scholarly journals LSTM-Based Path Prediction for Effective Sensor Filtering in Sensor Registry System

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8106
Author(s):  
Haotian Chen ◽  
Sukhoon Lee ◽  
Byung-Won On ◽  
Dongwon Jeong
Keyword(s):  
Monte Carlo ◽  
Internet Of Things ◽  
Service Provision ◽  
Heterogeneous Data ◽  
The Internet ◽  
Evaluation Approach ◽  
Registry System ◽  
Path Prediction ◽  
The Internet Of Things ◽  
Meaningful Data

The Internet of Things (IoT) is expected to provide intelligent services by receiving heterogeneous data from ambient sensors. A mobile device employs a sensor registry system (SRS) to present metadata from ambient sensors, then connects directly for meaningful data. The SRS should provide metadata for sensors that may be successfully connected. This process is location-based and is also known as sensor filtering. In reality, GPS sometimes shows the wrong position and thus leads to a failed connection. We propose a dual collaboration strategy that simultaneously collects GPS readings and predictions from historical trajectories to improve the probability of successful requests between mobile devices and ambient sensors. We also update the evaluation approach of sensor filtering in SRS by introducing a Monte Carlo-based simulation flow to measure the service provision rate. The empirical study shows that the LSTM-based path prediction can compensate for the loss of location abnormalities and is an effective sensor filtering model.

scholarly journals Characteristic Analysis of Multisource Heterogeneous Data in Digital City Planning Based on Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Xiao Xiao
Keyword(s):  
Internet Of Things ◽  
City Planning ◽  
Heterogeneous Data ◽  
Feature Analysis ◽  
The Internet ◽  
Digital City ◽  
Characteristic Analysis ◽  
True Value ◽  
The Relationship ◽  
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.

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

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.

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

2017 ◽  
Vol 256 ◽  
pp. 13-22 ◽  
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

scholarly journals Crop Management with the IoT: An Interdisciplinary Survey

Agronomy ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 181 ◽  
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.

scholarly journals Cloud Trust-Driven Hierarchical Sharing Method of Internet of Things Information Resources

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jianpeng Zhang
Keyword(s):  
Internet Of Things ◽  
Radio Frequency Identification ◽  
Resource Sharing ◽  
Detection Algorithm ◽  
Heterogeneous Data ◽  
Information Resource ◽  
The Internet ◽  
Security Model ◽  
Internet Information ◽  
The Internet Of Things

Short information sharing time is one of the problems to be solved in the traditional Internet. Therefore, this paper proposes a hierarchical simulation of the Internet of Things sharing structure framework that trusts the cloud to drive Internet information resource sharing. By setting thresholds and iterative adjustment parameters, a complementary judgment matrix is constructed to obtain the minimum nonnegative deviation value and the optimal weight vector. This paper sorts according to the value of the Internet information security model, obtains the optimal model to avoid human tampering, and designs the information resource acquisition process to ensure the reliability of the source data. We use radio frequency identification (RFID) equipment in the preprocessing of massive heterogeneous data in the Internet of Things. In a network environment where resources are limited and heterogeneous are fully considered, the trust-based adaptive detection algorithm is used to evaluate the credibility of the trust-driven algorithm for hierarchical information resource sharing services in the cloud environment of the Internet of Things. We propose a cloud trust-driven hierarchical information resource sharing Internet information resource model. Firstly, the key characteristics of hierarchical information resource sharing are analyzed. Then, a hierarchical information resource sharing model was established by using specific constraints, trust steepness function, cloud trust evaluation criteria, and trust constraint coefficient. Finally, an example of IoT system is designed to verify the effectiveness of the model. Experimental results show that, compared with the traditional model or algorithm, this model has a good hierarchical sharing effect of the underlying resource information.

A Kind of Heterogeneous Data Synchronous Design for the Internet of Things

2012 ◽  
Vol 476-478 ◽  
pp. 1392-1398
Author(s):  
Yu Lu ◽  
Jin Ying Wu ◽  
Hong Min Chen ◽  
Yong Pin Zheng ◽  
Yun Ping Wu
Keyword(s):  
Internet Of Things ◽  
Heating System ◽  
Heterogeneous Data ◽  
Data Synchronization ◽  
The Internet ◽  
Synchronization Mechanism ◽  
System Data ◽  
The Stability ◽  
Solar Water Heating System ◽  
The Internet Of Things

The way of heterogeneous data synchronization based on WEB service mode used in the Internet of things is analyzed, the problem of synchronization mechanism of heterogeneous data based on internet web port is solved. Combined with the characteristics of SOA architecture, this paper proposes synchronization model WLDSS (Web-level Data Synchronization System) of distributed heterogeneous data, and gives the design principles and the key strategies of the model. Through the application of distributed solar water heating system of control system data synchronization, it verifies the stability of the WLDSS model.

scholarly journals A Data Processing Middleware Based on SOA for the Internet of Things

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Feng Wang ◽  
Liang Hu ◽  
Jin Zhou ◽  
Kuo Zhao
Keyword(s):  
Internet Of Things ◽  
Data Processing ◽  
Service Oriented Architecture ◽  
Heterogeneous Systems ◽  
Heterogeneous Data ◽  
Sensor Data ◽  
The Internet ◽  
Heterogeneous Information ◽  
New Research ◽  
The Internet Of Things

The Internet of Things (IoT) emphasizes on connecting every object around us by leveraging a variety of wireless communication technologies. Heterogeneous data fusion is widely considered to be a promising and urgent challenge in the data processing of the IoT. In this study, we first discuss the development of the concept of the IoT and give a detailed description of the architecture of the IoT. And then we design a middleware platform based on service-oriented architecture (SOA) for integration of multisource heterogeneous information. New research angle regarding flexible heterogeneous information fusion architecture for the IoT is the theme of this paper. Experiments using environmental monitoring sensor data derived from indoor environment are performed for system validation. Through the theoretical analysis and experimental verification, the data processing middleware architecture represents better adaptation to multisensor and multistream application scenarios in the IoT, which improves heterogeneous data utilization value. The data processing middleware based on SOA for the IoT establishes a solid foundation of integration and interaction for diverse networks data among heterogeneous systems in the future, which simplifies the complexity of integration process and improves reusability of components in the system.

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