Current mobile systems for assisted navigation have limited effectiveness in satisfying user needs. The information content supporting location-based service discovery and path calculation is usually limited. Semantic-based technologies can allow to overcome these limitations, by exploiting accurate and meaningful descriptions of locations, POIs, road segments and environmental conditions. We present here a general framework leveraging an enriched cartography, which may be useful not only to enhance travel satisfaction and safety, but also to regulate vehicle efficiency, traffic and environmental impact. The availability of semantically annotated map data is a crucial requirement for this kind of proposals to be practically viable. Unfortunately, the majority of available systems is developed upon closed and proprietary solutions for both maps and software applications, so third parties cannot extend their functionality. In order to overcome this restriction, the framework proposed here is based on open standards and tools: in particular, it leverages Semantic Web technologies and crowd-sourced maps available from OpenStreetMap (http://www.openstreetmap.org/), enriching nodes and POIs with semantic annotations to enable innovative Location Based Services (LBSs) for traveling users. Particularly, the paper proposes a general method for storing semantic annotations into OpenStreetMap road nodes and POIs, expressed w.r.t. ontologies in standard Semantic Web languages. A user-friendly software tool is also presented for editing semantic annotations through a fully visual user interface, based on simple drag-and-drop operations, implemented as a plugin for the popular open source JOSM Open-StreetMap editor (http://josm.openstreetmap.de/), that will make any OpenStreetMap contributor capable of enriching maps with semantic information. Finally, a semantic-enhanced navigation tool is proposed, capable of exploiting the enriched OpenStreetMap cartography. We present the framework, the tool, and an initial users’ evaluation.

Reasoning engines are largely used in resource discovery and matchmaking scenarios where, given a request, they are able to provide a list of compatible items arranged in relevance order. A significant added value is the possibility to explain match outcomes in order to obtain information for modifying or refining early queries. Though the feasibility of running logic-based reasoning tasks over various knowledge bases has been widely proven on fixed servers, it is a challenging subject to execute inference processes on handheld devices. The paper presents a revised lightweight version of abduction and contraction algorithms (going back to previous works) for matchmaking in Description Logics in mobile ad-hoc contexts. Implementation and tests have been carried out in a mobile P2P case study based on a simplified Bluetooth interaction paradigm.

Home and building automation (HBA) trends toward the Ambient Intelligence paradigm, which aims to autonomously coordinate and control appliances and subsystems in a given environment. Nevertheless, HBA is based on an explicit user–home interaction and basically enables static and predetermined scenarios. This paper proposes a more flexible multi-agent approach, leveraging semantic-based resource discovery and orchestration for HBA applications. Backward-compatible enhancements to EIB/KNX domotic standard allow to support the semantic characterization of user profiles and device functionalities, thus enabling: 1) negotiation of the most suitable home services/functionalities according to implicit and explicit user needs and 2) device-driven interaction for adapting the environment to context evolution. A power-management problem in HBA is presented as a case study to better clarify the proposal and assess its effectiveness.