A novel ontology consistent with acknowledged standards in smart homes

Ning, Huansheng; Shi, Feifei; Zhu, Tao; Chen, Liming

doi:10.1016/j.comnet.2018.11.004

Cited by 25 publications

(10 citation statements)

References 14 publications

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“…There are three layers, the first layer is converting the raw sensor data to an entity event. Sensors have been attached in physical objects and reflect an attribute of this object [31]. The entity event layer is established according to the activity concept.…”

Section: Methodsmentioning

confidence: 99%

A hybrid approach to inferring the Internet of Things for complex activity recognition

Ning

Zhu

et al. 2019

J Wireless Com Network

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With the rapid development and large-scale uptake of the Internet of Things, smart home is evolving from a vision towards a realistically viable solution for assisted living. Activity recognition is one of the fundamental tasks in order to provide accurate and timely assistance and service. As daily living scenarios are full of similar activities, missing data, and noise, inferring complex activities using knowledge-driven reasoning algorithms suffers from several drawbacks, e.g., real-time raw sensor data segmentation, poor generalization, higher computational complexity, and scalability. To address these problems, this paper proposes a hybrid approach to complex daily activity recognition by merging the first-order logic and probability graphic modeling. Specifically, we develop a novel "Markov logic network" combining data-driven multi-feature and simplified rule-based modeling and inference, thus enabling and supporting the applicability and robustness of daily activity recognition. To evaluate the approach and associated methods, we design a testing scenario with a number of similar activity groups, missing data, or disturbance test datasets in a multi-modeling sensor scene. Initial results show our approach outperforms the traditional approach with a better accuracy in the situations of similar activities with missing data and noise disturbance. Experiments are also conducted to compare the Gibbs sampling and MC-SAT sampling algorithms for Markov logic network, and the results show that the Gibbs is better in our experimental settings.

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Section: Methodsmentioning

confidence: 99%

A hybrid approach to inferring the Internet of Things for complex activity recognition

Ning

Zhu

et al. 2019

J Wireless Com Network

Self Cite

View full text Add to dashboard Cite

show abstract

“…It models sensor streams, identifies patterns, and discovers the overlapping temporal relations in them. It supports generality in terms of data semantization [23], offers more expressiveness, and helps in decoupling the concurrent fragments of sensor data rather than using non-semantic models. It not only provides a basic model for representing the atomic and complex ADLs but also supports the expansion of dataset instances through the SPARQL queries.…”

Section: Data Sensing and Representationmentioning

confidence: 99%

SemImput: Bridging Semantic Imputation with Deep Learning for Complex Human Activity Recognition

Razzaq

Cleland

Nugent

et al. 2020

Sensors

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The recognition of activities of daily living (ADL) in smart environments is a well-known and an important research area, which presents the real-time state of humans in pervasive computing. The process of recognizing human activities generally involves deploying a set of obtrusive and unobtrusive sensors, pre-processing the raw data, and building classification models using machine learning (ML) algorithms. Integrating data from multiple sensors is a challenging task due to dynamic nature of data sources. This is further complicated due to semantic and syntactic differences in these data sources. These differences become even more complex if the data generated is imperfect, which ultimately has a direct impact on its usefulness in yielding an accurate classifier. In this study, we propose a semantic imputation framework to improve the quality of sensor data using ontology-based semantic similarity learning. This is achieved by identifying semantic correlations among sensor events through SPARQL queries, and by performing a time-series longitudinal imputation. Furthermore, we applied deep learning (DL) based artificial neural network (ANN) on public datasets to demonstrate the applicability and validity of the proposed approach. The results showed a higher accuracy with semantically imputed datasets using ANN. We also presented a detailed comparative analysis, comparing the results with the state-of-the-art from the literature. We found that our semantic imputed datasets improved the classification accuracy with 95.78% as a higher one thus proving the effectiveness and robustness of learned models.

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“…IoT-Lite [25] is an extension of SSN ontology that aims to create a lightweight ontology of cores that enables fast annotation and processing times. [26] is a smart home friendly extension to SSN ontology. It incorporates concepts such as spatio-temporal descriptions and user profiles to provide a comprehensive ontology in the smart home to achieve semantic richness.…”

Section: Semantic Web and Sensorsmentioning

confidence: 99%

RW-QAnswer: an assisting system for intelligent environments using semantic technology

Shimoda

Diefenbach²,

Singh

et al. 2020

J Reliable Intell Environ

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We have seen rapid growth of Internet of Things (IoT) paradigm. Challenges of IoT include the need to obtain comprehensive environmental information using multiple and different types of sensors as well as the need to reduce the amount of bandwidth used by a large number of sensors. Especially when image sensors are used, a significant amount of bandwidth is used for sending images. In addition, the use of IoT devices need not be limited to experts who are good at handling sensors. For example, there is the home IoT system which is expected to be used at home by several users. Thus, it is necessary to have an interface which is easy to handle, even for people who are not good at handling data and sensors. To solve these problems, we use Resource Description Framework (RDF) used in the Semantic Web field as metadata of sensor data for comprehensive environmental information acquisition. Then, by linking with an existing RDF search system called QAnswer, which uses natural language, we create a system that enables sensor data search using natural language. Thus, we design an intelligent system which enables users to interact with sensors using natural language. By combining an RDF database and a server which controls the flow of messages, we then investigate the trade-off between the response time to a user's request and the amount of bandwidth usage by messages. Our results show that in a sensor network using RDF, it is possible to reduce the amount of communication traffic by optimally transferring RDF and sensor data only on arrival of a request and this can be done without much increase in the communication latency.

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A novel ontology consistent with acknowledged standards in smart homes

Cited by 25 publications

References 14 publications

A hybrid approach to inferring the Internet of Things for complex activity recognition

A hybrid approach to inferring the Internet of Things for complex activity recognition

SemImput: Bridging Semantic Imputation with Deep Learning for Complex Human Activity Recognition

RW-QAnswer: an assisting system for intelligent environments using semantic technology

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