Saverio Ieva

A Mobile Matchmaker for the Ubiquitous Semantic Web

The Semantic Web and Internet of Things visions are converging toward the so-called Semantic Web of Things SWoT. It aims to enable smart semantic-enabled applications and services in ubiquitous contexts. Due to architectural and performance issues, it is currently impractical to use existing Semantic Web reasoners. They are resource consuming and are basically optimized for standard inference tasks on large ontologies. On the contrary, SWoT use cases generally require quick decision support through semantic matchmaking in resource-constrained environments. This paper presents Mini-ME, a novel mobile inference engine designed from the ground up for the SWoT. It supports Semantic Web technologies and implements both standard subsumption, satisfiability, classification and non-standard abduction, contraction, covering inference services for moderately expressive knowledge bases. In addition to an architectural and functional description, usage scenarios are presented and an experimental performance evaluation is provided both on a PC testbed against other popular Semantic Web reasoners and on a smartphone.

Resource Annotation, Dissemination and Discovery in the Semantic Web of Things: A CoAP-Based Framework

The Semantic Web of Things (SWoT) vision aims to provide more advanced resource management and discovery w.r.t. standard Internet of Things architectures, by means of the integration of knowledge representation and reasoning techniques originally devised for the Semantic Web. This paper proposes a novel SWoT framework, based on a backward-compatible extension of the Constrained Application Protocol (CoAP), supporting non-standard inference services for semantic matchmaking. It allows retrieval and logic-based ranking of annotated resources. A computationally efficient data mining is also integrated in the framework to process raw data gathered from the environment in order to detect high-level events and characterize them with machine-understandable metadata. In order to test the effectiveness of the proposed approach, a case study about environmental risk prevention for Vehicular Ad-hoc Networks (VANETs) is presented.

Semantic Annotation of OpenStreetMap Points of Interest for Mobile Discovery and Navigation

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 shallow. Semantic-based technologies can allow to overcome these limitations, by exploiting more accurate and meaningful descriptions of locations, points of interest, road segments and environmental conditions. This paper proposes a general framework leveraging an enriched cartography to enhance travel assistance and safety. The majority of available navigation systems is developed upon closed and proprietary solutions for both maps and software applications, so third parties cannot extend their functionality. Therefore the proposed framework is based on open standards and tools, particularly Semantic Web technologies and crowd-sourced maps available from OpenStreetMap. A case study is presented to assess provided benefits.

Linked Data (in Low-Resource) Platforms: A Mapping for Constrained Application Protocol

This paper proposes a mapping of the Linked Data Platform (LDP) specification for Constrained Application Protocol (CoAP). Main motivation stems from the fact that LDP W3C Recommendation presents resource management primitives for HTTP only. A general translation of LDP-HTTP requests and responses is provided, as well as a framework for HTTP-to-CoAP proxying. Experiments have been carried out using the LDP W3C Test Suite.

Semantic-enhanced resource discovery for CoAP-based sensor networks

The integration of knowledge representation and reasoning techniques (originally devised for the Semantic Web) in most common Wireless Sensor Networks (WSNs) protocols can allow to reach higher levels of autonomicity w.r.t. classic network architectures that basically provide only simplistic discovery capabilities. This paper presents a complete Semantic Sensor Network (SSN) framework, supporting a resource discovery based on non-standard inferences. A backward-compatible extension of Constrained Application Protocol (CoAP) has been proposed to support semantic matchmaking for retrieving and ranking resources annotated w.r.t. a reference ontology. Data mining procedures were also exploited to detect high-level events from gathered raw data. A case study on environmental monitoring has been proposed to test the effectiveness of our approach.

Indoor/Outdoor Mobile Navigation via Knowledge-Based POI Discovery in Augmented Reality

This paper proposes a novel framework and system for a mobile navigation assistant fit for the purpose of moving within a complex built space. Notable features include: annotation of accessibility information in the crowd-sourced OpenStreetMap (OSM) cartography; semantic description of Location-Based Services (LBSs) and Points Of Interest (POIs); a mobile Augmented Reality (AR) user interface; a built-in routing engine with support for both outdoor and indoor positioning and navigation in multilevel buildings. A functional prototype was developed for Android mobile devices specifically devoted to assist users with physical disabilities. Its effectiveness in POI discovery and accessibility conscious navigation were tested in a case study in the campus of the Technical University of Bari.

Linking the Web of Things: LDP-CoAP Mapping

Abstract The Linked Data Platform (LDP) W3C Recommendation defined resource management primitives for HTTP only, pushing into the background not-negligible use cases related to Web of Things (WoT) scenarios where HTTP-based communication and infras- tructures are unfeasible. This paper proposes a mapping of the LDP specification for Constrained Application Protocol (CoAP) in order to publish Linked Data on the WoT. A general translation of LDP-HTTP requests and responses is provided, as well as a fully comprehensive framework for HTTP-to-CoAP proxying. The theoretical work is corroborated by an experimental campaign using the W3C Test Suite for LDP.

T 2 .O.M. T.O.M.: techniques and technologies for an ontology-based mobility tool with open maps

Nowadays, vehicle navigation systems have a limited effectiveness in satisfying user interests and needs. They basically locate the users GPS position on a map and calculate a path toward a given destination. This paper presents an innovative semantic-based navigation system, acting as intelligent agent offering an advanced assistance during a road trip. It integrates a mobile matchmaker, which allows a knowledge-oriented destination discovery, in order to fully accomplish requirements of a user during her travel. The framework we propose is based on open standards, and particularly it uses crowd-sourced maps available under the OpenStreetMap project, where Points Of Interest (POI) of the basic cartography are semantically featured in order to enable novel services supporting a traveling user.

Semantic Matchmaking for Kinect-Based Posture and Gesture Recognition

Innovative analysis methods applied to data extracted by off-the-shelf peripherals can provide useful results in activity recognition without requiring large computational resources. In this paper a framework is proposed for automated posture and gesture recognition, exploiting depth data provided by a commercial tracking device. The detection problem is handled as a semantic-based resource discovery. A general data model and the corresponding ontology provide the formal underpinning for posture and gesture annotation via standard Semantic Web languages. Hence, a logic-based matchmaking, exploiting non-standard inference services, allows to: (i) detect postures via on-the-fly comparison of the annotations with standard posture descriptions stored as instances of a proper Knowledge Base; (ii) compare subsequent postures in order to recognize gestures. The framework has been implemented in a prototypical tool and experimental tests have been carried out on a reference dataset. Preliminary results indicate the feasibility of the proposed approach.

Knowledge discovery and sharing in the IoT: the physical semantic web vision

The Physical Semantic Web is proposed as an enhancement to the Google Physical Web approach for the Internet of Things. It allows associating beacons to semantic annotations (instead of trivial identifiers), in order to enable more powerful expressiveness in human-things and things-things interactions. This paper presents a general frame-work for the Physical Semantic Web based on machine-understandable descriptions of physical resources and novel logic-guided resource discovery capabilities. A possible application scenario and early experiments are outlined to prove benefits of the induced enhancements and the effectiveness of theoretical solutions.