scholarly journals Educational Mechatronics and Internet of Things: A Case Study on Dynamic Systems Using MEIoT Weather Station

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 181
Author(s):  
Miriam A. Carlos-Mancilla ◽  
Luis F. Luque-Vega ◽  
Héctor A. Guerrero-Osuna ◽  
Gerardo Ornelas-Vargas ◽  
Yehoshua Aguilar-Molina ◽  
...  
Keyword(s):  
Smart Cities ◽  
Graphic User Interface ◽  
Weather Station ◽  
Intelligent Environments ◽  
Time Data ◽  
Weather Variables ◽  
Open Platform ◽  
Smart Campus ◽  
Iot Devices

This paper presents the design and development of an IoT device, called MEIoT weather station, which combines the Educational Mechatronics and IoT to develop the required knowledge and skills for Industry 4.0. MEIoT weather station connects to the internet, measures eight weather variables, and upload the sensed data to the cloud. The MEIoT weather station is the first device working with the IoT architecture of the National Digital Observatory of Intelligent Environments. In addition, an IoT open platform, GUI-MEIoT, serves as a graphic user interface. GUI-MEIoT is used to visualize the real-time data of the weather variables, it also shows the historical data collected, and allows to export them to a csv file. Finally, an OBNiSE architecture application to Engineering Education is presented with a dynamic system case of study that includes the instructional design carried out within the Educational Mechatronics Conceptual Framework (EMCF) to show the relevance of this proposal. This work main contribution to the state of art is the design and integration of the OBNiSE architecture within the EMCF offering the possibility to add more IoT devices for several smart domains such as smart campus, smart cities, smart people and smart industries.

How the Internet of Things can help knowledge management: a case study from the automotive domain

2017 ◽  
Vol 21 (1) ◽  
pp. 57-70 ◽  
Author(s):  
Lorna Uden ◽  
Wu He
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Internet Of Things ◽  
Current Knowledge ◽  
Time Data ◽  
Content Type ◽  
Useful Knowledge ◽  
Iot Devices ◽  
The Internet Of Things

Purpose Current knowledge management (KM) systems cannot be used effectively for decision-making because of the lack of real-time data. This study aims to discuss how KM can benefit by embedding Internet of Things (IoT). Design/methodology/approach The paper discusses how IoT can help KM to capture data and convert data into knowledge to improve the parking service in transportation using a case study. Findings This case study related to intelligent parking service supported by IoT devices of vehicles shows that KM can play a role in turning the incoming big data collected from IoT devices into useful knowledge more quickly and effectively. Originality/value The literature review shows that there are few papers discussing how KM can benefit by embedding IoT and processing incoming big data collected from IoT devices. The case study developed in this study provides evidence to explain how IoT can help KM to capture big data and convert big data into knowledge to improve the parking service in transportation.

scholarly journals Mining Iot Data for Next Generation Smart Cities

2019 ◽  
Vol 8 (6) ◽  
pp. 259-262
Keyword(s):  
Real Time ◽  
Crucial Role ◽  
Data Analytics ◽  
Data Science ◽  
Smart Cities ◽  
Classification Algorithms ◽  
Time Data ◽  
New Era ◽  
Real Time Data ◽  
Iot Devices

Convergence of Cloud, IoT, Networking devices and Data science has ignited a new era of smart cities concept all around us. The backbone of any smart city is the underlying infrastructure involving thousands of IoT devices connected together to work in real time. Data Analytics can play a crucial role in gaining valuable insights into the volumes of data generated by these devices. The objective of this paper is to apply some most commonly used classification algorithms to a real time dataset and compare their performance on IoT data. The performance summary of the algorithms under test is also tabulated

scholarly journals Combining Fog and Cloud Computing to Support Spatial Analytics in Smart Cities

2021 ◽  
Vol 12 (4) ◽  
Author(s):  
João Paulo Clarindo ◽  
João Pedro C. Castro ◽  
Cristina D. Aguiar
Keyword(s):  
Data Warehouse ◽  
Spatial Data ◽  
Smart Cities ◽  
Fog Computing ◽  
Real Data ◽  
Distributed Data ◽  
Spatial Analytics ◽  
Spatial Data Warehouse ◽  
Iot Devices

Spatial data generated by an Internet of Things (IoT) network is important to assist the spatial analytics process in issues related to smart cities. In these cities, IoT devices generate spatial data constantly. Thus, data can get increasingly voluminous very fast. In this paper, we investigate the challenge of managing these data through the use of a spatial data warehouse designed over a parallel and distributed data processing framework extended with a spatial analytics system. We propose an architecture aimed to assist a smart cities manager in the decision-making process. This architecture integrates a cloud layer where these technologies are located with a fog computing layer for extracting, transforming and loading the data into the spatial data warehouse. Furthermore, we introduce a set of guidelines to aid smart cities managers to implement the proposed architecture. These guidelines describe and discuss important issues that should be faced by the managers. We validate our architecture with a case study that uses real data collected by IoT devices in a smart city. This case study encompasses the execution of three different categories of spatial queries, demonstrating the architecture's efficacy and effectiveness to support spatial analytics in the context of smart cities.

scholarly journals An Investigation into Stakeholders’ Perception of Smart Campus Criteria: The American University of Sharjah as a Case Study

2020 ◽  
Vol 12 (12) ◽  
pp. 5187 ◽  
Author(s):  
Vian Ahmed ◽  
Karam Abu Alnaaj ◽  
Sara Saboor
Keyword(s):  
United Arab Emirates ◽  
Quantitative Methods ◽  
Service Providers ◽  
Smart Cities ◽  
Clear Understanding ◽  
Exploratory Research ◽  
Research Attention ◽  
American University ◽  
Smart Campus

In recent times, smart cities and sustainable development have drawn significant research attention. Among developed and developing countries, the United Arab Emirates (UAE) has been at the forefront in becoming an incubator for smart cities; in particular, it has placed some efforts in the education sector by transforming the traditional campus into a Smart Campus. As the term Smart Campus attracts professionals and academics from multiple disciplines, and the technology keeps intervening in every aspect of life, it becomes inevitable for the Smart Campus to take place and deploy the future vision of smart cities. As a first step to achieve this vision, it is very important to develop a clear understanding of what is a Smart Campus. To date, there is still no clear perception of what a Smart Campus would look like, or what are the main components that can form a Smart Campus. Therefore, the objective of this research is to use the set of comprehensive criteria to identify what it is perceived to be a Smart Campus and evaluate these criteria from the stakeholders’ perception. The main criteria are defined from the literature review, and a case study is conducted on the American University of Sharjah campus stakeholders (faculty, students, management, and Information Technology (IT)) to assess the designated criteria. This exploratory research relies on both qualitative and quantitative methods to perform the analysis, taking into consideration the perceptions of students, faculty, and IT service providers. Finally, having defined and evaluated the criteria that underpin the Smart Campus framework, a set of recommendations are drawn to guide the utilization of a Smart Campus within higher education settings. This research opens the doors for future studies to gain a deeper insight into the type of decisions that need to be made to transform a traditional campus to a Smart Campus.

A visualization platform for internet of things in manufacturing applications

2016 ◽  
Vol 26 (2) ◽  
pp. 377-401 ◽  
Author(s):  
Zhuming Bi ◽  
Guoping Wang ◽  
Li Da Xu
Keyword(s):  
Real Time ◽  
Transportation Systems ◽  
Graphic User Interface ◽  
Functional Modules ◽  
Time Data ◽  
Content Type ◽  
Manufacturing Applications ◽  
Manufacturing Environments ◽  
And Storage

Purpose – The purpose of this paper is to present a visualization platform to control and monitor wireless sensor networks (WSNs) in manufacturing applications. Design/methodology/approach – To make the platform flexible and versatile, a modular framework is adopted in modeling and visualizing WSNs. The Eclipse programming environment is used to maximize the scalability and adaptability of the platform. A set of the core functional modules have been designed and implemented to support the system operation. The platform is validated through a case study simulation. Findings – The platform is capable of accommodating different operating systems such as Windows and Linux. It allows integrating new plug-ins developed in various languages such as Java, C, C++, and Matlab. The Graphic User Interface has been applied to process and visualize the acquired real-time data from a WSN, and the embodied methodologies can be used to predict the behaviors of objects in the network. Research limitations/implications – The work has shown the feasibility and potential of the proposed platform in improving the real-time performance of WSN. However, the number of the developed functional modules is limited, and additional effort is required to develop sophisticated functional modules or sub-systems for a customized application. Practical implications – The platform can be applied to monitor and visualize various WSN applications in manufacturing environments such as automated workcells, transportation systems, logistic, and storage systems. Originality/value – The work is motivated by the scarce research on the development tools for monitoring and visualization of WSNs in manufacturing applications. The proposed platform serves for both of system developers and users. It is modularized with a set of core functional modules; it can be extended to accommodate new functional modules with a minimal effort for a different application.

SenCity – Evaluating Users’ Experiences of Intelligent Lighting for Well-Being in Smart Cities

2018 ◽  
pp. 60-67
Author(s):  
Henrika Pihlajaniemi ◽  
Anna Luusua ◽  
Eveliina Juntunen
Keyword(s):  
Case Studies ◽  
Smart City ◽  
Smart Cities ◽  
Well Being ◽  
Evaluation Methods ◽  
Data Analyses ◽  
Sense Of Security ◽  
System Data

This paper presents the evaluation of usersХ experiences in three intelligent lighting pilots in Finland. Two of the case studies are related to the use of intelligent lighting in different kinds of traffic areas, having emphasis on aspects of visibility, traffic and movement safety, and sense of security. The last case study presents a more complex view to the experience of intelligent lighting in smart city contexts. The evaluation methods, tailored to each pilot context, include questionnaires, an urban dashboard, in-situ interviews and observations, evaluation probes, and system data analyses. The applicability of the selected and tested methods is discussed reflecting the process and achieved results.

scholarly journals Virtualizing AI at the Distributed Edge Towards Intelligent IoT Applications

2021 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
Claudia Campolo ◽  
Giacomo Genovese ◽  
Antonio Iera ◽  
Antonella Molinaro
Keyword(s):  
Resource Constraints ◽  
Smart Cities ◽  
Traditional Approach ◽  
Low Cost ◽  
Iot Applications ◽  
Software And Hardware ◽  
Iot Devices ◽  
Consumer Devices ◽  
Hardware Platforms ◽  
Smart Factories

Several Internet of Things (IoT) applications are booming which rely on advanced artificial intelligence (AI) and, in particular, machine learning (ML) algorithms to assist the users and make decisions on their behalf in a large variety of contexts, such as smart homes, smart cities, smart factories. Although the traditional approach is to deploy such compute-intensive algorithms into the centralized cloud, the recent proliferation of low-cost, AI-powered microcontrollers and consumer devices paves the way for having the intelligence pervasively spread along the cloud-to-things continuum. The take off of such a promising vision may be hurdled by the resource constraints of IoT devices and by the heterogeneity of (mostly proprietary) AI-embedded software and hardware platforms. In this paper, we propose a solution for the AI distributed deployment at the deep edge, which lays its foundation in the IoT virtualization concept. We design a virtualization layer hosted at the network edge that is in charge of the semantic description of AI-embedded IoT devices, and, hence, it can expose as well as augment their cognitive capabilities in order to feed intelligent IoT applications. The proposal has been mainly devised with the twofold aim of (i) relieving the pressure on constrained devices that are solicited by multiple parties interested in accessing their generated data and inference, and (ii) and targeting interoperability among AI-powered platforms. A Proof-of-Concept (PoC) is provided to showcase the viability and advantages of the proposed solution.

scholarly journals A Dynamic Mobility Traffic Model Based on Two Modes of Transport in Smart Cities

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 253-270
Author(s):  
Mohammed Bin Hariz ◽  
Dhaou Said ◽  
Hussein T. Mouftah
Keyword(s):  
Data Exchange ◽  
Smart Cities ◽  
Time Data ◽  
School Bus ◽  
The Public ◽  
Ride Sharing ◽  
Dynamic Mobility ◽  
Proposed Model ◽  
Loading System ◽  
Suitable Combination

This paper focuses on transportation models in smart cities. We propose a new dynamic mobility traffic (DMT) scheme which combines public buses and car ride-sharing. The main objective is to improve transportation by maximizing the riders’ satisfaction based on real-time data exchange between the regional manager, the public buses, the car ride-sharing and the riders. OpenStreetMap and OMNET++ were used to implement a realistic scenario for the proposed model in a city like Ottawa. The DMT scheme was compared to a multi-loading system used for a school bus. Simulations showed that rider satisfaction was enhanced when a suitable combination of transportation modes was used. Additionally, compared to the other scheme, this DMT scheme can reduce the stress level of car ride-sharing and public buses during the day to the minimal level.

scholarly journals Challenges and Barriers for Net‐Zero/Positive Energy Buildings and Districts—Empirical Evidence from the Smart City Project SPARCS

Buildings ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 78
Author(s):  
Daria Uspenskaia ◽  
Karl Specht ◽  
Hendrik Kondziella ◽  
Thomas Bruckner
Keyword(s):  
Smart City ◽  
Urban Areas ◽  
Smart Cities ◽  
Scale Up ◽  
Practical Experience ◽  
Positive Energy ◽  
Low Carbon ◽  
Net Zero ◽  
Multidimensional Representation

Without decarbonizing cities energy and climate objectives cannot be achieved as cities account for approximately two thirds of energy consumption and emissions. This goal of decarbonizing cities has to be facilitated by promoting net-zero/positive energy buildings and districts and replicating them, driving cities towards sustainability goals. Many projects in smart cities demonstrate novel and groundbreaking low-carbon solutions in demonstration and lighthouse projects. However, as the historical, geographic, political, social and economic context of urban areas vary greatly, it is not always easy to repeat the solution in another city or even district. It is therefore important to look for the opportunities to scale up or repeat successful pilots. The purpose of this paper is to explore common trends in technologies and replication strategies for positive energy buildings or districts in smart city projects, based on the practical experience from a case study in Leipzig—one of the lighthouse cities in the project SPARCS. One of the key findings the paper has proven is the necessity of a profound replication modelling to deepen the understanding of upscaling processes. Three models analyzed in this article are able to provide a multidimensional representation of the solution to be replicated.

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