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Electronics
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Emerging Internet of Things Solutions and Technologies
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Emerging Internet of Things Solutions and Technologies

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 35154

Special Issue Editors

Dr. Franco Cicirelli

E-Mail Website
Guest Editor
ICAR-CNR, Institute of High Performance Computing and Networking of the Italian National Research Council, 87036 Rende, Cosenza, Italy
Interests: Software Engineering tools and methodologies for the modeling; analysis and implementation of complex time-dependent systems; agent-based systems; distributed simulation; parallel and distributed systems; real-time systems; workflow management systems; Internet of Things and cyber-physical systems; smart cities; Petri Nets; Timed Automata and the DEVS formalism
Special Issues, Collections and Topics in MDPI journals
Dr. Antonio Guerrieri

E-Mail Website
Guest Editor
ICAR-CNR, Institute of High Performance Computing and Networking of the Italian National Research Council, 87036 Rende, Cosenza, Italy
Interests: Internet of Things; wireless sensor and actuator networks; WSN frameworks; multi agent systems
Special Issues, Collections and Topics in MDPI journals
Dr. Carlo Mastroianni

E-Mail Website
Guest Editor
ICAR-CNR, Institute of High Performance Computing and Networking of the Italian National Research Council, 87036 Rende, Cosenza, Italy
Interests: parallel and distributed computing; internet of things; cloud computing and data centers; smart grids
Special Issues, Collections and Topics in MDPI journals
Dr. Andrea Vinci

E-Mail Website
Guest Editor
ICAR-CNR, Institute of High Performance Computing and Networking of the Italian National Research Council, 87036 Rende, Cosenza, Italy
Interests: Internet of Things and Cyber-Physical Systems; definitions of platforms and methodologies for the design and implementation of cyber-physical systems; distributed algorithms for the efficient management of urban facilities; swarm intelligence and peer-to-peer techniques; and Data Mining; Ambient Intelligence; edge computing; GPU computing; smart cities
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things, together with its related emerging solutions and technologies, is driving a revolution with respect to the way people perceive and interact with the surrounding environment. Smart homes, smart offices, and smart factories are effective examples of daily life environments that, enriched with sensing, actuating, communication, and computing capabilities, are able to offer advanced services and applications. In this context, multiple advantages can be achieved. Among these, valuable benefits include improving the quality of life of people, increasing the efficiency of systems and plants, enriching the sustainability of buildings, enhancing the performance and usability of the offered services, and making technological solutions affordable to everyone and available everywhere. Further added value can be achieved by carefully combining the above benefits.

The full potential of the emerging Internet of Things paradigm needs a large amount of industrial and academic research efforts directed to the design, development, and assessment of novel architectures, methodologies, solutions, and technologies. Novelties in this field include, but are not limited to, the integration of consciousness and awareness in the Internet of Things, the adoption of machine learning and artificial intelligence, the exploitation of the edge and fog computing paradigms, the development of new standards and protocols, and the use of the digital twin concept to integrate physical and cyber worlds.

This Special Issue aims to involve both academic and industrial communities that operate in the fields of computer science, electronics, control systems, and telecommunications and that intend to offer their contributions in the abovementioned topics, thus advancing Internet of Things solutions and technologies.

Dr. Franco Cicirelli
Dr. Antonio Guerrieri
Dr. Carlo Mastroianni
Dr. Andrea Vinci
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Internet of Things
  • Cyber-physical systems
  • Smart environments
  • Edge and fog computing
  • Smart buildings
  • Architectures, methodologies, and infrastructures for IoT Systems
  • Cognitive and social Internet of Things
  • Digital twin for the Internet of Things
  • Green and sustainable Internet of Things
  • Low energy and battery-less systems
  • Machine learning and artificial intelligence for the Internet of Things
  • Wireless sensor and actuator networks
  • Standards and protocols for the Internet of Things

Published Papers (9 papers)

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Editorial

Jump to: Research, Review

3 pages, 157 KiB  
Editorial
Emerging Internet of Things Solutions and Technologies
by Franco Cicirelli, Antonio Guerrieri, Carlo Mastroianni and Andrea Vinci
Electronics 2021, 10(16), 1928; https://doi.org/10.3390/electronics10161928 - 11 Aug 2021
Cited by 2 | Viewed by 1321
Abstract
The Internet of Things, together with its related emerging solutions and technologies, is driving a revolution with respect to the way people perceive and interact with the surrounding environment [...] Full article
(This article belongs to the Special Issue Emerging Internet of Things Solutions and Technologies)

Research

Jump to: Editorial, Review

17 pages, 4225 KiB  
Article
A Novel Adaptive Battery-Aware Algorithm for Data Transmission in IoT-Based Healthcare Applications
by Hina Magsi, Ali Hassan Sodhro, Mabrook S. Al-Rakhami, Noman Zahid, Sandeep Pirbhulal and Lei Wang
Electronics 2021, 10(4), 367; https://doi.org/10.3390/electronics10040367 - 03 Feb 2021
Cited by 35 | Viewed by 2978
Abstract
The internet of things (IoT) comprises various sensor nodes for monitoring physiological signals, for instance, electrocardiogram (ECG), electroencephalogram (EEG), blood pressure, and temperature, etc., with various emerging technologies such as Wi-Fi, Bluetooth and cellular networks. The IoT for medical healthcare applications forms the [...] Read more.
The internet of things (IoT) comprises various sensor nodes for monitoring physiological signals, for instance, electrocardiogram (ECG), electroencephalogram (EEG), blood pressure, and temperature, etc., with various emerging technologies such as Wi-Fi, Bluetooth and cellular networks. The IoT for medical healthcare applications forms the internet of medical things (IoMT), which comprises multiple resource-restricted wearable devices for health monitoring due to heterogeneous technological trends. The main challenge for IoMT is the energy drain and battery charge consumption in the tiny sensor devices. The non-linear behavior of the battery uses less charge; additionally, an idle time is introduced for optimizing the charge and battery lifetime, and hence the efficient recovery mechanism. The contribution of this paper is three-fold. First, a novel adaptive battery-aware algorithm (ABA) is proposed, which utilizes the charges up to its maximum limit and recovers those charges that remain unused. The proposed ABA adopts this recovery effect for enhancing energy efficiency, battery lifetime and throughput. Secondly, we propose a novel framework for IoMT based pervasive healthcare. Thirdly, we test and implement the proposed ABA and framework in a hardware platform for energy efficiency and longer battery lifetime in the IoMT. Furthermore, the transition of states is modeled by the deterministic mealy finite state machine. The Convex optimization tool in MATLAB is adopted and the proposed ABA is compared with other conventional methods such as battery recovery lifetime enhancement (BRLE). Finally, the proposed ABA enhances the energy efficiency, battery lifetime, and reliability for intelligent pervasive healthcare. Full article
(This article belongs to the Special Issue Emerging Internet of Things Solutions and Technologies)
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26 pages, 5876 KiB  
Article
Internet of Things (IoT) Based Indoor Air Quality Sensing and Predictive Analytic—A COVID-19 Perspective
by Rafia Mumtaz, Syed Mohammad Hassan Zaidi, Muhammad Zeeshan Shakir, Uferah Shafi, Muhammad Moeez Malik, Ayesha Haque, Sadaf Mumtaz and Syed Ali Raza Zaidi
Electronics 2021, 10(2), 184; https://doi.org/10.3390/electronics10020184 - 15 Jan 2021
Cited by 59 | Viewed by 7759
Abstract
Indoor air quality typically encompasses the ambient conditions inside buildings and public facilities that may affect both the mental and respiratory health of an individual. Until the COVID-19 outbreak, indoor air quality monitoring was not a focus area for public facilities such as [...] Read more.
Indoor air quality typically encompasses the ambient conditions inside buildings and public facilities that may affect both the mental and respiratory health of an individual. Until the COVID-19 outbreak, indoor air quality monitoring was not a focus area for public facilities such as shopping complexes, hospitals, banks, restaurants, educational institutes, and so forth. However, the rapid spread of this virus and its consequent detrimental impacts have brought indoor air quality into the spotlight. In contrast to outdoor air, indoor air is recycled constantly causing it to trap and build up pollutants, which may facilitate the transmission of virus. There are several monitoring solutions which are available commercially, a typical system monitors the air quality using gas and particle sensors. These sensor readings are compared against well known thresholds, subsequently generating alarms when thresholds are violated. However, these systems do not predict the quality of air for future instances, which holds paramount importance for taking timely preemptive actions, especially for COVID-19 actual and potential patients as well as people suffering from acute pulmonary disorders and other health problems. In this regard, we have proposed an indoor air quality monitoring and prediction solution based on the latest Internet of Things (IoT) sensors and machine learning capabilities, providing a platform to measure numerous indoor contaminants. For this purpose, an IoT node consisting of several sensors for 8 pollutants including NH3, CO, NO2, CH4, CO2, PM 2.5 along with the ambient temperature & air humidity is developed. For proof of concept and research purposes, the IoT node is deployed inside a research lab to acquire indoor air data. The proposed system has the capability of reporting the air conditions in real-time to a web portal and mobile app through GSM/WiFi technology and generates alerts after detecting anomalies in the air quality. In order to classify the indoor air quality, several machine learning algorithms have been applied to the recorded data, where the Neural Network (NN) model outperformed all others with an accuracy of 99.1%. For predicting the concentration of each air pollutant and thereafter predicting the overall quality of an indoor environment, Long and Short Term Memory (LSTM) model is applied. This model has shown promising results for predicting the air pollutants’ concentration as well as the overall air quality with an accuracy of 99.37%, precision of 99%, recall of 98%, and F1-score of 99%. The proposed solution offers several advantages including remote monitoring, ease of scalability, real-time status of ambient conditions, and portable hardware, and so forth. Full article
(This article belongs to the Special Issue Emerging Internet of Things Solutions and Technologies)
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19 pages, 2690 KiB  
Article
A Framed Slotted ALOHA-Based MAC for Eliminating Vain Wireless Power Transfer in Wireless Powered IoT Networks
by Ying-Jen Lin and Show-Shiow Tzeng
Electronics 2021, 10(1), 9; https://doi.org/10.3390/electronics10010009 - 23 Dec 2020
Cited by 5 | Viewed by 2317
Abstract
Multiple access control (MAC) is crucial for devices to send data packets and harvest wireless energy in wireless powered Internet of Things (IoT) networks. A framed slotted ALOHA (FSA) protocol is employed in several practical networks. This paper studies an FSA-based MAC in [...] Read more.
Multiple access control (MAC) is crucial for devices to send data packets and harvest wireless energy in wireless powered Internet of Things (IoT) networks. A framed slotted ALOHA (FSA) protocol is employed in several practical networks. This paper studies an FSA-based MAC in a centralized wireless powered IoT network, including half-duplex devices and a full-duplex base station transmitting wireless energy in an intended direction. Under such a network, it is possible that a half-duplex device contends for a time slot to transmit a packet while the base station transmits wireless energy to the device in the same time slot, which causes vain charging and wastes the opportunity to charge other devices. To eliminate the vain charging, this paper designs a MAC in which a base station utilizes the information conveyed from devices in advance to arrange the charging order of devices. The novelty is to develop an algorithm to find a charging order of half-duplex devices instead of using full-duplex devices to eliminate the vain charging. Event-driven simulations are conducted to study the performance of the proposed MAC. Simulation results show that the proposed MAC produces better system performances than the system not eliminating the vain charging. In summary, the application of the proposed MAC yields the benefits of higher throughput and lower packet loss. Full article
(This article belongs to the Special Issue Emerging Internet of Things Solutions and Technologies)
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14 pages, 538 KiB  
Article
IoT System for School Dropout Prediction Using Machine Learning Techniques Based on Socioeconomic Data
by Francisco A. da S. Freitas, Francisco F. X. Vasconcelos, Solon A. Peixoto, Mohammad Mehedi Hassan, M. Ali Akber Dewan, Victor Hugo C. de Albuquerque and Pedro P. Rebouças Filho
Electronics 2020, 9(10), 1613; https://doi.org/10.3390/electronics9101613 - 01 Oct 2020
Cited by 16 | Viewed by 4498
Abstract
School dropout permeates various teaching modalities and has generated social, economic, political, and academic damage to those involved in the educational process. Evasion data in higher education courses show the pessimistic scenario of fragility that configures education, mainly in underdeveloped countries. In this [...] Read more.
School dropout permeates various teaching modalities and has generated social, economic, political, and academic damage to those involved in the educational process. Evasion data in higher education courses show the pessimistic scenario of fragility that configures education, mainly in underdeveloped countries. In this context, this paper presents an Internet of Things (IoT) framework for predicting dropout using machine learning methods such as Decision Tree, Logistic Regression, Support Vector Machine, K-nearest neighbors, Multilayer perceptron, and Deep Learning based on socioeconomic data. With the use of socioeconomic data, it is possible to identify in the act of pre-registration who are the students likely to evade, since this information is filled in the pre-registration form. This paper proposes the automation of the prediction process by a method capable of obtaining information that would be difficult and time consuming for humans to obtain, contributing to a more accurate prediction. With the advent of IoT, it is possible to create a highly efficient and flexible tool for improving management and service-related issues, which can provide a prediction of dropout of new students entering higher-level courses, allowing personalized follow-up to students to reverse a possible dropout. The approach was validated by analyzing the accuracy, F1 score, recall, and precision parameters. The results showed that the developed system obtained 99.34% accuracy, 99.34% F1 score, 100% recall, and 98.69% precision using Decision Tree. Thus, the developed system presents itself as a viable option for use in universities to predict students likely to leave university. Full article
(This article belongs to the Special Issue Emerging Internet of Things Solutions and Technologies)
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17 pages, 2386 KiB  
Article
On the Performance of Cloud Services and Databases for Industrial IoT Scalable Applications
by Paolo Ferrari, Emiliano Sisinni, Alessandro Depari, Alessandra Flammini, Stefano Rinaldi, Paolo Bellagente and Marco Pasetti
Electronics 2020, 9(9), 1435; https://doi.org/10.3390/electronics9091435 - 03 Sep 2020
Cited by 12 | Viewed by 2652
Abstract
In the Industry 4.0 the communication infrastructure is derived from the Internet of Things (IoT), and it is called Industrial IoT or IIoT. Smart objects deployed on the field collect a large amount of data which is stored and processed in the Cloud [...] Read more.
In the Industry 4.0 the communication infrastructure is derived from the Internet of Things (IoT), and it is called Industrial IoT or IIoT. Smart objects deployed on the field collect a large amount of data which is stored and processed in the Cloud to create innovative services. However, differently from most of the consumer applications, the industrial scenario is generally constrained by time-related requirements and its needs for real-time behavior (i.e., bounded and possibly short delays). Unfortunately, timeliness is generally ignored by traditional service provider, and the Cloud is treated as a black box. For instance, Cloud databases (generally seen as “Database as a service”—DBaaS) have unknown or hard-to-compare impact on applications. The novelty of this work is to provide an experimental measurement methodology based on an abstract view of IIoT applications, in order to define some easy-to-evaluate metrics focused on DBaaS latency (no matter the actual implementation details are). In particular, the focus is on the impact of DBaaS on the overall communication delays in a typical IIoT scalable context (i.e., from the field to the Cloud and the way back). In order to show the effectiveness of the proposed approach, a real use case is discussed (it is a predictive maintenance application with a Siemens S7 industrial controller transmitting system health status information to a Cloudant DB inside the IBM Bluemix platform). Experiments carried on in this use case provide useful insights about the DBaaS performance: evaluation of delays, effects of involved number of devices (scalability and complexity), constraints of the architecture, and clear information for comparing with other implementations and for optimizing configuration. In other words, the proposed evaluation strategy helps in finding out the peculiarities of Cloud Database service implementations. Full article
(This article belongs to the Special Issue Emerging Internet of Things Solutions and Technologies)
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18 pages, 381 KiB  
Article
Optimization Model for IoT-Aware Energy Exchange in Energy Communities for Residential Users
by Andrea Giordano, Carlo Mastroianni and Luigi Scarcello
Electronics 2020, 9(6), 1003; https://doi.org/10.3390/electronics9061003 - 15 Jun 2020
Cited by 18 | Viewed by 2723
Abstract
In recent years, the distribution of local and renewable generation plants has introduced significant challenges in the management of electrical energy. In order to increase the usage of renewable energy, the prosumers, i.e., the residential users that can act both as producers and [...] Read more.
In recent years, the distribution of local and renewable generation plants has introduced significant challenges in the management of electrical energy. In order to increase the usage of renewable energy, the prosumers, i.e., the residential users that can act both as producers and consumers, can benefit from joining together and forming energy communities. The deployment of an energy community is based both on technological advancements and on a deep understanding of human decision-making, which in turn requires knowledge about the factors that influence the behavior of residential users. This new scenario calls for great research investigations aimed to improve the management of energy exchanges inside energy communities. An important role in this context is played by the Internet of Things (IoT) technology, as smart IoT objects are used both as a source of real-time information regarding the energy production and the users’ requirements, and as actuators that can help to regulate the distribution and use of energy. In this paper, an IoT-aware optimization model for the energy management in energy communities is presented. The main novelty consists in modeling the entire energy community as a whole, rather than each prosumer separately, with the goal of optimizing the energy sharing and balance at the community level. Experimental results, performed in an university campus, show the advantages of the approach and its capability of reducing the energy costs and increasing the community’s energy autonomy. Full article
(This article belongs to the Special Issue Emerging Internet of Things Solutions and Technologies)
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14 pages, 2613 KiB  
Article
Vulnerabilities’ Assessment and Mitigation Strategies for the Small Linux Server, Onion Omega2
by Darshana Upadhyay, Srinivas Sampalli and Bernard Plourde
Electronics 2020, 9(6), 967; https://doi.org/10.3390/electronics9060967 - 10 Jun 2020
Cited by 5 | Viewed by 4075
Abstract
The merger of SCADA (supervisory control and data acquisition) and IoTs (internet of things) technologies allows end-users to monitor and control industrial components remotely. However, this transformation opens up a new set of attack vectors and unpredicted vulnerabilities in SCADA/IoT field devices. Proper [...] Read more.
The merger of SCADA (supervisory control and data acquisition) and IoTs (internet of things) technologies allows end-users to monitor and control industrial components remotely. However, this transformation opens up a new set of attack vectors and unpredicted vulnerabilities in SCADA/IoT field devices. Proper identification, assessment, and verification of each SCADA/IoT component through advanced scanning and penetration testing tools in the early stage is a crucial step in risk assessment. The Omega2, a small Linux server from Onion™, is used to develop various SCADA/IoT systems and is a key component of nano power grid systems. In this paper, we report product level vulnerabilities of Onion Omega2 that we have uncovered using advanced vulnerability scanning tools. Through this research, we would like to assist vendors, asset owners, network administrators, and security professionals by creating an awareness of the vulnerabilities of Onion Omega2 and by suggesting effective mitigations and security best practices. Full article
(This article belongs to the Special Issue Emerging Internet of Things Solutions and Technologies)
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Review

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22 pages, 769 KiB  
Review
Data Quality and Trust: Review of Challenges and Opportunities for Data Sharing in IoT
by John Byabazaire, Gregory O’Hare and Declan Delaney
Electronics 2020, 9(12), 2083; https://doi.org/10.3390/electronics9122083 - 07 Dec 2020
Cited by 25 | Viewed by 5542
Abstract
Existing research recognizes the critical role of quality data in the current big-data and Internet of Things (IoT) era. Quality data has a direct impact on model results and hence business decisions. The growth in the number of IoT-connected devices makes it hard [...] Read more.
Existing research recognizes the critical role of quality data in the current big-data and Internet of Things (IoT) era. Quality data has a direct impact on model results and hence business decisions. The growth in the number of IoT-connected devices makes it hard to access data quality using traditional assessments methods. This is exacerbated by the need to share data across different IoT domains as it increases the heterogeneity of the data. Data-shared IoT defines a new perspective of IoT applications which benefit from sharing data among different domains of IoT to create new use-case applications. For example, sharing data between smart transport and smart industry can lead to other use-case applications such as intelligent logistics management and warehouse management. The benefits of such applications, however, can only be achieved if the shared data is of acceptable quality. There are three main practices in data quality (DQ) determination approaches that are restricting their effective use in data-shared platforms: (1) most DQ techniques validate test data against a known quantity considered to be a reference; a gold reference. (2) narrow sets of static metrics are used to describe the quality. Each consumer uses these metrics in similar ways. (3) data quality is evaluated in isolated stages throughout the processing pipeline. Data-shared IoT presents unique challenges; (1) each application and use-case in shared IoT has a unique description of data quality and requires a different set of metrics. This leads to an extensive list of DQ dimensions which are difficult to implement in real-world applications. (2) most data in IoT scenarios does not have a gold reference. (3) factors endangering DQ in shared IoT exist throughout the entire big-data model from data collection to data visualization, and data use. This paper aims to describe data-shared IoT and shared data pools while highlighting the importance of sharing quality data across various domains. The article examines how we can use trust as a measure of quality in data-shared IoT. We conclude that researchers can combine such trust-based techniques with blockchain for secure end-to-end data quality assessment. Full article
(This article belongs to the Special Issue Emerging Internet of Things Solutions and Technologies)
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