information to right people in the right place at the right time, shortly known as 4R services. These capabilities for GIServices are achieved by tools or middleware on domain knowledge captured as ontologies and rules, semantic reasoning over knowledge bases, automatic service discovery and workflow composition, and quality and traceability.Cibercity and Internet of Things (IoT) technologies contribute to smart city, which utilizes IoT composed of ubiquitous sensors, acquires all kinds of information from urban objects, and realizes the fusion of information by supercomputers and cloud computing. Intelligent management and service for cities can be achieved with infrastructure of smart city. One of the challenges of smart city is attributed to construction of intelligently sensitive and ubiquitous GIServices, which requires that services adapt to continual changing context. In this paper, they are named as context-aware GIServices, which are realization of intelligent GIServices.Representation, exploration and combination of context-aware GIServices are great challenges. The paper focuses on the key points on context-aware GIServices, and investigates the following issues:1.Construction of geographical context ontologies for smart city. Geographical context for smart city include any information describing city circumstances. Referring to context models in pervasive computing, urban computing, semantic web, the model put forward in the paper is classified as seven dimensions containing space, time, society, environment, technology, user and task, etc. Space dimension is the characteristic feature of geographical context, including location, spatial objects, etc. User dimension comprises identity information, professional information, social information, preference information. Environment dimension consists of weather, temperature, humidity, pollution, environmental security, etc. Technology dimension contains hardware, software, data, human-machine Interaction, IT security, etc. Society dimension mainly includes culture (language, convention and norms), social resource and social organization, etc.2. Representation of context-aware GIServices. OWL-S ontology extended fromgeographical context is used for representing context-aware GIServices (OWL-SGC). OWL-SGC ontology uses OWL-GCt to make it context-aware. Extension means making relations between Service objects in OWL-S and GeoContext objects in OWL-GCt. Context-aware GIServices should response to changes of geographical context. OWL-SGC extends atomic process in OWL-S, and makes its precondition and effect context-aware. In order to express precondition and effect of external geographical context, two elements GeoContextPrecondition and GeoContextEffect are added to OWL-SGC. If geographical context needs to be input and output, GeoContextBinding should be used for establishing corresponding relations between input/output and context value.3. Matching of context-aware GIServices. According to semantic representation, matching framework of context-aware GIServices is put forward. The framework is multi-level, and involving matching objects as input, output, service precondition, service effect, precondition of geographical context, and effect of geographical context.4. Combination of context-aware GIServices. Intelligent AI planning is used for service combination. OWL-S extended models are converted to PDDL (Planning Domain Definition Language). Then problems on PDDL and domain files are enhanced by techniques of semantic similarity. And then PDDL files are input to AI planner meeting PDDL specifications. At last, planning results are converted to workflow of WS-BPEL(Web Services Business Process Execution Language), and input to workflow engine to execute.