Building Blocks of the Internet of Things: State of the Art and Beyond

Serbanati, Alexandru; Medaglia, Carlo Maria; Ceipidor, U. Biader

doi:10.5772/19997

Cited by 47 publications

(21 citation statements)

References 17 publications

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“…We perform a comparative study on the service discovery methods using the OWL-S service retrieval test collection OWLS-TC v3.0 10 (which consists of 1007 services). The services are divided into seven categories and a total of 29 queries are provided together with a relevant answer set for each query.…”

Section: Performance Evaluationmentioning

confidence: 99%

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Knowledge Representation in the Internet of Things: Semantic Modelling and its Applications

Wang

Cassar

et al. 2013

Automatika

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Original scientific paperSemantic modelling provides a potential basis for interoperating among different systems and applications in the Internet of Things (IoT). However, current work has mostly focused on IoT resource management while not on the access and utilisation of information generated by the "Things". We present the design of a comprehensive and lightweight semantic description model for knowledge representation in the IoT domain. The design follows the widely recognised best practices in knowledge engineering and ontology modelling. Users are allowed to extend the model by linking to external ontologies, knowledge bases or existing linked data. Scalable access to IoT services and resources is achieved through a distributed, semantic storage design. The usefulness of the model is also illustrated through an IoT service discovery method.Key words: Internet of things, Knowledge representation, Ontology engineering, Distributed service storage, Service discovery, Service ranking Prikaz znanja u internetu stvari: semantičko modeliranje i njegove primjene. Semantičko modeliranje pruža potencijalnu osnovu za meudjelovanje različitih sustava i aplikacija unutar interneta stvari (IoT). Meutim, postojeći radovi uglavnom su fokusirani na upravljanje IoT resursima, ali ne i pristupu i korištenju informacija koje generira "stvar". Predstavljamo projektiranje sveobuhvatnog i laganog semantičkog opisnog modela za prikaz znanja u IoT domeni. Projektiranje slijedi široko-priznate najbolje običaje u inženjerstvu znanja i ontološkom modeliranju. Korisnicima se dopušta proširenje modela povezivanjem na eksterne ontologije, baze znanja ili postojeće povezane podatke. Skalabilni pristup IoT uslugama i resursima postiže se kroz distribuirano, semantičko projektiranje pohrane. Upotrebljivost modela takoer je ilustrirana kroz metodu pronalaska IoT usluga.Ključne riječi: Internet stvari, prikaz znanja, ontološko inženjerstvo, distribuirana pohrana usluga, pronalazak usluga, poredak usluga

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Section: Performance Evaluationmentioning

confidence: 99%

“…Recent works in [9,10] propose a modelling approach in which resources in the IoT are able to expose standard service interfaces (we term the services exposed by the connected Things in the physical world as "IoT Services"), embodying the "EntityDevice-Resource" modelling approach.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Representation in the Internet of Things: Semantic Modelling and its Applications

Wang

Cassar

et al. 2013

Automatika

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“…Common attacks of this nature include Sybil, node-replication attack, Hello flood attack, selective forwarding, and sinkhole attack / black holes (Serbanati et al, 2011).…”

Section: Attacks On Sensor Networkmentioning

confidence: 99%

Sensor Network Topologies and Design Considerations

McGrath¹,

Scanaill²

2013

Sensor Technologies

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Connecting the devices in our lives, from toasters to fitness devices, to each other and to the Internet is the fundamental principle of the Internet of Things (IoT). Enabling this connectivity requires at the very least a direct connection to the Internet, but often data is routed and processed at a local network stage before being passed to the Internet. Some sensor networks are not connected to the Internet at all, and their data is simply aggregated and displayed at a local aggregation point, such as a smartphone or PC. The topology of the sensor network depends largely on the overall system application: a personal area network may simply stream data from all sensors to a single central aggregator (star topology), whereas a home monitoring network may use a self-healing mesh topology. The different sensor topologies and the applications to which they are most suited are discussed in this chapter.As more and more devices are connected to the Internet, big data challenges emerge: volume, velocity, variety, and veracity. Sensor data is cheap to generate, but expensive to move, store, and manage. Not all data is useful. The sensor network, or more specifically the aggregator on the sensor network, has a key role in aggregating data: identifying which data should be presented to the user or a remote network and which data should be discarded. The earlier data is processed in the sensor lifecycle, the cheaper the overall system will be. Increasingly capable, low-power, low-cost devices are being developed for this edge-processing role. We discuss these aggregation devices in detail in this chapter.Managing large numbers of different sensors across a sensor network is a challenging task, particularly if the sensors are deployed in remote locations. Cloud-based sensor-network management tools are becoming increasingly popular. These tools provide real-time network status information, the ability to remotely change a sensor's configuration, and basic data storage and visualization. Many popular cloud-based services can be quickly integrated with popular sensor platforms, such as Arduino. Therefore, they are becoming a popular tool for the maker community and IoT enthusiasts. The most popular sensor-network management tools and their sensor interfaces are discussed and compared at the end of this chapter. Sensor Network ComponentsA sensor network consists of a group of smart sensors that are wired or wirelessly connected to another smart sensor or to a common aggregator. In networking terminology, each component in the network that has a communications module is called a node. A node that generates data is called a source node, while a node that requests data is called a sink or sink node. A sink can be another sensor node on the network, a gateway to another larger network, or a local aggregator. A source node can report routine data, an alert, or maintenance data. The sensor network performs two key tasks: data gathering and data dissemination. Data gathering is term used to describe the capture and transfer of data...

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“…The recent work in [6] and [7] proposes a modeling approach in which resources on the IoT are able to expose standard service interfaces. This coincides with the principle of the Service Oriented Computing [8] and potentially provides a scalable, distributed and service-oriented means to access IoT information.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Ontology for Knowledge Representation in the Internet of Things

Wang

Toenjes³

et al. 2012

2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications

132

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Abstract-Semantic modeling for the Internet of Things has become fundamental to resolve the problem of interoperability given the distributed and heterogeneous nature of the "Things". Most of the current research has primarily focused on devices and resources modeling while paid less attention on access and utilisation of the information generated by the things. The idea that things are able to expose standard service interfaces coincides with the service oriented computing and more importantly, represents a scalable means for business services and applications that need context awareness and intelligence to access and consume the physical world information. We present the design of a comprehensive description ontology for knowledge representation in the domain of Internet of Things and discuss how it can be used to support tasks such as service discovery, testing and dynamic composition.

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Building Blocks of the Internet of Things: State of the Art and Beyond

Cited by 47 publications

References 17 publications

Knowledge Representation in the Internet of Things: Semantic Modelling and its Applications

Knowledge Representation in the Internet of Things: Semantic Modelling and its Applications

Sensor Network Topologies and Design Considerations

A Comprehensive Ontology for Knowledge Representation in the Internet of Things

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