scholarly journals Internet of Things (IoT) for digital concrete quality control (DCQC): A conceptual framework

2019 ◽  
Author(s):  
Arka Ghosh ◽  
M. Reza Hosseini ◽  
Riyadh Al-Ameri ◽  
Gintaris Kaklauskas ◽  
Bahareh Nikmehr
Keyword(s):  
Quality Control ◽  
Internet Of Things ◽  
Conceptual Framework ◽  
Real Time ◽  
Progress Monitoring ◽  
Predictive Analytics ◽  
Time Data ◽  
Ongoing Research ◽  
Concrete Members ◽  
Concrete Quality

Concreting is generally a manual, labour intensive and time-consuming process, putting additional burden on constrained resources. Current practices of concreting are wasteful, non-sustainable and end products usually lack proper quality conformance. This paper, as the first outcome of an ongoing research project, proposes concrete as an area ripe for being disrupted by new technological developments and the wave of automation. It puts forward arguments to show that The Internet of Things (IoT), as an emerging concept, has the potential to revolutionize concreting operations, resulting in substantial time savings, confidence in its durability and enhanced quality conformance. A conceptual framework for a digital concrete quality control (DCQC) drawing upon IoT is outlined; DCQC facilitates automated lifecycle monitoring of concrete, controlled by real-time monitoring of parameters like surface humidity, temperature variance, moisture content, vibration level, and crack occurrence and propagation of concrete members through embedded sensors. Drawing upon an analytical approach, discussions provide evidence for the advantages of adopting DCQC. The proposed system is of particular appeal for practitioners, as a workable solution for reducing water, energy consumption and required man-hours for concreting procedures, as well as, providing an interface for access to real-time data, site progress monitoring, benchmarking, and predictive analytics purposes.

scholarly journals Diagnostics and Prognostics of Energy Conversion Processes via Knowledge-Based Systems

Proceedings ◽  
2020 ◽  
Vol 58 (1) ◽  
pp. 1
Author(s):  
Roberto Melli ◽  
Enrico Sciubba
Keyword(s):  
Expert System ◽  
Knowledge Base ◽  
Real Time ◽  
System Analysis ◽  
Intelligent System ◽  
Time Derivative ◽  
Time History ◽  
Control Procedure ◽  
Time Data ◽  
Ongoing Research

This paper presents a critical and analytical description of an ongoing research program aimed at the implementation of an expert system capable of monitoring, through an Intelligent Health Control procedure, the instantaneous performance of a cogeneration plant. The expert system is implemented in the CLIPS environment and is denominated PROMISA as the acronym for Prognostic Module for Intelligent System Analysis. It generates, in real time and in a form directly useful to the plant manager, information on the existence and severity of faults, forecasts on the future time history of both detected and likely faults, and suggestions on how to control the problem. The expert procedure, working where and if necessary with the support of a process simulator, derives from the available real-time data a list of selected performance indicators for each plant component. For a set of faults, pre-defined with the help of the plant operator (Domain Expert), proper rules are defined in order to establish whether the component is working correctly; in several instances, since one single failure (symptom) can originate from more than one fault (cause), complex sets of rules expressing the combination of multiple indices have been introduced in the knowledge base as well. Creeping faults are detected by analyzing the trend of the variation of an indicator over a pre-assigned interval of time. Whenever the value of this ‘‘discrete time derivative’’ becomes ‘‘high’’ with respect to a specified limit value, a ‘‘latent creeping fault’’ condition is prognosticated. The expert system architecture is based on an object-oriented paradigm. The knowledge base (facts and rules) is clustered—the chunks of knowledge pertain to individual components. A graphic user interface (GUI) allows the user to interrogate PROMISA about its rules, procedures, classes and objects, and about its inference path. The paper also presents the results of some simulation tests.

Sectional Data Acquisition and Control System for Solar Street Lamps Based on Internet of Things

2013 ◽  
Vol 773 ◽  
pp. 148-153 ◽  
Author(s):  
Juan Zhou ◽  
Bing Yan Chen ◽  
Meng Ni Zhang ◽  
Ying Ying Tang
Keyword(s):  
Control System ◽  
Internet Of Things ◽  
Data Acquisition ◽  
Real Time ◽  
Network Flow ◽  
Short Interval ◽  
Management Problem ◽  
Time Data ◽  
Control Centre ◽  
And Control

Aiming at the management problem of real-time data created while intelligent solar street lamps working, sectional data acquisition and control system based on internet of things is introduced in the paper. Communication protocol with unified form and flexible function is designed in the system, and communication address is composed of sectional address and subsection address. Three-level data structure is built in the polling algorithm to trace real-time state of lamps and to detect malfunction in time, which is suitable for sectional lamps management characteristics. The system reflects necessary statistic data and exception information to remote control centre through GPRS to short interval expend on transmission and procession and save network flow and system energy. The result shows the system brings improved management affection and accords with the idea of energy-saving and environmental protection.

Trends and Technologies in Big Data Processing

2020 ◽  
pp. 17-42
Author(s):  
Amitava Choudhury ◽  
Kalpana Rangra
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Data Processing ◽  
Real Time ◽  
Computing Technology ◽  
Time Data ◽  
Big Data Processing ◽  
Real Time Data ◽  
Real Time Data Processing

Data type and amount in human society is growing at an amazing speed, which is caused by emerging new services such as cloud computing, internet of things, and location-based services. The era of big data has arrived. As data has been a fundamental resource, how to manage and utilize big data better has attracted much attention. Especially with the development of the internet of things, how to process a large amount of real-time data has become a great challenge in research and applications. Recently, cloud computing technology has attracted much attention to high performance, but how to use cloud computing technology for large-scale real-time data processing has not been studied. In this chapter, various big data processing techniques are discussed.

Adaptive Modeling and Dynamic Targeting for Real Time Analytics in Mobile Advertising

2017 ◽  
Vol 7 (2) ◽  
pp. 24-39 ◽  
Author(s):  
Donald Kridel ◽  
Dan Dolk ◽  
David Castillo
Keyword(s):  
Real Time ◽  
Predictive Analytics ◽  
User Behavior ◽  
Model Management ◽  
Mobile Marketing ◽  
Time Data ◽  
Adaptive Modeling ◽  
Very Large Databases ◽  
Customer Profiling ◽  
Modeling Strategy

Mobile marketing campaigns are now largely deployed through demand side platforms (DSPs) who provide dynamic customer targeting and a performance-intensive real-time bidding (RTB) version of predictive analytics as a service. Matching users with the campaigns they are most likely to engage with in extreme real-time environments requires adaptive model management, advanced parallel processing hardware/software, and the integration of multiple very large databases. The authors present (1) an adaptive modeling strategy to satisfy the performance thresholds of 40 to 100ms for DSPs to decide whether and how much to bid for a potential client to receive a particular advertisement via their mobile device. (2) a dynamic customer profiling technique to map mobile devices to specific lattices (geographic locations), and to track user behavior via device-histories. In this “big data” decision environment, analytic model management is automated via model feedback loops which adjust the models dynamically as real-time data streams in.

A Block Chain Approach for Privacy Preserving and Data Security in Internet of Things

2020 ◽  
Vol 17 (9) ◽  
pp. 3979-3982
Author(s):  
N. Harish Kumar ◽  
G. Deepak
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Data Security ◽  
Cloud Storage ◽  
Storage System ◽  
Information Storage ◽  
Entire System ◽  
Time Data ◽  
Real Time Data ◽  
Block Chain

Internet of Things has been increasing its usage and recognition in vast sectors like Defence, Business, Industries, and Hospitals. The data disruption is strictly unacceptable in a number of these sectors because it could end up in serious Loss or Damages to the entire system. As of now, IOT is using a central cloud storage system for information storage and transactions. However, some examples already verified that Central cloud storage information might be hacked and changed by the specialists. This paper presents an IoT system having localized block chain storage which works on real time data and manipulates with narrowness of data interruption and modification and its recovery.

scholarly journals Internet of Things based Customer Relationship Management – A Research Perspective

2018 ◽  
Vol 7 (2.7) ◽  
pp. 444 ◽  
Author(s):  
Samir Yerpude ◽  
Dr Tarun Kumar Singhal
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Mobile Internet ◽  
Customer Relationship ◽  
Time Data ◽  
Crm System ◽  
Real Time Data ◽  
The Impact

Objectives: To study the impact of Internet of things (IoT) on the Customer Relationship Management process and evaluate the benefits in terms of customer satisfaction and customer retention. Methods: An extensive literature review was conducting wherein the constructs of CRM and IoT are studied. Various preliminary information on IoT and CRM system along with the components of Digital enablers have been evaluated. References from research papers, journals, Internet sites, statistical data sites and books were used to collate the relevant content on the subject. The study of all the relevant scenarios where there is a possible impact of IoT origin real time data on CRM was undertaken. Findings: Customer demands are continuously evolving and it is very relevant for all the organizations to align and keep pace with the change. Organizations need to be customer centric and agile to the changing market scenarios. Evaluation of the trends in mobile internet vs desktop internet was also conducted to validate the findings. Application: The usage of real time data emerging out of the IoT landscape has become a reality with the data transmitted over the Internet and consumed by the CRM system. It improves the control on the customer relationship function helping the organizations to operate within healthy and sustained profit  

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