A stream processing architecture for heterogeneous data sources in the Internet of Things

The purpose of this article is to use the Internet of Things related technology to analyze the characteristics of multisource and easy-to-purchase data for the different types of planning data and different levels of cognitive needs of participants in the entire urban planning process. This paper uses the ontology idea to reconstruct the relationship between multisource and heterogeneous planning data including Internet of Things data, planning documents, and planning drawings, to design the data semantic relationship of the ontology model elements, define the relationship between the data types, and implement the ontology-based method. The semantic expression algorithm in the planning field facilitates the exchange of various planning participants’ understanding of the planning scheme, at the same time, according to the classification of multisource heterogeneous data features, logical reasoning of ontology relationships, filtering redundant information, and multisource heterogeneous planning data visualization. Finally, the information of the same nature collected by the sensor nodes of the Internet of Things is batched, and the calculated fusion information is closer to the true value through a series of weighting formulas. Experiments prove that the feature analysis method proposed in this paper can maintain a loss of 0.02% and achieve an accuracy rate of 79.1% when the overall characteristics of digital city planning are reduced by 67%, which effectively proves the multisource heterogeneous data feature analysis for digital city planning importance.

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Consensus-Based Data Management within Fog Computing For the Internet of Things

10.31219/osf.io/cr85d ◽

2021 ◽

Author(s):

AISDL

Keyword(s):

Internet Of Things ◽

Data Management ◽

Fog Computing ◽

Heterogeneous Data ◽

The Internet ◽

Effective Decision ◽

Effective Decision Making ◽

New Generation ◽

Delivery Models ◽

The Internet Of Things

The Internet of Things (IoT) infrastructure forms a gigantic network of interconnected and interacting devices. This infrastructure involves a new generation of service delivery models, more advanced data management and policy schemes, sophisticated data analytics tools, and effective decision making applications. IoT technology brings automation to a new level wherein nodes can communicate and make autonomous decisions in the absence of human interventions. IoT enabled solutions generate and process enormous volumes of heterogeneous data exchanged among billions of nodes. This results in Big Data congestion, data management, storage issues and various inefficiencies. Fog Computing aims at solving the issues with data management as it includes intelligent computational components and storage closer to the data sources.

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Collaborative Filtering Recommendation Algorithm for Heterogeneous Data Mining in the Internet of Things

IEEE Access ◽

10.1109/access.2019.2935224 ◽

2019 ◽

Vol 7 ◽

pp. 123583-123591 ◽

Cited By ~ 1

Author(s):

Ying Gao ◽

Lingxi Ran

Keyword(s):

Data Mining ◽

Internet Of Things ◽

Collaborative Filtering ◽

Heterogeneous Data ◽

The Internet ◽

Recommendation Algorithm ◽

The Internet Of Things

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Description and classification for facilitating interoperability of heterogeneous data/events/services in the Internet of Things

Neurocomputing ◽

10.1016/j.neucom.2016.03.115 ◽

2017 ◽

Vol 256 ◽

pp. 13-22 ◽

Cited By ~ 10

Author(s):

Hyung-Jun Yim ◽

Dongmin Seo ◽

Hanmin Jung ◽

Moon-Ki Back ◽

InA Kim ◽

...

Keyword(s):

Internet Of Things ◽

Heterogeneous Data ◽

The Internet ◽

The Internet Of Things

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Crop Management with the IoT: An Interdisciplinary Survey

Agronomy ◽

10.3390/agronomy11010181 ◽

2021 ◽

Vol 11 (1) ◽

pp. 181 ◽

Cited By ~ 1

Author(s):

Giuliano Vitali ◽

Matteo Francia ◽

Matteo Golfarelli ◽

Maurizio Canavari

Keyword(s):

Machine Learning ◽

Internet Of Things ◽

Crop Management ◽

Cropping System ◽

Heterogeneous Data ◽

Machine Learning Techniques ◽

The Internet ◽

Crop Fields ◽

Learning Techniques ◽

The Internet Of Things

In this study, we analyze how crop management will benefit from the Internet of Things (IoT) by providing an overview of its architecture and components from agronomic and technological perspectives. The present analysis highlights that IoT is a mature enabling technology with articulated hardware and software components. Cheap networked devices can sense crop fields at a finer grain to give timeliness warnings on the presence of stress conditions and diseases to a wider range of farmers. Cloud computing allows reliable storage, access to heterogeneous data, and machine-learning techniques for developing and deploying farm services. From this study, it emerges that the Internet of Things will draw attention to sensor quality and placement protocols, while machine learning should be oriented to produce understandable knowledge, which is also useful to enhance cropping system simulation systems.

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IoTCrawler: Challenges and Solutions for Searching the Internet of Things

Sensors ◽

10.3390/s21051559 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1559

Author(s):

Thorben Iggena ◽

Eushay Bin Bin Ilyas ◽

Marten Fischer ◽

Ralf Tönjes ◽

Tarek Elsaleh ◽

...

Keyword(s):

Internet Of Things ◽

Rapid Development ◽

Data Sources ◽

The Internet ◽

Privacy And Security ◽

Extensive Evaluation ◽

Data Fragmentation ◽

Independent Design ◽

Domain Independent ◽

The Internet Of Things

Due to the rapid development of the Internet of Things (IoT) and consequently, the availability of more and more IoT data sources, mechanisms for searching and integrating IoT data sources become essential to leverage all relevant data for improving processes and services. This paper presents the IoT search framework IoTCrawler. The IoTCrawler framework is not only another IoT framework, it is a system of systems which connects existing solutions to offer interoperability and to overcome data fragmentation. In addition to its domain-independent design, IoTCrawler features a layered approach, offering solutions for crawling, indexing and searching IoT data sources, while ensuring privacy and security, adaptivity and reliability. The concept is proven by addressing a list of requirements defined for searching the IoT and an extensive evaluation. In addition, real world use cases showcase the applicability of the framework and provide examples of how it can be instantiated for new scenarios.

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