Search Techniques for the Web of Things: A Taxonomy and Survey

Zhou, Yan; De, Suparna; Wang, Wei; Moessner, Klaus

doi:10.3390/s16050600

Cited by 53 publications

(47 citation statements)

References 81 publications

(125 reference statements)

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“…An interesting search mechanism for retrieving observation data from mobile sources has been detailed in [62]. As pointed out in [98], data collection methods need to leverage Big Data techniques and distributed intelligence to be able to facilitate cooperative sensing and transparent access to data processing mechanisms.…”

Section: Discussionmentioning

confidence: 99%

Cyber–Physical–Social Frameworks for Urban Big Data Systems: A Survey

Zhou

Abad³

et al. 2017

Applied Sciences

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Abstract:The integration of things' data on the Web and Web linking for things' description and discovery is leading the way towards smart Cyber-Physical Systems (CPS). The data generated in CPS represents observations gathered by sensor devices about the ambient environment that can be manipulated by computational processes of the cyber world. Alongside this, the growing use of social networks offers near real-time citizen sensing capabilities as a complementary information source. The resulting Cyber-Physical-Social System (CPSS) can help to understand the real world and provide proactive services to users. The nature of CPSS data brings new requirements and challenges to different stages of data manipulation, including identification of data sources, processing and fusion of different types and scales of data. To gain an understanding of the existing methods and techniques which can be useful for a data-oriented CPSS implementation, this paper presents a survey of the existing research and commercial solutions. We define a conceptual framework for a data-oriented CPSS and detail the various solutions for building human-machine intelligence.

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Section: Discussionmentioning

confidence: 99%

Cyber–Physical–Social Frameworks for Urban Big Data Systems: A Survey

Zhou

Abad³

et al. 2017

Applied Sciences

Self Cite

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“…49 This will ease in searching covering a multitude of functionality and dimensionality. Since IoT has multiple parameters to be focused, therefore, resource discovery demands techniques or algorithms that cover basic fundamental search principles for indexing and clustering, knowledge representation, and content being searched.…”

Section: Motivationmentioning

confidence: 99%

Clustering‐based resource discovery on Internet‐of‐Things

Bharti

Kumar

Saxena

2018

Int J Communication

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Resource discovery on Internet-of-Things paradigm is an eminent challenge due to data-specific activities with respect to foraging and sense-making loops. The prerequisite to deal with the challenge is to process and analyze the data that require resources to be indexed, ranked, and stored in an efficient manner. A novel clustering technique is proposed to resolve the specified challenge. The technique, namely, iterative k-means clustering algorithm, targets concrete cluster formation using similarity coefficients of vector space model and performs efficient search against matching criteria with respect to complexity. It is simulated on MATLAB, and the obtained results are compared with fuzzy k-means and fuzzy c-means clustering algorithm with similarity coefficients of vector space model against exponential increase in the number of resources.

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“…Each node in the tree is associated with MBR that represents the location range of its child nodes. However, R-tree is not scalable if there are frequent changes in MBR [16]. To address this problem, the authors index gateways in which each of them is associated with MBR (the location range of its registered services).…”

Section: Background and Related Workmentioning

confidence: 99%

Distributed spatial indexing for the Internet of Things data management

Fathy

Barnaghi

Tafazolli

2017

2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM)

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Abstract-The Internet of Things (IoT) has become a new enabler for collecting real-world observation and measurement data from the physical world. The IoT allows objects with sensing and network capabilities (i.e. Things and devices) to communicate with one another and with other resources (e.g. services) on the digital world. The heterogeneity, dynamicity and ad-hoc nature of underlying data, and services published by most of IoT resources make accessing and processing the data and services a challenging task. The IoT demands distributed, scalable, and efficient indexing solutions for large-scale distributed IoT networks. We describe a novel distributed indexing approach for IoT resources and their published data. The index structure is constructed by encoding the locations of IoT resources into geohashes and then building a quadtree on the minimum bounding box of the geohash representations. This allows to aggregate resources with similar geohashes and reduce the size of the index. We have evaluated our proposed solution on a large-scale dataset and our results show that the proposed approach can efficiently index and enable discovery of the IoT resources with 65% better response time than a centralised approach and with a high success rate (around 90% in the first few attempts).

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Search Techniques for the Web of Things: A Taxonomy and Survey

Cited by 53 publications

References 81 publications

Cyber–Physical–Social Frameworks for Urban Big Data Systems: A Survey

Cyber–Physical–Social Frameworks for Urban Big Data Systems: A Survey

Clustering‐based resource discovery on Internet‐of‐Things

Distributed spatial indexing for the Internet of Things data management

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