Semantic Models for Scalable Search in the Internet of Things

Mietz, Richard; Groppe, Sven; Römer, Kay Uwe; Pfisterer, Dennis

doi:10.3390/jsan2020172

Cited by 20 publications

(10 citation statements)

References 21 publications

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“…Raychoudhury et al further build a context map based on the relations in the IOT for navigation among objects [4]. Mietz et al extend the search of physical objects by providing semantic Web-based [24] data storage format and the query language. Perera et al [25] propose a model for sensor search based on users priorities and characteristics of sensors (e.g., reliability, accuracy, and battery life).…”

Section: Related Workmentioning

confidence: 99%

A Distributed Relation Detection Approach in the Internet of Things

Zhu

Lü

Cui

et al. 2017

Mobile Information Systems

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In the Internet of Things, it is important to detect the various relations among objects for mining useful knowledge. Existing works on relation detection are based on centralized processing, which is not suitable for the Internet of Things owing to the unavailability of a server, one-point failure, computation bottleneck, and moving of objects. In this paper, we propose a distributed approach to detect relations among objects. We first build a system model for this problem that supports generic forms of relations and both physical time and logical time. Based on this, we design the Distributed Relation Detection Approach (DRDA), which utilizes a distributed spanning tree to detect relations using in-network processing. DRDA can coordinate the distributed treebuilding process of objects and automatically change the depth of the routing tree to a proper value. Optimization among multiple relation detection tasks is also considered. Extensive simulations were performed and the results show that the proposed approach outperforms existing approaches in terms of the energy consumption.

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Section: Related Workmentioning

confidence: 99%

A Distributed Relation Detection Approach in the Internet of Things

Zhu

Lü

Cui

et al. 2017

Mobile Information Systems

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“…room). Another method based on semantic prediction models is proposed in [Mietz et al 2013] for ranking IoT resources. The models are constructed using RDF to describe states that sensors can measure (e.g.…”

Section: Fig 4 Taxonomy Of Indexing Approachesmentioning

confidence: 99%

Large-Scale Indexing, Discovery, and Ranking for the Internet of Things (IoT)

2018

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Network-enabled sensing and actuation devices are key enablers to connect real-world objects to the cyber world. The Internet of Things (IoT) consists of the network-enabled devices and communication technologies that allow connectivity and integration of physical objects (Things) into the digital world (Internet). Enormous amounts of dynamic IoT data are collected from Internet-connected devices. IoT data is usually multi-variant streams that are heterogeneous, sporadic, multi-modal and spatio-temporal. IoT data can be disseminated with di erent granularities and have diverse structures, types and qualities. Dealing with the data deluge from heterogeneous IoT resources and services imposes new challenges on indexing, discovery and ranking mechanisms that will allow building applications that require on-line access and retrieval of ad-hoc IoT data. However, the existing IoT data indexing and discovery approaches are complex or centralised which hinders their scalability. The primary objective of this paper is to provide a holistic overview of the state-of-the-art on indexing, discovery and ranking of IoT data. The paper aims to pave the way for researchers to design, develop, implement and evaluate techniques and approaches for on-line large-scale distributed IoT applications and services.

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“…Some research efforts have proposed to explore things similarity and relations from semantic Web perspective [Mietz et al 2013;Christophe et al 2011a]. In such cases, explicit relations of things can be characterized by using keyword-based, textual-level calculations.…”

Section: Introductionmentioning

confidence: 99%

Unveiling Correlations via Mining Human-Thing Interactions in the Web of Things

Yao

Sheng

Ngu

et al. 2017

ACM Trans. Intell. Syst. Technol.

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With recent advances in radio-frequency identification (RFID), wireless sensor networks, and Web services, physical things are becoming an integral part of the emerging ubiquitous Web. Finding correlations among ubiquitous things is a crucial prerequisite for many important applications such as things search, discovery, classification, recommendation, and composition. This article presents DisCor-T, a novel graph-based approach for discovering underlying connections of things via mining the rich content embodied in the humanthing interactions in terms of user, temporal and spatial information. We model these various information using two graphs, namely a spatio-temporal graph and a social graph. Then, random walk with restart (RWR) is applied to find proximities among things, and a relational graph of things (RGT) indicating implicit correlations of things is learned. The correlation analysis lays a solid foundation contributing to improved effectiveness in things management and analytics. To demonstrate the utility of the proposed approach, we develop a flexible feature-based classification framework on top of RGT and perform a systematic case study. Our evaluation exhibits the strength and feasibility of the proposed approach.

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Semantic Models for Scalable Search in the Internet of Things

Cited by 20 publications

References 21 publications

A Distributed Relation Detection Approach in the Internet of Things

A Distributed Relation Detection Approach in the Internet of Things

Large-Scale Indexing, Discovery, and Ranking for the Internet of Things (IoT)

Unveiling Correlations via Mining Human-Thing Interactions in the Web of Things

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