scholarly journals A Roadmap to Integrate Digital Twins for Small and Medium-Sized Enterprises

2021 ◽  
Vol 11 (20) ◽  
pp. 9479
Author(s):  
Alim Yasin ◽  
Toh Yen Pang ◽  
Chi-Tsun Cheng ◽  
Miro Miletic
Keyword(s):  
Data Storage ◽  
Cyber Security ◽  
3D Model ◽  
Low Cost ◽  
Sensor Data ◽  
Time Data ◽  
Communication Framework ◽  
Temperature Detector ◽  
Physical Asset ◽  
Iot Devices

In the last decade, Australian SMEs are steadily becoming more digitally engaged, but they still face issues and barriers to fully adopt Industry 4.0 (I4.0). Among the tools that I4.0 encompasses, digital twin (DT) and digital thread (DTH) technologies hold significant interest and value. Some of the challenges are the lack of expertise in developing the communication framework required for data collection, processing, and storing; concerns about data and cyber security; lack of knowledge of the digitization and visualisation of data; and value generation for businesses from the data. This article aims to demonstrate the feasibility of DT implementation for small and medium-sized enterprises (SMEs) by developing a framework based on simple and low-cost solutions and providing insight and guidance to overcome technological barriers. To do so, this paper first outlines the theoretical framework and its components, and subsequently discusses a simplified and generalised DT model of a real-world physical asset that demonstrates how these components function, how they are integrated and how they interact with each other. An experimental scenario is presented to transform data harvested from a resistance temperature detector sensor connected with a WAGO 750-8102 Programmable Logic Controller for data storage and analysis, predictive simulation and modelling. Our results demonstrate that sensor data could be readily integrated from Internet-of-Things (IoT) devices and enabling DT technologies, where users could view real time data and key performance indicators (KPIs) in the form of a 3D model. Data from both the sensor and 3D model are viewable in a comprehensive history log through a database. Via this technological demonstration, we provide several recommendations on software, hardware, and expertise that SMEs may adopt to assist with their DT implementations.

scholarly journals BIM and IoT Sensors Integration: A Framework for Consumption and Indoor Conditions Data Monitoring of Existing Buildings

2021 ◽  
Vol 13 (8) ◽  
pp. 4496
Author(s):  
Giuseppe Desogus ◽  
Emanuela Quaquero ◽  
Giulia Rubiu ◽  
Gianluca Gatto ◽  
Cristian Perra
Keyword(s):  
Energy Consumption ◽  
Low Cost ◽  
Application Programming Interface ◽  
Visual Programming ◽  
Information Modeling ◽  
Sensor Data ◽  
Time Data ◽  
Existing Building ◽  
Building Maintenance ◽  
Indoor Conditions

The low accessibility to the information regarding buildings current performances causes deep difficulties in planning appropriate interventions. Internet of Things (IoT) sensors make available a high quantity of data on energy consumptions and indoor conditions of an existing building that can drive the choice of energy retrofit interventions. Moreover, the current developments in the topic of the digital twin are leading the diffusion of Building Information Modeling (BIM) methods and tools that can provide valid support to manage all data and information for the retrofit process. This paper shows the aim and the findings of research focused on testing the integrated use of BIM methodology and IoT systems. A common data platform for the visualization of building indoor conditions (e.g., temperature, luminance etc.) and of energy consumption parameters was carried out. This platform, tested on a case study located in Italy, is developed with the integration of low-cost IoT sensors and the Revit model. To obtain a dynamic and automated exchange of data between the sensors and the BIM model, the Revit software was integrated with the Dynamo visual programming platform and with a specific Application Programming Interface (API). It is an easy and straightforward tool that can provide building managers with real-time data and information about the energy consumption and the indoor conditions of buildings, but also allows for viewing of the historical sensor data table and creating graphical historical sensor data. Furthermore, the BIM model allows the management of other useful information about the building, such as dimensional data, functions, characteristics of the components of the building, maintenance status etc., which are essential for a much more conscious, effective and accurate management of the building and for defining the most suitable retrofit scenarios.

scholarly journals Semantic Traffic Sensor Data: The TRAFAIR Experience

2020 ◽  
Vol 10 (17) ◽  
pp. 5882
Author(s):  
Federico Desimoni ◽  
Sergio Ilarri ◽  
Laura Po ◽  
Federica Rollo ◽  
Raquel Trillo-Lado
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Smart Cities ◽  
Low Cost ◽  
Semantic Annotation ◽  
Open Data ◽  
Transportation Systems ◽  
Sensor Data ◽  
Time Data ◽  
Future Evolution

Modern cities face pressing problems with transportation systems including, but not limited to, traffic congestion, safety, health, and pollution. To tackle them, public administrations have implemented roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. In the case of traffic sensor data not only the real-time data are essential, but also historical values need to be preserved and published. When real-time and historical data of smart cities become available, everyone can join an evidence-based debate on the city’s future evolution. The TRAFAIR (Understanding Traffic Flows to Improve Air Quality) project seeks to understand how traffic affects urban air quality. The project develops a platform to provide real-time and predicted values on air quality in several cities in Europe, encompassing tasks such as the deployment of low-cost air quality sensors, data collection and integration, modeling and prediction, the publication of open data, and the development of applications for end-users and public administrations. This paper explicitly focuses on the modeling and semantic annotation of traffic data. We present the tools and techniques used in the project and validate our strategies for data modeling and its semantic enrichment over two cities: Modena (Italy) and Zaragoza (Spain). An experimental evaluation shows that our approach to publish Linked Data is effective.

scholarly journals DNS/DANE Collision-Based Distributed and Dynamic Authentication for Microservices in IoT †

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3292 ◽  
Author(s):  
Daniel Díaz-Sánchez ◽  
Andrés Marín-Lopez ◽  
Florina Almenárez Mendoza ◽  
Patricia Arias Cabarcos
Keyword(s):  
Low Cost ◽  
Fog Computing ◽  
Time Data ◽  
Property A ◽  
Domain Name ◽  
Core Networks ◽  
Secure Information ◽  
Nomadic Users ◽  
Iot Devices ◽  
Trapdoor Function

IoT devices provide real-time data to a rich ecosystem of services and applications. The volume of data and the involved subscribe/notify signaling will likely become a challenge also for access and core networks. To alleviate the core of the network, other technologies like fog computing can be used. On the security side, designers of IoT low-cost devices and applications often reuse old versions of development frameworks and software components that contain vulnerabilities. Many server applications today are designed using microservice architectures where components are easier to update. Thus, IoT can benefit from deploying microservices in the fog as it offers the required flexibility for the main players of ubiquitous computing: nomadic users. In such deployments, IoT devices need the dynamic instantiation of microservices. IoT microservices require certificates so they can be accessed securely. Thus, every microservice instance may require a newly-created domain name and a certificate. The DNS-based Authentication of Named Entities (DANE) extension to Domain Name System Security Extensions (DNSSEC) allows linking a certificate to a given domain name. Thus, the combination of DNSSEC and DANE provides microservices’ clients with secure information regarding the domain name, IP address, and server certificate of a given microservice. However, IoT microservices may be short-lived since devices can move from one local fog to another, forcing DNSSEC servers to sign zones whenever new changes occur. Considering DNSSEC and DANE were designed to cope with static services, coping with IoT dynamic microservice instantiation can throttle the scalability in the fog. To overcome this limitation, this article proposes a solution that modifies the DNSSEC/DANE signature mechanism using chameleon signatures and defining a new soft delegation scheme. Chameleon signatures are signatures computed over a chameleon hash, which have a property: a secret trapdoor function can be used to compute collisions to the hash. Since the hash is maintained, the signature does not have to be computed again. In the soft delegation schema, DNS servers obtain a trapdoor that allows performing changes in a constrained zone without affecting normal DNS operation. In this way, a server can receive this soft delegation and modify the DNS zone to cope with frequent changes such as microservice dynamic instantiation. Changes in the soft delegated zone are much faster and do not require the intervention of the DNS primary servers of the zone.

scholarly journals Analysis of Single Board Architectures Integrating Sensors Technologies

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6303
Author(s):  
José Luis Álvarez ◽  
Juan Daniel Mozo ◽  
Eladio Durán
Keyword(s):  
Data Storage ◽  
Low Cost ◽  
Communication Technologies ◽  
Ease Of Use ◽  
Science Technology ◽  
Starting Point ◽  
Testing And Measurement ◽  
Do It Yourself ◽  
Iot Devices ◽  
And Mathematics

Development boards, Single-Board Computers (SBCs) and Single-Board Microcontrollers (SBMs) integrating sensors and communication technologies have become a very popular and interesting solution in the last decade. They are of interest for their simplicity, versatility, adaptability, ease of use and prototyping, which allow them to serve as a starting point for projects and as reference for all kinds of designs. In this sense, there are innumerable applications integrating sensors and communication technologies where they are increasingly used, including robotics, domotics, testing and measurement, Do-It-Yourself (DIY) projects, Internet of Things (IoT) devices in the home or workplace and science, technology, engineering, educational and also academic world for STEAM (Science, Technology, Engineering and Mathematics) skills. The interest in single-board architectures and their applications have caused that all electronics manufacturers currently develop low-cost single board platform solutions. In this paper we realized an analysis of the most important topics related with single-board architectures integrating sensors. We analyze the most popular platforms based on characteristics as: cost, processing capacity, integrated processing technology and open-source license, as well as power consumption (mA@V), reliability (%), programming flexibility, support availability and electronics utilities. For evaluation, an experimental framework has been designed and implemented with six sensors (temperature, humidity, CO2/TVOC, pressure, ambient light and CO) and different data storage and monitoring options: locally on a μSD (Micro Secure Digital), on a Cloud Server, on a Web Server or on a Mobile Application.

A Data-Driven Analysis of the Paradigm Shift From Permanent to Contractual Recruitment

2021 ◽  
Vol 11 (2) ◽  
pp. 1-16
Author(s):  
Shyla ◽  
Vishal Bhatnagar ◽  
Raju Ranjan ◽  
Arushi Jain
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Paradigm Shift ◽  
High Volume ◽  
Large Datasets ◽  
Streaming Data ◽  
Sensor Data ◽  
Time Data ◽  
Contract Workers ◽  
Programming Framework

Big data is the high-volume, high-variety data which involves data storage, data management, and data analysis that presents a wide view of business possibility for real-time data, sensor data, and streaming data over the web. Big data relies on technology, analysis, and mythology where technology deals with computation power, accuracy, linking, and large datasets; analysis is to find patterns by analyzing large datasets to discover hidden information; and mythology is the wrong beliefs that large datasets give insight knowledge of data that is not obtained by small datasets. In this paper, the authors analyzed the major benefits the organization see from employing contract workers using map reduce programming framework.

scholarly journals Towards data sharing economy on Internet of Things: a semantic for telemetry data

2022 ◽  
Vol 9 (1) ◽  
Author(s):  
Dareen K. Halim ◽  
Samuel Hutagalung
Keyword(s):  
Internet Of Things ◽  
Data Sharing ◽  
Physical World ◽  
Sharing Economy ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Telemetry Data ◽  
Time Data ◽  
Domain Specific Knowledge ◽  
Iot Devices

AbstractInternet of Things (IoT) provides data processing and machine learning techniques with access to physical world data through sensors, namely telemetry data. Acquiring sensor data through IoT faces challenges such as connectivity and proper measurement requiring domain-specific knowledge, that results in data quality problems. Data sharing is one solution to this. In this work, we propose IoT Telemetry Data Hub (IoT TeleHub), a general framework and semantic for telemetry data collection and sharing. The framework is principled on abstraction, layering of elements, and extensibility and openness. We showed that while the framework is defined specifically for telemetry data, it is general enough to be mapped to existing IoT platforms with various use cases. Our framework also considers the machine-readable and machine-understandable notion in regard to resource-constrained IoT devices. We also present IoThingsHub, an IoT platform for real-time data sharing based on the proposed framework. The platform demonstrated that the framework could be implemented with existing technologies such as HTTP, MQTT, SQL, NoSQL.

scholarly journals MWCNT-Coated Cotton Yarn Array For Piezoresistive Force and Bending Sensor Applications in Internet of Things Systems

2021 ◽  
Author(s):  
Dae-Young Jeon ◽  
So Jeong Park ◽  
Tae Yoon Lee ◽  
Gyu-Tae Kim
Keyword(s):  
Internet Of Things ◽  
Data Storage ◽  
Industrial Revolution ◽  
Low Cost ◽  
New Technology ◽  
Force Sensor ◽  
Time Data ◽  
Sensor Applications ◽  
Sensing Material ◽  
Bending Sensor

Abstract Advanced sensors based on the Internet of Things (IoT) are bringing new technology and productivity paradigms to the era of the fourth industrial revolution. Here, piezoresistive force and bending sensors with good performance (sensitivity ≈ 0.3 kPa−1 and Δnormalized resistance/Δcurvature ≈ 1.5 cm) were fabricated by using perpendicularly aligned multi-walled (MW) carbon nanotube-coated yarn (CNT-CY) arrays, which were obtained from a simple dipping-and-drying method. Verification was performed using a proof-of-concept force sensor matrix with an Arduino open-source microcontroller. In addition, a force sensor combined with real-time data storage in Google Cloud was successfully demonstrated. This work provides important information for the development of a robust, low-cost, and easy-to-manufacture IoT platform with MWCNT-based composites as an active sensing material.

scholarly journals DNS-Based Dynamic Authentication for Microservices in IoT

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1233
Author(s):  
Daniel Sánchez ◽  
Andrés López ◽  
Florina Mendoza ◽  
Patricia Arias  Cabarcos
Keyword(s):  
Data Collection ◽  
Ubiquitous Computing ◽  
Real Time ◽  
Low Cost ◽  
Fog Computing ◽  
Time Data ◽  
Elapsed Time ◽  
Real Time Data ◽  
Nomadic Users ◽  
Iot Devices

IoT devices provide with real-time data to a rich ecosystems of services and applications that will be of uttermost importance for ubiquitous computing. The volume of data and the involved subscribe/notify signaling will likely become a challenge also for access and core netkworks. Designers may opt for microservice architectures and fog computing to address this challenge while offering the required flexibility for the main players of ubiquitous computing: nomadic users. Microservices require strong security support for Fog computing, to rely on nodes in the boundary of the network for secure data collection and processing. IoT low cost devices face outdated certificates and security support, due to the elapsed time from manufacture to deployment. In this paper we propose a solution based on microservice architectures and DNSSEC, DANE and chameleon signatures to overcome these difficulties. We will show how trap doors included in the certificates allow a secure and flexible delegation for off-loading data collection and processing to the fog. The main result is showing this requires minimal manufacture device configuration, thanks to DNSSEC support.

scholarly journals Evaluating the applicability of a low-cost sensor for measuring PM2.5 concentration in Ho Chi Minh city, Viet Nam

2019 ◽  
Vol 22 (3) ◽  
pp. 343-347
Author(s):  
Chi Doan Thien Nguyen ◽  
Hien Thi To
Keyword(s):  
Real Time ◽  
Strong Correlation ◽  
Low Cost ◽  
Gravimetric Method ◽  
Reference Method ◽  
Sensor Data ◽  
Ho Chi Minh City ◽  
Time Data ◽  
New Approach ◽  
Pm2.5 Concentration

Introduction: Continuous monitoring provides real-time data which is helpful for measuring air quality; however, these systems are often very expensive, especially for developing countries such as Vietnam. The use of low-cost sensors for monitoring air pollution is a new approach in Vietnam and this study assesses the utility of low-cost, light-scattering-based, particulate sensors for measuring PM2.5 concentrations in Ho Chi Minh City. Methods: The low-cost sensors were compared with both a Beta attenuation monitor (BAM) reference method and a gravimetric method during the rainy season period of October to December 2018. Results: The results showed that there was a very strong correlation between two low-cost sensors (R = 0.97, slope = 1.0), and that the sensor precision varied from 0 to 21.4% with a mean of 3.1%. Both one-minute averaged data and one-hour averaged data showed similar correlations between sensors and BAM (R2 = 0.62 and 0.69, respectively), while 24-hour averaged data showed excellent agreement (R2 = 0.95, slope = 1.05). In addition, we also found a strong correlation between those instruments and a gravimetric method using 24-hour averaged data. A linear regression was used to calibrate the 24-hour averaged sensor data and, once calibrated, the bias dropped to zero. Conclusion: These results show that low-cost sensors can be used for daily measurements of PM2.5 concentrations in Ho Chi Minh City. The effect of air conditions, such as temperature and humidity, should be conducted. Moreover, technical methods to improve time resolution of lowcost sensors need to be developed and applied in order to provide real-time measurements at an inexpensive cost.  

scholarly journals An Interoperable Architecture for the Internet of COVID-19 Things (IoCT) Using Open Geospatial Standards—Case Study: Workplace Reopening

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 50
Author(s):  
Steve H. L. Liang ◽  
Sara Saeedi ◽  
Soroush Ojagh ◽  
Sepehr Honarparvar ◽  
Sina Kiaei ◽  
...  
Keyword(s):  
Real Time ◽  
Situational Awareness ◽  
Low Cost ◽  
Heterogeneous Systems ◽  
Geospatial Data ◽  
Sensor Data ◽  
Contact Tracing ◽  
Time Data ◽  
Sensor Web

To safely protect workplaces and the workforce during and after the COVID-19 pandemic, a scalable integrated sensing solution is required in order to offer real-time situational awareness and early warnings for decision-makers. However, an information-based solution for industry reopening is ineffective when the necessary operational information is locked up in disparate real-time data silos. There is a lot of ongoing effort to combat the COVID-19 pandemic using different combinations of low-cost, location-based contact tracing, and sensing technologies. These ad hoc Internet of Things (IoT) solutions for COVID-19 were developed using different data models and protocols without an interoperable way to interconnect these heterogeneous systems and exchange data on people and place interactions. This research aims to design and develop an interoperable Internet of COVID-19 Things (IoCT) architecture that is able to exchange, aggregate, and reuse disparate IoT sensor data sources in order for informed decisions to be made after understanding the real-time risks in workplaces based on person-to-place interactions. The IoCT architecture is based on the Sensor Web paradigm that connects various Things, Sensors, and Datastreams with an indoor geospatial data model. This paper presents a study of what, to the best of our knowledge, is the first real-world integrated implementation of the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) and IndoorGML standards to calculate the risk of COVID-19 online using a workplace reopening case study. The proposed IoCT offers a new open standard-based information model, architecture, methodologies, and software tools that enable the interoperability of disparate COVID-19 monitoring systems with finer spatial-temporal granularity. A workplace cleaning use case was developed in order to demonstrate the capabilities of this proposed IoCT architecture. The implemented IoCT architecture included proximity-based contact tracing, people density sensors, a COVID-19 risky behavior monitoring system, and the contextual building geospatial data.

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