Towards an Ontology for IoT Context-Based Security Evaluation

Gonzalez-Gil, Pedro; Skarmeta, Antonio F.; Martı́nez, Juan Antonio

doi:10.1109/giots.2019.8766400

Cited by 15 publications

(7 citation statements)

References 11 publications

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“…Dealing with sensory data is usually noisy and faulty, so we also need to model the Quality of Information. The common quality concepts modeled are properties such as Completeness, Correctness, Concordance, Currency, Plausibility [ 28 ], Security [ 29 ], Access control in cloud data [ 30 ] and Confidence in the provenance of the data, PROV-O (Provenance Ontology) [ 31 ]. Another model [ 16 ] considers two dimensions of quality in which some of the mentioned properties also are included: generic data quality dimension (Accuracy, Confidence, Completeness, Data volume, Timeliness, Ease of access, Access security and Interpretability) and domain-specific data quality dimension (Duplicates and Availability).…”

Section: Literature Reviewmentioning

confidence: 99%

Systematic design of health monitoring systems centered on older adults and ADLs

Garcia-Moreno,

Bermudez-Edo,

Pérez-Mármol

et al. 2024

BMC Med Inform Decis Mak

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Background Older adults face unique health challenges as they age, including physical and mental health issues and mood disorders. Negative emotions and social isolation significantly impact mental and physical health. To support older adults and address these challenges, healthcare professionals can use Information and Communication Technologies (ICTs) such as health monitoring systems with multiple sensors. These systems include digital biomarkers and data analytics that can streamline the diagnosis process and help older adults to maintain their independence and quality of life. Method A design research methodology is followed to define a conceptual model as the main artifact and basis for the systematic design of successful systems centered on older adults monitoring within the health domain. Results The results include a conceptual model focused on older adults' Activities of Daily Living (ADLs) and Health Status, considering various health dimensions, including social, emotional, physical, and cognitive dimensions. We also provide a detailed instantiation of the model in real use cases to validate the usefulness and feasibility of the proposal. In particular, the model has been used to develop two health systems intended to measure the degree of the elders' frailty and dependence with biomarkers and machine learning. Conclusions The defined conceptual model can be the basis to develop health monitoring systems with multiple sensors and intelligence based on data analytics. This model offers a holistic approach to caring for and supporting older adults as they age, considering ADLs and various health dimensions. We have performed an experimental and qualitative validation of the proposal in the field of study. The conceptual model has been instantiated in two specific case uses, showing the provided abstraction level and the feasibility of the proposal to build reusable, extensible and adaptable health systems. The proposal can evolve by exploiting other scenarios and contexts.

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Section: Literature Reviewmentioning

confidence: 99%

Systematic design of health monitoring systems centered on older adults and ADLs

Garcia-Moreno,

Bermudez-Edo,

Pérez-Mármol

et al. 2024

BMC Med Inform Decis Mak

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“…DS4IoT (Gonzalez-Gil, Martinez, & Skarmeta, 2020) reused concepts from ST AC (Gyrard, Bonnet, & Boudaoud, 2014a), IoT SECEv (Gonzalez-Gil, Skarmeta, & Martinez, 2019 and IoT − P riv (Arruda & Bulcão-Neto, 2019). IoT SecEv (Gonzalez-Gil et al, 2019) reused concepts from IoT Sec (Mozzaquatro et al, 2015)/ COP ri (Gharib et al, 2020) reused concepts from P ri (Kalloniatis et al, 2008). IoT − OAS (Cirani et al, 2014) reused concepts from CoAP (Pereira, Eliasson, & Delsing, 2014).…”

Section: Analysis Of the Selected Ontologiesmentioning

confidence: 99%

A Survey on Understanding and Representing Privacy Requirements in the Internet-of-Things

Ogunniye

Kökciyan

2023

jair

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People are interacting with online systems all the time. In order to use the services being provided, they give consent for their data to be collected. This approach requires too much human effort and is impractical for systems like Internet-of-Things (IoT) where human-device interactions can be large. Ideally, privacy assistants can help humans make privacy decisions while working in collaboration with them. In our work, we focus on the identification and representation of privacy requirements in IoT to help privacy assistants better understand their environment. In recent years, more focus has been on the technical aspects of privacy. However, the dynamic nature of privacy also requires a representation of social aspects (e.g., social trust). In this survey paper, we review the privacy requirements represented in existing IoT ontologies. We discuss how to extend these ontologies with new requirements to better capture privacy, and we introduce case studies to demonstrate the applicability of the novel requirements.

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“…When talking about Quality of Information, categories or metrics are important to describe the details. There are five common metrics: Completeness, Correctness, Concordance, Currency, Plausibility [18], and Security [19]. In the CityPulse project (http://www.ict-citypulse.eu/page/), they extend the ontology and used five categories; Timeliness, Cost, Accuracy, Communication, and Security, each with a collection of sub-metrics.…”

Section: Related Workmentioning

confidence: 99%

IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams and Its Use with Data Analytics and Event Detection Services

Elsaleh

Enshaeifar

Rezvani

et al. 2020

Sensors

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With the proliferation of sensors and IoT technologies, stream data are increasingly stored and analysed, but rarely combined, due to the heterogeneity of sources and technologies. Semantics are increasingly used to share sensory data, but not so much for annotating stream data. Semantic models for stream annotation are scarce, as generally, semantics are heavy to process and not ideal for Internet of Things (IoT) environments, where the data are frequently updated. We present a light model to semantically annotate streams, IoT-Stream. It takes advantage of common knowledge sharing of the semantics, but keeping the inferences and queries simple. Furthermore, we present a system architecture to demonstrate the adoption the semantic model, and provide examples of instantiation of the system for different use cases. The system architecture is based on commonly used architectures in the field of IoT, such as web services, microservices and middleware. Our system approach includes the semantic annotations that take place in the pipeline of IoT services and sensory data analytics. It includes modules needed to annotate, consume, and query data annotated with IoT-Stream. In addition to this, we present tools that could be used in conjunction to the IoT-Stream model and facilitate the use of semantics in IoT.

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Towards an Ontology for IoT Context-Based Security Evaluation

Cited by 15 publications

References 11 publications

Systematic design of health monitoring systems centered on older adults and ADLs

Systematic design of health monitoring systems centered on older adults and ADLs

A Survey on Understanding and Representing Privacy Requirements in the Internet-of-Things

IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams and Its Use with Data Analytics and Event Detection Services

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