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Internet of Raspberry Things (IoRT): Applications, Challenges and Opportunities
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Internet of Raspberry Things (IoRT): Applications, Challenges and Opportunities

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (30 June 2019) | Viewed by 46213

Special Issue Editors

Dr. Nadeem Javaid

E-Mail Website
Guest Editor
Associate Professor, Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
Interests: energy optimization in smart/micro grids; cloud computing for smart grids; IoT enabled wireless sensor networks; big data analytics in smart grids
Prof. Dr. Leonard Barolli

E-Mail Website
Guest Editor
Department of Information and Communication Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology (FIT), 3-30-1 Wajiro-Higashi, Higashi-Ku, Fukuoka 811-0295, Japan
Interests: high-speed networks; mobile communication systems; ad hoc networking; sensor networks; P2P systems; quality of service (QoS); traffic control mechanisms (policing, routing, congestion control, connection admission control (CAC)); intelligent algorithms (fuzzy theory, genetic algorithms, neural networks); network protocols; agent-based systems; grid and Internet computing; cybersecurity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of things (IoT) encompasses a variety of devices, which has amble of computational capabilities. IoT will make a world global village through intelligent connectivity to maximally analyze and utilize information technology to enable realistic, effective, context aware and scalable data computations, storage and analysis; anywhere and anytime. The presence of context awareness will show the true vision of IoT in multiple disciplines, e.g., health, transportation, smart grid, smart cities, logistics, etc.

However, presently almost 3.5% of the electricity around the globe is being consumed by IoT devices and this figure will be 20% in 2040. Therefore, a need emerges for hardware technologies, platforms, and software architectures to employ IoT to make context based decisions for reliable and precise information about the physical world. In this regard, Raspberry Pi is the most feasible platform which allows low powered operations in IoT devices. This will envision the era of IoT with small, cheap and flexible computer hardware which support end-users to program the devices using Ruby, PHP, JAVA, Python, etc.

Raspberry Pi is, not only the most effective platform to build IoT networks, but also a superb platform to learn about IoT applications. One of the significant factors of Raspberry Pi in IoT is its connectivity space which has a very low power consumption which makes it friendly for battery-operated devices. With the exponential growth of IoT devices across the world, Raspberry Pi will be the best competitor to entertain IoT applications with efficient power consuming hardware.

Therefore, this Special Issue invites research about Raspberry Pi in IoT to make it more user friendly and effective. Topics of interest for the Special Issue include, but are not limited to, the following:

  • Intelligent storage of big data using Raspberry Pi
  • Energy management in residential sector
  • Minimizing energy dissipation in electricity supply side, transmission, and distribution
  • Deep learning for IoRT
  • Artificial intelligence for IoRT connectivity
  • Machine learning for heterogeneous resource based IoRT
  • Pervasive networks and communications
  • IoRT based wireless sensor networks
  • Data Science, data analytics and applications
  • Context aware computing for energy efficiency using IoRT
  • Ubiquitous intelligence based computing
  • Intelligent interfacing for IoRT

Dr. Nadeem Javaid
Prof. Dr. Leonard Barroli
Guest Editors

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Published Papers (5 papers)

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Research

13 pages, 2494 KiB  
Article
An IoT-Based Non-Invasive Glucose Level Monitoring System Using Raspberry Pi
by Antonio Alarcón-Paredes, Victor Francisco-García, Iris P. Guzmán-Guzmán, Jessica Cantillo-Negrete, René E. Cuevas-Valencia and Gustavo A. Alonso-Silverio
Appl. Sci. 2019, 9(15), 3046; https://doi.org/10.3390/app9153046 - 28 Jul 2019
Cited by 34 | Viewed by 13765
Abstract
Patients diagnosed with diabetes mellitus must monitor their blood glucose levels in order to control the glycaemia. Consequently, they must perform a capillary test at least three times per day and, besides that, a laboratory test once or twice per month. These standard [...] Read more.
Patients diagnosed with diabetes mellitus must monitor their blood glucose levels in order to control the glycaemia. Consequently, they must perform a capillary test at least three times per day and, besides that, a laboratory test once or twice per month. These standard methods pose difficulty for patients since they need to prick their finger in order to determine the glucose concentration, yielding discomfort and distress. In this paper, an Internet of Things (IoT)-based framework for non-invasive blood glucose monitoring is described. The system is based on Raspberry Pi Zero (RPi) energised with a power bank, using a visible laser beam and a Raspberry Pi Camera, all implemented in a glove. Data for the non-invasive monitoring is acquired by the RPi Zero taking a set of pictures of the user fingertip and computing their histograms. Generated data is processed by an artificial neural network (ANN) implemented on a Flask microservice using the Tensorflow libraries. In this paper, all measurements were performed in vivo and the obtained data was validated against laboratory blood tests by means of the mean absolute error (10.37%) and Clarke grid error (90.32% in zone A). Estimated glucose values can be harvested by an end device such as a smartphone for monitoring purposes. Full article
(This article belongs to the Special Issue Internet of Raspberry Things (IoRT): Applications, Challenges and Opportunities)
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19 pages, 3716 KiB  
Article
Towards the Real-World Deployment of a Smart Home EMS: A DP Implementation on the Raspberry Pi
by Giuseppe La Tona, Massimiliano Luna, Annalisa Di Piazza and Maria Carmela Di Piazza
Appl. Sci. 2019, 9(10), 2120; https://doi.org/10.3390/app9102120 - 24 May 2019
Cited by 17 | Viewed by 3669
Abstract
As the adoption of distributed generation and energy storage grows and the attention to energy efficiency rises, Energy Management is assuming a growing importance in smart homes. Energy Management Systems (EMSs) should be easily deployable on smart homes and seamlessly integrate with the [...] Read more.
As the adoption of distributed generation and energy storage grows and the attention to energy efficiency rises, Energy Management is assuming a growing importance in smart homes. Energy Management Systems (EMSs) should be easily deployable on smart homes and seamlessly integrate with the Internet of Things (IoT) ecosystem, including generators and storage devices. This paper redesigns a previously presented EMS to reduce its computational complexity, implement it on a Raspberry Pi, and make it compatible with the IoT paradigm. The EMS manages the power flows between smart home loads, renewable generators, electrical storage, and power grid. It communicates with a network of wireless sensors for electrical appliances and with a cloud-based utility data aggregator. The EMS uses Artificial Intelligence and a Dynamic Programming algorithm to fulfill two objectives at the same time: lowering the end user’s electricity bill and reducing the uncertainty on the power exchanged between the end user and the grid manager. The latter goal is obtained by an effective compensation of forecasting errors. A test bench emulating four smart homes was used to measure the effectiveness of the EMS and the efficiency of the proposed implementation. The results show an uncertainty of the aggregated exchanged power of only 2.88% and a reduction of the electrical bill for end-users of up to 3.23%. Furthermore, the EMS can complete its most onerous task in less than 9 min. The good performance of the proposed EMS makes it a candidate for fast adoption by the market. Full article
(This article belongs to the Special Issue Internet of Raspberry Things (IoRT): Applications, Challenges and Opportunities)
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34 pages, 9553 KiB  
Article
A Context Aware Smart Classroom Architecture for Smart Campuses
by Li-Shing Huang, Jui-Yuan Su and Tsang-Long Pao
Appl. Sci. 2019, 9(9), 1837; https://doi.org/10.3390/app9091837 - 3 May 2019
Cited by 80 | Viewed by 10942
Abstract
The Smart campus is a concept of an education institute using technologies, such as information systems, internet of things (IoT), and context-aware computing, to support learning, teaching, and administrative activities. Classrooms are important building blocks of a school campus. Therefore, a feasible architecture [...] Read more.
The Smart campus is a concept of an education institute using technologies, such as information systems, internet of things (IoT), and context-aware computing, to support learning, teaching, and administrative activities. Classrooms are important building blocks of a school campus. Therefore, a feasible architecture for building and running smart classrooms is essential for a smart campus. However, most studies related to the smart classroom are focused on studying or addressing particular technical or educational issues, such as networking, AI applications, lecture quality, and user responses to technology. In this study, an architecture for building and running context-aware smart classrooms is proposed. The proposed architecture consists of three parts including a prototype of a context-aware smart classroom, a model for technology integration, and supporting measures for the operation of smart classrooms in this architecture. The classroom prototype was designed based on our study results and a smart classroom project in Ming Chuan University (MCU). The integration model was a layered model uses Raspberry Pi in the bottom layer of the model to integrate underlying technologies and provide application interfaces to the higher layer applications for the ease of building context-aware smart classroom applications. As a result, application interfaces were implemented using Raspberry Pi based on the proposed technology integration model, and a context-aware energy-saving smart classroom application was implemented based on the proposed classroom prototype and the implemented web application interface. The result shows that, in terms of technology, the proposed architecture is feasible for building context-aware smart classrooms in smart campuses. Full article
(This article belongs to the Special Issue Internet of Raspberry Things (IoRT): Applications, Challenges and Opportunities)
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23 pages, 3719 KiB  
Article
IoT Implementation of Kalman Filter to Improve Accuracy of Air Quality Monitoring and Prediction
by Xiaozheng Lai, Ting Yang, Zetao Wang and Peng Chen
Appl. Sci. 2019, 9(9), 1831; https://doi.org/10.3390/app9091831 - 2 May 2019
Cited by 63 | Viewed by 8471
Abstract
In order to obtain high-accuracy measurements, traditional air quality monitoring and prediction systems adopt high-accuracy sensors. However, high-accuracy sensors are accompanied with high cost, which cannot be widely promoted in Internet of Things (IoT) with many sensor nodes. In this paper, we propose [...] Read more.
In order to obtain high-accuracy measurements, traditional air quality monitoring and prediction systems adopt high-accuracy sensors. However, high-accuracy sensors are accompanied with high cost, which cannot be widely promoted in Internet of Things (IoT) with many sensor nodes. In this paper, we propose a low-cost air quality monitoring and real-time prediction system based on IoT and edge computing, which reduces IoT applications dependence on cloud computing. Raspberry Pi with computing power, as an edge device, runs the Kalman Filter (KF) algorithm, which improves the accuracy of low-cost sensors by 27% on the edge side. Based on the KF algorithm, our proposed system achieves the immediate prediction of the concentration of six air pollutants such as SO2, NO2 and PM2.5 by combining the observations with errors. In the comparison experiments with three common predicted algorithms including Simple Moving Average, Exponentially Weighted Moving Average and Autoregressive Integrated Moving Average, the KF algorithm can obtain the optimal prediction results, and root-mean-square error decreases by 68.3% on average. Taken together, the results of the study indicate that our proposed system, combining edge computing and IoT, can be promoted in smart agriculture. Full article
(This article belongs to the Special Issue Internet of Raspberry Things (IoRT): Applications, Challenges and Opportunities)
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21 pages, 4552 KiB  
Article
A Lightweight Perceptron-Based Intrusion Detection System for Fog Computing
by Belal Sudqi Khater, Ainuddin Wahid Bin Abdul Wahab, Mohd Yamani Idna Bin Idris, Mohammed Abdulla Hussain and Ashraf Ahmed Ibrahim
Appl. Sci. 2019, 9(1), 178; https://doi.org/10.3390/app9010178 - 6 Jan 2019
Cited by 98 | Viewed by 8409
Abstract
Fog computing is a paradigm that extends cloud computing and services to the edge of the network in order to address the inherent problems of the cloud, such as latency and lack of mobility support and location-awareness. The fog is a decentralized platform [...] Read more.
Fog computing is a paradigm that extends cloud computing and services to the edge of the network in order to address the inherent problems of the cloud, such as latency and lack of mobility support and location-awareness. The fog is a decentralized platform capable of operating and processing data locally and can be installed in heterogeneous hardware which makes it ideal for Internet of Things (IoT) applications. Intrusion Detection Systems (IDSs) are an integral part of any security system for fog and IoT networks to ensure the quality of service. Due to the resource limitations of fog and IoT devices, lightweight IDS is highly desirable. In this paper, we present a lightweight IDS based on a vector space representation using a Multilayer Perceptron (MLP) model. We evaluated the presented IDS against the Australian Defense Force Academy Linux Dataset (ADFA-LD) and Australian Defense Force Academy Windows Dataset (ADFA-WD), which are new generation system calls datasets that contain exploits and attacks on various applications. The simulation shows that by using a single hidden layer and a small number of nodes, we are able to achieve a 94% Accuracy, 95% Recall, and 92% F1-Measure in ADFA-LD and 74% Accuracy, 74% Recall, and 74% F1-Measure in ADFA-WD. The performance is evaluated using a Raspberry Pi. Full article
(This article belongs to the Special Issue Internet of Raspberry Things (IoRT): Applications, Challenges and Opportunities)
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