Domenico Furno

Hybrid approach for context-aware service discovery in healthcare domain

Context-awareness computing is a research field which often refers to healthcare as an interesting and rich area of application. Context aware computing attains environments monitoring by means of sensors to provide relevant information or services according to the identified context. In particular, wireless ad hoc sensor networks for medical purposes are playing an increasing role within healthcare. Body Sensor Networks (BSN) are being designed for prophylactic and follow-up monitoring of patients in e.g. their homes, during hospitalization, and in emergencies. This work presents an integrated environment aimed at providing personalized healthcare services which appropriately meet the user@?s context. Deploying the semantics embedded in web services and context models is a mandatory step in the automation of service discovery, invocation and composition. Nevertheless, in a context aware domain purely logic-based reasoning on respectively context and services may not be enough. The main idea of this work is related to enrich with qualitative representation of context underling data by means of Fuzzy Logic in order to automatically recognize the context and to consequently find the right set of healthcare services among the available ones. Semantic formalisms (e.g., OWL, OWL-S, etc.) enable the context and services modeling in terms of domain ontology concepts. On the other hand, soft computing techniques support activity of unsupervised context analysis and healthcare semantic service discovery. Goal is to define context-aware system whose quality of retrieved services relies on the acquisition of user context by means of a robust theoretical approach. Moreover, this work defines hybrid architecture which attains a synergy between the agent-based paradigm and the fuzzy modeling. Specifically, the system exploits some task oriented agents in order to achieve context recognition, services matchmaking and brokerage activities.

Agent-based Cognitive approach to Airport Security Situation Awareness

Situation awareness is crucial factor in decision-making. It involves monitoring and identification of relationships among objects in collaborative dynamic environments. In the domain of Airport Security one of the main needs is to support the security operator to manage in real-time risk scenarios in the airside. This work relies on a cognitive approach to model the awareness ontology and introduces an agent-based architecture to address the problem. In particular, in order to model situation awareness the work instantiates the generic Situation Theory Ontology(STO) in the specific domain of airport security. Furthermore, some task-oriented agents allow to distribute the information in order to achieve better performances.

A hybrid context aware system for tourist guidance based on collaborative filtering

In the area of ambient intelligence there is a need to address user needs according with context features. Recently, the synergy between context aware computing and collaborative filtering is leading to enhance recommender systems with capabilities always nearer to user needs. Specifically, in the domain of tourism it is useful to proactively suggest right sets of attractive locations, events and so on. This work defines a context aware recommender system aimed at suggesting pertinent points of interest (POIs) to tourists. In particular, the approach is strongly based on the synergy between soft computing and data mining techniques. The general framework integrates user profiles, history of social networking and POIs data. Then by defining collaborative filtering approach on the history meaningful POIs are extracted. Indeed, soft computing techniques are mainly applied in order to support activity of unsupervised users and POIs classification. On the other hand, data mining techniques are exploited in order to extract rules able to associate user profile and context features with an eligible set of recommendable POIs. Experimental results show performance in terms of recommendations accuracy.

OWL-FC: an upper ontology for semantic modeling of Fuzzy Control

This work introduces an OWL-based upper ontology, called OWL-FC (Ontology Web Language for Fuzzy Control), capable to support a semantic definition of Fuzzy Control. It focuses on the fuzzy rules representation by providing domain independent ontology, supporting interoperability and favoring domain ontologies re-usability. The main contribution is that OWL-FC exploits Fuzzy Logic in OWL to model vagueness and uncertainty of the real world. Moreover, OWL-FC enables automatic discovery and execution of fuzzy controllers, by means of context aware parameter setting: appropriate controllers can be activated, depending on the parameters proactively identified in the work environment. In fact, the semantic modeling of concepts allows the characterization of constraints and restrictions for the identification of the right matches between concepts and individuals. OWL-FC ontology provides a wide, semantic-based interoperability among different domain ontologies, through the specification of fuzzy concepts, independently by the application domain. Then, OWL-FC is coherent to the Semantic Web infrastructure and avoids inconsistencies in the ontology.

Decentralised smart grids monitoring by swarm-based semantic sensor data analysis

The large-scale deployment of the smart grids paradigm is expected to support the evolution of traditional electrical power systems toward active, flexible and self-healing web energy networks composed by distributed and cooperative energy resources. In this field, the application of hierarchical monitoring paradigms has many disadvantages that could hinder their application in modern smart grids where the constant growth of grid complexity and the need for supporting rapid decisions in a data rich, but information limited environment, require more scalable, more flexible monitoring paradigms. In trying and addressing these challenges, in this paper, a distributed and cooperative monitoring architecture aimed at exploiting the semantic representation of power system measurements for automatically detecting anomalies and incoherencies in power sensors data is proposed. Numerical results, obtained on the 57 bus IEEE test network, demonstrate the effectiveness of the proposed framework.

Swarm-based semantic fuzzy reasoning for situation awareness computing

Situation awareness computing employs sensor networks to collect large amounts of heterogeneous data in different and complex environments. The rapid development and deployment of sensor technology stress the problem related to the availability of too much and heterogeneous data. Last trend emphasizes the semantic annotation of acquired sensor data. Semantic sensor data provides machine understandable contextual information. In particular, the availability of semantic sensor data allows situation awareness in several application domains. This paper introduces a swarm-based approach to semantic web reasoning in order to identify situations. On one hand, fuzzy control has been employed in order to face with uncertainty of happening situations. On the other hand, Situation Theory has been used in order to model situation awareness. A multi agent swarm architecture enables to monitor complex environments by using spatially distributed autonomous sensors. An application scenario for bank intrusion detection has been described.

Towards an agent-based architecture for managing uncertainty in situation awareness

In computing, Ambient Intelligence (AmI) refers to electronic environments that are sensitive and responsive to the presence of people. The ambient intelligence paradigm is characterized by systems and technologies founded on a situational computing and, more generally, situation awareness substratum dealing with situational context representation and reasoning. At the same time, the global information infrastructure is becoming more and more pervasive and human computer interactions are performed in diverse situations, using a variety of mobile devices and across multiple communication channels. Nevertheless, recent advances in multi-sensors systems, multimodal access has yet to develop its full potential, due to imperfect observations, time-dependence of multimedia predicates, and to difficulties in conjoining facts coming from different modal streams. Hence, the knowledge upon which the context/situation aware paradigm is built is rather vague. To deal with this shortcoming, in this paper we propose a distributed architecture aimed at identifying and reasoning about the current situation of involved entities. Specifically, this work presents an hybrid architecture attaining a synergy among Agent Paradigm (AP), Situation Theory (ST) and semantic fuzzy modeling to efficiently support situation awareness in uncertain environments.

Swarm-based approach to evaluate fuzzy classification of semantic sensor data

Sensor networks currently are employed to collect large amounts of heterogeneous data in different and wide environments. Nevertheless, the rapid development and deployment of sensor technology stress the problem related to the availability of too much data and not enough knowledge. Last trend emphasizes the semantic annotation of sensor data. Semantic sensor data increase interoperability between heterogeneous sensor networks and provide contextual information to support situation awareness and management in several application domains. This work defines a framework aimed to reason on distributed semantic sensor data. In particular, defined approach combines swarm intelligence and Fuzzy Control theory in order to infer emerging situation by performing fuzzy classification of semantic sensor data. Swarm architecture enables us to monitor environment by using spatially distributed autonomous sensors. Fuzzy Control theory allows managing of the uncertainty of data sensing. An application scenario for broadcasting traffic news has been described.

f-SPARQL extension and application to support context recognition

Context aware computing as well as wearable and ubiquitous computing often attain with pattern recognition on incoming sensor data. Recognizing more (useful) contexts requires more information about the context, and thus more sensors and better recognition algorithms. In order to enable logic inference on incoming data, the proposed work assumes that incoming data are represented by means of semantic languages (e.g., RDF, OWL, etc.). Nevertheless, in a context aware computing purely logic-based reasoning on context may not be enough. So, the work introduces soft computing techniques to approximate context recognition. Specifically, this paper introduces an approach to context analysis and recognition that relies on f-SPARQL[1] tool, that is a flexible extension of SPARQL. In particular, in this work a JAVA implementation of f-SPARQL and the integrated support for fuzzy clustering and classification are discussed. This tool is exploited in the architecture that foresees some task oriented agents in order to achieve context analysis and recognition in order to identify critical situations. Finally, a simple application scenario and preliminary experimental results have been described.

Enhanced Healthcare Environment by Means of Proactive Context Aware Service Discovery

Context aware computing attains environments monitoring by means of sensors in order to provide relevant information or services according to the identified context. Nowadays, ad hoc wireless sensor networks for medical purposes are playing an increasing role within healthcare. Specifically, Body Sensor Networks (BSN) and Wireless Sensor Network, are being designed for prophylactic and follow-up monitoring of patients e.g., at home, at hospital, and so on. This work defines a framework aimed at proactively providing personalized healthcare services by performing sensor data analysis in order to recognize specific users context. In particular, the approach is strongly based on the synergy between semantic formalisms and soft computing techniques. Semantic Web formalisms are exploited to model healthcare services and context. Soft computing techniques are applied in order to support activity of unsupervised context analysis and semantic service matchmaking. Specifically, Fuzzy Logic enable us to automatically characterize the context and to consequently find the set of healthcare services among the available ones that approximately meet the users context. Experimental results shows performance in terms of services matchmaking.