Towards IoT data classification through semantic features

Antunes, Mário; Gomes, Diogo; Aguiar, Rui L.

doi:10.1016/j.future.2017.11.045

Cited by 37 publications

(22 citation statements)

References 27 publications

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“…In IoT, pattern recognition algorithms have been used in several works. This paper is interested in the application of the two main types of pattern recognition algorithms: classification [6][7][8][9][10][11][12][13][14][15], and clustering [16][17][18][19][20] algorithms. The survey in this section makes possible, on the one hand, to clear the scope of the current use of these algorithms and, on the other hand, to motivate the application of these algorithms to sustain of non-functional requirements, such as QoS in IoT platforms.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Currently, there is no a uniform way to represent, share, and understand IoT data, leading to information silos where the devices cannot work and learn together with minimal human intervention. [10] discusses the limitations of current storage and analytical solutions, points the advantages of semantic approaches for context organization, and proposes an unsupervised model to learn word categories automatically. The model was evaluated in the Miller-Charles dataset and an IoT semantic dataset extracted from an IoT platform.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The average value of the GAD of the class p for each individual in a class k, is calculated according to Eq (10).…”

Section: Lamda-had Algorithmmentioning

confidence: 99%

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Experimental comparison of the diagnostic capabilities of classification and clustering algorithms for the QoS management in an autonomic IoT platform

Morales¹,

Ouedraogo²,

Aguilar³

et al. 2019

SOCA

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The IoT platforms must allow the communication between the Applications and Devices according to their non-functional requirements. One of the main non-functional requirements is the Quality of Service (QoS). In a previous work has been defined an Autonomic Internet of Things (IoT) platform for the QoS Management, based on the concept of autonomic cycle of data analysis tasks. In this platform have been defined two autonomic cycles, one based on a classification task that determines the current operational state to define the set of tasks to execute in the communication system to guarantee a given QoS. The other one is based on a clustering task that discovers the current operational state, and based on it, determines the set of tasks to be executed in the communication system. This paper analyzes the diagnostic capabilities of the system based on both approaches, using different metrics. For that, a real scenario has been considered, with simulations that have generated data to test both tasks. Each technique has different aspects to be considered for a correct QoS management in the context of IoT platforms. The classification technique can determine very well the learned operational states, but the clustering approach can carry out a more detailed description of the operational states. Additionally, due to the classification and clustering technique used, called LAMDA (Learning Algorithm for Multivariate Data Analysis), the paper analyzes the operational state profile determined by them, which is very useful in a diagnostic process. IntroductionThe classical components of an IoT ecosystem, according to the standards are [1][2]: Devices, Network, IoT platform, and Application. Devices are the responsible of the collection of the data, and in some case, the preprocessing of these data and the execution of specific tasks; the Applications exploit the advantage of a set of interconnected Devices in order to carry out actions in the environment; the Network allows the communication between the different components in the IoT platform, particularly, between the devices and applications; and finally, the IoT platform manages smart capabilities like the autonomy, security, among other things, and can support heterogeneous non-functional requirements of Applications and Devices. On the one hand, the IoT platforms are based on European Telecommunications Standards Institute (ETSI) SmartM2M and OneM2M [1][2] specifications, which define that an IoT platform consists mainly of two types of entities, IoT server(s) and gateway(s). These entities are implemented as Chain of Network Functions (NFs) that achieve a connection between applications and devices. Namely, NFs are processing functions, with defined functional behaviors and interfaces (e.g. a load balancing function applied on Internet protocol (IP) packets). On the other hand, these platforms are not able to sustain Quality of Service (QoS) to IoT missions critical Applications like remote surgery [34]. As per [34], QoS in IoT is one of the critical factors, which nee...

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Experimental comparison of the diagnostic capabilities of classification and clustering algorithms for the QoS management in an autonomic IoT platform

Morales¹,

Ouedraogo²,

Aguilar³

et al. 2019

SOCA

View full text Add to dashboard Cite

show abstract

“…In order to promote it, different studies have been conducted. For instance, a threelayered architecture has been proposed to abstract the IoT devices heterogeneity [21], a metadata model has been proposed to unify the IoT data description [22] and semantic methods have been proposed to identify similarity between data sent by heterogeneous sensors [23]. These studies deal only with sharing the sensors raw measurements.…”

Section: Related Workmentioning

confidence: 99%

Efficient Resource Allocation for Multi-Tenant Monitoring of Edge Infrastructures

Abderrahim

Ouzzif

Jérôme

et al. 2019

2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)

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By relying on small sized and massively distributed infrastructures, the Edge computing paradigm aims at supporting the low latency and high bandwidth requirements of the next generation services that will leverage IoT devices (e.g., video cameras, sensors). To favor the advent of this paradigm, management services, similar to the ones that made the success of Cloud computing platforms, should be proposed. However, they should be designed in order to cope with the limited capabilities of the resources that are located at the edge. In that sense, they should mitigate as much as possible their footprint. Among the different management services that need to be revisited, we investigate in this paper the monitoring one. Monitoring functions tend to become compute-, storage-and network-intensive, in particular because they will be used by a large part of applications that rely on real-time data. To reduce as much as possible the footprint of the whole monitoring service, we propose to mutualize identical processing functions among different tenants while ensuring their quality-of-service (QoS) expectations. We formalize our approach as a constraint satisfaction problem and show through micro-benchmarks its relevance to mitigate compute and network footprints.

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“…The most important aspect of clustering IoT streaming data is its dynamic and heterogeneous nature. Therefore, a novel clustering mechanism is needed to represent the hierarchical relationships-based annotations for the IoT streaming data [6]. In this paper, incremental hierarchical clustering is deployed for unifying the streaming data in a hierarchical manner.…”

Section: Introductionmentioning

confidence: 99%

Incremental Hierarchical Clustering driven Automatic Annotations for Unifying IoT Streaming Data

Núñez‐Valdéz¹,

Solanki²,

Balakrishna³

et al. 2020

IJIMAI

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In the Internet of Things (IoT), Cyber-Physical Systems (CPS), and sensor technologies huge and variety of streaming sensor data is generated. The unification of streaming sensor data is a challenging problem. Moreover, the huge amount of raw data has implied the insufficiency of manual and semi-automatic annotation and leads to an increase of the research of automatic semantic annotation. However, many of the existing semantic annotation mechanisms require many joint conditions that could generate redundant processing of transitional results for annotating the sensor data using SPARQL queries. In this paper, we present an Incremental Clustering Driven Automatic Annotation for IoT Streaming Data (IHC-AA-IoTSD) using SPARQL to improve the annotation efficiency. The processes and corresponding algorithms of the incremental hierarchical clustering driven automatic annotation mechanism are presented in detail, including data classification, incremental hierarchical clustering, querying the extracted data, semantic data annotation, and semantic data integration. The IHC-AA-IoTSD has been implemented and experimented on three healthcare datasets and compared with leading approaches namely-Agent-based Text Labelling and Automatic Selection (ATLAS), Fuzzy-based Automatic Semantic Annotation Method (FBASAM), and an Ontology-based Semantic Annotation Approach (OBSAA), yielding encouraging results with Accuracy of 86.67%, Precision of 87.36%, Recall of 85.48%, and F-score of 85.92% at 100k triple data.

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Towards IoT data classification through semantic features

Cited by 37 publications

References 27 publications

Experimental comparison of the diagnostic capabilities of classification and clustering algorithms for the QoS management in an autonomic IoT platform

Experimental comparison of the diagnostic capabilities of classification and clustering algorithms for the QoS management in an autonomic IoT platform

Efficient Resource Allocation for Multi-Tenant Monitoring of Edge Infrastructures

Incremental Hierarchical Clustering driven Automatic Annotations for Unifying IoT Streaming Data

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