Floriano Scioscia

Semantic-Based Resource Discovery and Orchestration in Home and Building Automation: A Multi-Agent Approach

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.

Semantic-Based Enhancement of ISO/IEC 14543-3 EIB/KNX Standard for Building Automation

Current technologies for Home and Building Automation (HBA) basically require an explicit interaction with the user and allow a static set of operational scenarios defined during system implementation. On the contrary, novel HBA solutions should enable so-called ambient intelligence, deriving from a flexible and automatic control of appliances and subsystems dipped into an environment. To this aim, this paper proposes backward-compatible enhancements to one of the most widespread domotic standards, i.e., EIB/KNX ISO/IEC 14543-3, able to support advanced, knowledge-based and context-aware functionalities, grounded on the semantic annotation of both user profiles and device capabilities. Such an approach enables novel resource discovery, matchmaking, and decision support features in HBA. Main benefits are in: (i) determining the most suitable services/functionalities according to implicit and explicit user needs and (ii) allowing device-driven interaction for autonomous adaptation of the environment to context modification. A case study is presented to better clarify the proposed framework also highlighting main characteristics, while performance evaluation is provided to assess its effectiveness.

Enabling the Semantic Web of Things: Framework and Architecture

The Semantic Web of Things is a novel paradigm combining the Semantic Web and the Internet of Things, aiming to associate semantic annotations to real-world objects, locations and events. This paper presents a general framework for the Semantic Web of Things, based on an evolution of classic Knowledge Base models, also providing architectural solutions for information storage, communication and processing.

Estimation of fetal weight by measurement of fetal thigh soft-tissue thickness in the late third trimester

The accuracy of current formulae for the sonographic estimation of fetal weight (EFW) is compromised by significant intra‐ and interobserver variability of biometrical measurements, particularly circumferences. The aim of this study was to assess the reliability of the linear measurement of mid‐thigh soft‐tissue thickness (STT) and to derive a novel formula for EFW.

Concept abduction and contraction in semantic-based P2P environments

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.

Semantic-Based Bluetooth-RFID Interaction for Advanced Resource Discovery in Pervasive Contexts

We propose a novel object discovery framework integrating the application layer of Bluetooth and RFID standards. The approach is motivated and illustrated in an innovative u-commerce setting. Given a request, it allows an advanced discovery process, exploiting semantically annotated descriptions of goods available in the u-marketplace. The RFID data exchange protocol and the Bluetooth service discovery protocol have been modified and enhanced to enable support for such semantic annotation of products. Modifications to the standards have been conceived to be backward compatible, thus allowing the smooth coexistence of the legacy discovery and/or identification features. Also noteworthy is the introduction of a dedicated compression tool to reduce storage/transmission problems due to the verbosity of XML-based semantic languages.

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.

If objects could talk: a novel resource discovery approach for pervasive environments

We propose to extend standard Radio Frequency IDentification (RFID) usage by storing semantically annotated data within RFID tags memory, so that objects may actually describe themselves in a variety of scenarios. In particular here we exploit our approach to carry out an advanced matchmaking process using metadata stored in RFIDs. A fully backward compatible modification to the original RFID data exchange protocol is presented, allowing to implement an advanced resource discovery framework. A compression algorithm specifically devised to ease embedding of annotated DIG description is also proposed. Motivations and benefits of the approach are outlined in a tracking of agricultural and food products case study.

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.