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

Ruta, Michele; Scioscia, Floriano; Pinto, Agnese; Sciascio, Eugenio Di; Gramegna, Filippo; Ieva, Saverio; Loseto, Giuseppe

doi:10.1109/greencom-ithings-cpscom.2013.103

Cited by 26 publications

(12 citation statements)

References 20 publications

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“…Main motivation for this paper stems from our studies in pervasive computing field, and particularly in the Semantic Web of Things (SWoT) one [3], [2]. In such scenarios, infor mation is gathered through micro-devices enriching everyday items or deployed in given environments and interconnected wirelessly.…”

Section: Motivationmentioning

confidence: 99%

A semantic-based approach for Machine Learning data analysis

Pinto

Scioscia

Loseto

et al. 2015

Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)

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Pervasive applications and services are increasingly based on the intelligent interpretation of data gathered via heterogeneous sensors dipped in the environment. Classical Machine Learning (ML) techniques often do not go beyond a basic classification, lacking a meaningful representation of the detected events. This paper introduces a early proposal for a semantic-enhanced machine learning analysis on data of sensors streams, performing better even on resource-constrained pervasive smart objects. The framework merges an ontology driven characterization of statistical data distributions with non standard matchmaking services, enabling a fine-grained event detection by treating the typical classification problem of ML as a resource discovery.

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Section: Motivationmentioning

confidence: 99%

A semantic-based approach for Machine Learning data analysis

Pinto

Scioscia

Loseto

et al. 2015

Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)

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“…The combination of the IoT with Semantic Web ideas and technologies is bringing about the so-called Semantic Web of Things (SWoT) [28]. This paradigm aims to enable novel classes of intelligent applications and services grounded on Knowledge Representation (KR), exploiting semantic-based automatic inferences to infer implicit information starting from an explicit event and context detection [30].…”

Section: Motivationmentioning

confidence: 99%

Machine learning in the Internet of Things: A semantic-enhanced approach

Ruta

Scioscia

Loseto

et al. 2018

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New Internet of Things (IoT) applications and services more and more rely on an intelligent understanding of the environment from data gathered via heterogeneous sensors and micro-devices. Though increasingly effective, Machine Learning (ML) techniques generally do not go beyond classification of events with opaque labels, lacking meaningful representations and explanations of taxonomies. This paper proposes a framework for a semantic-enhanced data mining on sensor streams, amenable to resource-constrained pervasive contexts. It merges an ontology-based characterization of data distributions with non-standard reasoning for a fine-grained event detection by treating the typical classification problem of ML as a resource discovery. Outputs of classification are endowed with machine-understandable descriptions in standard Semantic Web languages, while explanation of matchmaking outcomes motivates confidence on results. A case study on road and traffic analysis allowed to validate the proposal and achieve an assessment with respect to state-of-the-art ML algorithms.

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“…Additionally, search and manipulation of these metadata can be done through query languages like SPARQL. Ruta et al [94] has adopted the SSN-XG W3C ontology to collect and annotate data from Semantic Sensor Networks (SSN) and by extending the CoAP protocol (discussed in section 1.6) and CoRE Link Format that is used for resource discovery, their proposed solution ranks resources based on partial or full request matching situations.…”

Section: Identification and Resource/service Discoverymentioning

confidence: 99%

Internet of Things: an overview

2016

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As technology proceeds and the number of smart devices continues to grow substantially, need for ubiquitous context-aware platforms that support interconnected, heterogeneous, and distributed network of devices has given rise to what is referred today as Internet-of-Things. However, paving the path for achieving aforementioned objectives and making the IoT paradigm more tangible requires integration and convergence of different knowledge and research domains, covering aspects from identification and communication to resource discovery and service integration. Through this chapter, we aim to highlight researches in topics including proposed architectures, security and privacy, network communication means and protocols, and eventually conclude by providing future directions and open challenges facing the IoT development.

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

Cited by 26 publications

References 20 publications

A semantic-based approach for Machine Learning data analysis

A semantic-based approach for Machine Learning data analysis

Machine learning in the Internet of Things: A semantic-enhanced approach

Internet of Things: an overview

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