Disaster Management Using Internet of Things

2019 ◽  
pp. 211-222 ◽  
Author(s):  
Meghna Sharma ◽  
Jagdeep Kaur
Keyword(s):  
Real Time ◽  
Time Data ◽  
Continuous Increase ◽  
The Real ◽  
Robotic Exploration ◽  
Real Time Data ◽  
Hazardous Gases ◽  
Definition Of ◽  
Harmful Effects

The problem of hazard detection and the robotic exploration of the hazardous environment is the need of the of the hour due to the continuous increase of the hazardous gases owing to the industry proliferation and modernization of the infrastructure. It includes radiological materials and toxic gases with long term harmful effects. The definition of a hazardous environment and extracting the parameters for the same is itself a complicated task. The chapter proposes the alarming solution to warn about the level of hazardous effects for a particular environment area. The need of the hour is to build complete systems that can autosense the hazardous environment even in low visibility environment and raise an alarm. The combination of IoT and machine learning can be best used for getting the real-time data and using the real-time data for analyzing the accurate current hazardous level as well as prediction of future hazards by reading the parameters for detection and also selecting the useful parameters from them.

scholarly journals The Importance of Implementing Cyber Physical Systems to Acquire Real-Time Data and Indicators

J ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 147-153
Author(s):  
Paula Morella ◽  
María Pilar Lambán ◽  
Jesús Antonio Royo ◽  
Juan Carlos Sánchez
Keyword(s):  
Decision Making ◽  
Data Acquisition ◽  
Real Time ◽  
Failure Detection ◽  
Cyber Physical Systems ◽  
Future Research ◽  
Time Data ◽  
Physical Systems ◽  
The Real ◽  
Real Time Data

Among the new trends in technology that have emerged through the Industry 4.0, Cyber Physical Systems (CPS) and Internet of Things (IoT) are crucial for the real-time data acquisition. This data acquisition, together with its transformation in valuable information, are indispensable for the development of real-time indicators. Moreover, real-time indicators provide companies with a competitive advantage over the competition since they enhance the calculus and speed up the decision-making and failure detection. Our research highlights the advantages of real-time data acquisition for supply chains, developing indicators that would be impossible to achieve with traditional systems, improving the accuracy of the existing ones and enhancing the real-time decision-making. Moreover, it brings out the importance of integrating technologies 4.0 in industry, in this case, CPS and IoT, and establishes the main points for a future research agenda of this topic.

Intensifiers on Tyneside

2010 ◽  
Vol 31 (3) ◽  
pp. 252-287 ◽  
Author(s):  
Katie Barnfield ◽  
Isabelle Buchstaller
Keyword(s):  
Real Time ◽  
Longitudinal Analysis ◽  
Developmental Trajectories ◽  
Time Data ◽  
Longitudinal Changes ◽  
Real Time Data ◽  
Quantitative Development ◽  
First Time

We report on longitudinal changes in the system of intensification in an innovative corpus that spans five decades of dialectal speech. Our analyses allow us — for the first time in a British context — to trace the quantitative development in the variable across four generations. Longitudinal analysis across real and apparent time determines the effect of extralinguistic and intralinguistic variables on intensification in Tyneside and tests to what extent real time data corroborates trends reported from previous apparent time analyses. Long-term competition within the variable manifests itself in distinctive developmental trajectories: expansion — both proportionally within the variable as well as across adjectival categories — tends to follow one of three types of patterns, exemplified, respectively, by really, so and dead. Variant retraction, however, follows only one schema. Importantly, numerical decline in the system does not necessarily go hand in hand with a reduction in breadth of application.

scholarly journals Smart carbon monitoring platform under IoT-Cloud architecture for small cities in B5G

2021 ◽  
Author(s):  
He Zhang ◽  
Jianxun Zhang ◽  
Rui Wang ◽  
Yazhe Huang ◽  
Mengxiao Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Carbon Emission ◽  
Emission Measurement ◽  
Low Carbon ◽  
Time Data ◽  
Small Cities ◽  
Real Time Data ◽  
Monitoring Platform ◽  
Term Data

AbstractWith the rapid development of the Internet of Things (IoT) in the 5G age, the construction of smart cities around the world consequents on the exploration of carbon reduction path based on IoT technology is an important direction for global low carbon city research. Carbon dioxide emissions in small cities are usually higher than that in large and medium cities. However, due to the huge difference in data environment between small cities and Medium-large sized cities, the weak hardware foundation of the IoT, and the high input cost, the construction of a small city smart carbon monitoring platform has not yet been carried out. This paper proposes a real-time estimate model of carbon emissions at the block and street scale and designs a smart carbon monitoring platform that combines traditional carbon control methods with IoT technology. It can exist long-term data by using real-time data acquired with the sensing device. Therefore, the dynamic monitoring and management of low-carbon development in small cities can be achieved. The contributions are summarized as follows: (1) Intelligent thermoelectric systems, industrial energy monitoring systems, and intelligent transportation systems are three core systems of the monitoring platform. Carbon emission measurement methods based on sample monitoring, long-term data, and real-time data have been established, they can solve the problem of the high cost of IoT equipment in small cities. (2) Combined with long-term data, the real-time correction technology, they can dispose of the matter of differences in carbon emission measurement under diverse scales.

Customer behavior analysis using real-time data processing

2019 ◽  
Vol 31 (1) ◽  
pp. 265-290 ◽  
Author(s):  
Ganjar Alfian ◽  
Muhammad Fazal Ijaz ◽  
Muhammad Syafrudin ◽  
M. Alex Syaekhoni ◽  
Norma Latif Fitriyani ◽  
...  
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Association Rule ◽  
Customer Behavior ◽  
Time Data ◽  
Content Type ◽  
The Real ◽  
Real Time Data ◽  
Real Time Data Processing ◽  
Big Data Technology

PurposeThe purpose of this paper is to propose customer behavior analysis based on real-time data processing and association rule for digital signage-based online store (DSOS). The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is utilized to handle the vast amount of customer behavior data.Design/methodology/approachIn order to extract customer behavior patterns, customers’ browsing history and transactional data from digital signage (DS) could be used as the input for decision making. First, the authors developed a DSOS and installed it in different locations, so that customers could have the experience of browsing and buying a product. Second, the real-time data processing system gathered customers’ browsing history and transaction data as it occurred. In addition, the authors utilized the association rule to extract useful information from customer behavior, so it may be used by the managers to efficiently enhance the service quality.FindingsFirst, as the number of customers and DS increases, the proposed system was capable of processing a gigantic amount of input data conveniently. Second, the data set showed that as the number of visit and shopping duration increases, the chance of products being purchased also increased. Third, by combining purchasing and browsing data from customers, the association rules from the frequent transaction pattern were achieved. Thus, the products will have a high possibility to be purchased if they are used as recommendations.Research limitations/implicationsThis research empirically supports the theory of association rule that frequent patterns, correlations or causal relationship found in various kinds of databases. The scope of the present study is limited to DSOS, although the findings can be interpreted and generalized in a global business scenario.Practical implicationsThe proposed system is expected to help management in taking decisions such as improving the layout of the DS and providing better product suggestions to the customer.Social implicationsThe proposed system may be utilized to promote green products to the customer, having a positive impact on sustainability.Originality/valueThe key novelty of the present study lies in system development based on big data technology to handle the enormous amounts of data as well as analyzing the customer behavior in real time in the DSOS. The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is used to handle the vast amount of customer behavior data. In addition, the present study proposed association rule to extract useful information from customer behavior. These results can be used for promotion as well as relevant product recommendations to DSOS customers. Besides in today’s changing retail environment, analyzing the customer behavior in real time in DSOS helps to attract and retain customers more efficiently and effectively, and retailers can get a competitive advantage over their competitors.

Cassandra-based data repository design for food supply chain traceability

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sandeep Kumar Singh ◽  
Mamata Jenamani
Keyword(s):  
Supply Chain ◽  
Real Time ◽  
Radio Frequency Identification ◽  
Query Language ◽  
Data Repository ◽  
Time Data ◽  
Content Type ◽  
The Real ◽  
Traceability System ◽  
Real Time Data

Purpose The purpose of this paper is to design a supply chain database schema for Cassandra to store real-time data generated by Radio Frequency IDentification technology in a traceability system. Design/methodology/approach The real-time data generated in such traceability systems are of high frequency and volume, making it difficult to handle by traditional relational database technologies. To overcome this difficulty, a NoSQL database repository based on Casandra is proposed. The efficacy of the proposed schema is compared with two such databases, document-based MongoDB and column family-based Cassandra, which are suitable for storing traceability data. Findings The proposed Cassandra-based data repository outperforms the traditional Structured Query Language-based and MongoDB system from the literature in terms of concurrent reading, and works at par with respect to writing and updating of tracing queries. Originality/value The proposed schema is able to store the real-time data generated in a supply chain with low latency. To test the performance of the Cassandra-based data repository, a test-bed is designed in the lab and supply chain operations of Indian Public Distribution System are simulated to generate data.

scholarly journals Aesthetic Archives

2016 ◽  
Vol 41 (1) ◽  
pp. 11-23
Author(s):  
Michael Takeo Magruder ◽  
Jeremy Pilcher
Keyword(s):  
New Media ◽  
Real Time ◽  
Emerging Technologies ◽  
Distributed Data ◽  
Time Data ◽  
The Real ◽  
New Media Art ◽  
Data Flows ◽  
Real Time Data ◽  
Visual Artist

Michael Takeo Magruder, visual artist and researcher, discusses his digital and new media art and practice with Jeremy Pilcher, lawyer and academic, whose research engages with the intersection of art and law. Takeo's work asks viewers to question their relationship both to and within the real-time data flows generated by emerging technologies and the implications these have for archives. His art concerns the way institutions use such systems to create narratives that structure societies. This conversation discusses how Takeo's practice invites us, as individuals, to critically reflect on the implications of the stories that are both told to and about us by using gathered and distributed data.

The Application of Real-Time Monitor System Based on 3D Visualization Technology

2013 ◽  
Vol 373-375 ◽  
pp. 888-891
Author(s):  
Fang Liu ◽  
Wei Tong ◽  
Zhi Jun Qian ◽  
Yu Hong Dong
Keyword(s):  
Real Time ◽  
Virtual Environment ◽  
3D Visualization ◽  
Laboratory Model ◽  
Monitor System ◽  
Time Data ◽  
Virtual Scene ◽  
The Real ◽  
Real Time Data ◽  
Visualization Technology

This paper introduced the laboratory model of Real-time monitor system based on the 3D Visualization for calefaction furnace, depicted the process of the model.In this paper we created a virtual environment and transport the real-time data which we collected from the locale to the virtual scene,to realize the real time monitor on the real environment.Through simulating in the lab,the effect of this system was realistic at the same time it arrived at the goal of better monitoring with better real-time.

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