scholarly journals An Innovative Industry 4.0 Cloud Data Transfer Method for an Automated Waste Collection System

2020 ◽  
Vol 12 (5) ◽  
pp. 1839 ◽  
Author(s):  
Costel Emil Cotet ◽  
Gicu Calin Deac ◽  
Crina Narcisa Deac ◽  
Cicerone Laurentiu Popa
Keyword(s):  
Industry 4.0 ◽  
Data Transfer ◽  
Waste Collection ◽  
Collection System ◽  
Time Data ◽  
Cloud Data ◽  
Data Encoding ◽  
Big Data Applications ◽  
Transfer Method ◽  
Cloud Applications

Moving to Industry 4.0 involves the collection of massive amounts of data and the development of big data applications that can ensure a quick data flow between different systems, including massive amounts of data and information collection from smart sensors, and sending them to cloud applications that allow real-time data monitoring and processing. Securing and protecting the transmitted data represents a big issue to be discussed and resolved. In the paper, we propose a new method of data encoding and encryption for cloud applications using PNG format images. The proposed method is described in comparison with one of the classical methods of data encoding and transmission used currently. The paper includes a case study in which the proposed method was used to collect and transmit data from an automated waste collection system. The results show that the proposed method represents a secure, fast and efficient way to send and store the data in the cloud compared to the methods currently used. The proposed method is not limited to being used only in waste management but can be used successfully for any type of manufacturing system from smart factories.

Presenting an Innovative Process Improvement Method for the Efficient Forklift Material Handling Systems in the Industry 4.0

2020 ◽  
Vol 14 (1) ◽  
pp. 32-38
Author(s):  
Róbert Skapinyecz ◽  
Béla Illés ◽  
Tamás Bányai ◽  
Umetaliev Akylbek ◽  
Ibolya Hardai ◽  
...  
Keyword(s):  
Process Improvement ◽  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Material Handling ◽  
Data Transfer ◽  
Manufacturing Sector ◽  
Manufacturing Companies ◽  
Time Data ◽  
Material Handling Systems ◽  
Improvement Method

The rapid adoption of Industry 4.0 principles in the manufacturing sector during the last couple of years has created numerous possibilities for development. One key area related to manufacturing in which Industry 4.0 solutions can have a significant impact is the field of materials handling. However, today the majority of manufacturing companies still overly rely on the utilization of standard forklift material handling systems for supporting their operations. In this paper, our goal is to present a novel process improvement method utilizing Industry 4.0 principles which could significantly aid in the design of efficient forklift material handling systems. We believe that by utilizing the opportunities inherent in Industry 4.0 (in this case mainly sensor systems, big data analysis, digitalization and real-time data transfer), forklift based material handling can be elevated to an entirely new level in terms of efficiency, which could greatly improve the overall performance of the vast majority of manufacturing systems.

scholarly journals Smarter Open Government Data for Society 5.0: Are Your Open Data Smart Enough?

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5204
Author(s):  
Anastasija Nikiforova
Keyword(s):  
Industry 4.0 ◽  
Economic Value ◽  
Open Data ◽  
Digital Data ◽  
Open Government ◽  
Data Sets ◽  
Time Data ◽  
Open Government Data ◽  
Information And Communication ◽  
Government Data

Nowadays, governments launch open government data (OGD) portals that provide data that can be accessed and used by everyone for their own needs. Although the potential economic value of open (government) data is assessed in millions and billions, not all open data are reused. Moreover, the open (government) data initiative as well as users’ intent for open (government) data are changing continuously and today, in line with IoT and smart city trends, real-time data and sensor-generated data have higher interest for users. These “smarter” open (government) data are also considered to be one of the crucial drivers for the sustainable economy, and might have an impact on information and communication technology (ICT) innovation and become a creativity bridge in developing a new ecosystem in Industry 4.0 and Society 5.0. The paper inspects OGD portals of 60 countries in order to understand the correspondence of their content to the Society 5.0 expectations. The paper provides a report on how much countries provide these data, focusing on some open (government) data success facilitating factors for both the portal in general and data sets of interest in particular. The presence of “smarter” data, their level of accessibility, availability, currency and timeliness, as well as support for users, are analyzed. The list of most competitive countries by data category are provided. This makes it possible to understand which OGD portals react to users’ needs, Industry 4.0 and Society 5.0 request the opening and updating of data for their further potential reuse, which is essential in the digital data-driven world.

How Industry 4.0 Can Benefit From Semantic Web Technologies and Artefacts

2021 ◽  
Vol 13 (4) ◽  
pp. 64-74
Author(s):  
Leila Zemmouchi-Ghomari
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Semantic Web ◽  
Industry 4.0 ◽  
The Internet ◽  
Time Data ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Real Time Data ◽  
The Internet Of Things

Industry 4.0 is a technology-driven manufacturing process that heavily relies on technologies, such as the internet of things (IoT), cloud computing, web services, and big real-time data. Industry 4.0 has significant potential if the challenges currently being faced by introducing these technologies are effectively addressed. Some of these challenges consist of deficiencies in terms of interoperability and standardization. Semantic Web technologies can provide useful solutions for several problems in this new industrial era, such as systems integration and consistency checks of data processing and equipment assemblies and connections. This paper discusses what contribution the Semantic Web can make to Industry 4.0.

scholarly journals DynDL: Scheduling Data-Locality-Aware Tasks with Dynamic Data Transfer Cost for Multicore-Server-Based Big Data Clusters

2018 ◽  
Vol 8 (11) ◽  
pp. 2216
Author(s):  
Jiahui Jin ◽  
Qi An ◽  
Wei Zhou ◽  
Jiakai Tang ◽  
Runqun Xiong
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Processing Time ◽  
Data Transfer ◽  
Data Locality ◽  
Free Time ◽  
Time Data ◽  
Dynamic Data ◽  
Network Bandwidth ◽  
Transfer Cost

Network bandwidth is a scarce resource in big data environments, so data locality is a fundamental problem for data-parallel frameworks such as Hadoop and Spark. This problem is exacerbated in multicore server-based clusters, where multiple tasks running on the same server compete for the server’s network bandwidth. Existing approaches solve this problem by scheduling computational tasks near the input data and considering the server’s free time, data placements, and data transfer costs. However, such approaches usually set identical values for data transfer costs, even though a multicore server’s data transfer cost increases with the number of data-remote tasks. Eventually, this hampers data-processing time, by minimizing it ineffectively. As a solution, we propose DynDL (Dynamic Data Locality), a novel data-locality-aware task-scheduling model that handles dynamic data transfer costs for multicore servers. DynDL offers greater flexibility than existing approaches by using a set of non-decreasing functions to evaluate dynamic data transfer costs. We also propose online and offline algorithms (based on DynDL) that minimize data-processing time and adaptively adjust data locality. Although DynDL is NP-complete (nondeterministic polynomial-complete), we prove that the offline algorithm runs in quadratic time and generates optimal results for DynDL’s specific uses. Using a series of simulations and real-world executions, we show that our algorithms are 30% better than algorithms that do not consider dynamic data transfer costs in terms of data-processing time. Moreover, they can adaptively adjust data localities based on the server’s free time, data placement, and network bandwidth, and schedule tens of thousands of tasks within subseconds or seconds.

scholarly journals Optimization of Household Waste Collection Routes Using GIS: Case Study El Bousten district, commune of Sfax, Tunisia

2017 ◽  
Vol 12 (1) ◽  
pp. 53-60 ◽  
Author(s):  
Rim Sallem ◽  
Mohamed Rouis
Keyword(s):  
Information System ◽  
Geographical Information System ◽  
Optimization Procedure ◽  
Geographical Information ◽  
Household Waste ◽  
Environmental Costs ◽  
Waste Collection ◽  
Collection System ◽  
Network Analyst

This paper presents a method for optimizing the household waste collection system supported by Geographical Information System (GIS) tool for the sector 1of district El Bousten of Sfax commune, Tunisia. The ArcGIS Network Analyst based model is applied for the purpose of improving the collection process effectiveness, namely, the household collection bins’ reallocation along with the vehicles’ tour optimization procedure in terms of distance and time. Results indicated a reduction of 25, 83% in route and 21, 5 % in the time spent of collection along with fuel consumption savings. These findings show that GIS based model tends to exhibit significant improvements as to the collection and transportation system, therefore, to its economical and environmental costs.

Optimization of a Recyclable Waste Collection System - The Valorsul Case Study

2015 ◽  
pp. 97-105 ◽  
Author(s):  
Diogo Lopes ◽  
Tânia Rodrigues Pereira Ramos ◽  
Ana Paula Barbosa-Póvoa
Keyword(s):  
Waste Collection ◽  
Collection System
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