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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (20 August 2020) | Viewed by 58721

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Prof. Dr. Daniele D. Giusto

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Department of Electrical and Electronic Engineering (DIEE), CNIT Research Unit, University of Cagliari, 09123 Cagliari, Italy
Interests: communication systems; sensor networks and IoT; smart cities and smart living; digital media
Special Issues, Collections and Topics in MDPI journals

Dr. Virginia Pilloni

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Guest Editor

Department of Electrical and Electronic Engineering, University of Cagliari, 09127 Cagliari, Italy
Interests: Internet of Things; ad hoc networks; efficient resource allocation; smart buildings; crowdsensing/crowdsourcing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite submissions to a Special Issue of Energies on the subject of “IoT and Sensor Networks in Industry and Society”. In the last decade, the deployment of IoT and sensor networks have made a strong impact on many aspects of society. The seamless integration of technologies to perform sensor data generation, transmission, and processing have enabled the development of smart solutions such as Smart Cities, Smart Agriculture, and Smart Transports. As the business environment is being increasingly digitized, traditional industrial processes are rapidly reshaping to build the fourth industrial revolution, namely Industry 4.0. This will pave the way for the use of ICT as the foundations of Society 5.0, a super smart human-centric society in which both economic development and resolution of societal challenges are achieved.

This Special Issue encourages high-quality unpublished contributions on recent advances in IoT and sensor networks towards the implementation of Industry 4.0 and Society 5.0. Topics of interest for publication include but are not limited to:

Human-centric society and quality of life improvement;
Artificial intelligence and robots for healthcare and ambient assisted living;
ICT for infrastructure inspection and maintenance;
Modeling, planning, and operating industrial processes in smart manufacturing;
IoT data analysis for smart agriculture;
Big data analytics and social media mining;
Safe and secured society in both cyber and physical spaces;
Smart retail and sales management;
Connected and autonomous vehicles;
IoT and sensor networks for environmental monitoring.

Prof. Dr. Daniele Giusto
Prof. Dr. Virginia Pilloni
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. Energies 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 2600 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

Industry 4.0
Society 5.0
Internet of Things
Sensor networks
Artificial intelligence
Big data
Smart city
Robotics
Human-centric society

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Published Papers (12 papers)

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Research

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21 pages, 3095 KiB

Open AccessArticle

Efficiency of Telematics Systems in Management of Operational Activities in Road Transport Enterprises

by Ryszard K. Miler, Marcin J. Kisielewski, Anna Brzozowska and Antonina Kalinichenko

Energies 2020, 13(18), 4906; https://doi.org/10.3390/en13184906 - 18 Sep 2020

Cited by 3 | Viewed by 2931

Abstract

Implemented in road transport enterprises (RTEs) on a large scale, telematics systems are dedicated both to the particular aspects of their operation and to the integrated fields of the total operational functioning of such entities. Hence, a research problem can be defined as the identification of their efficiency levels in the context of operational activities undertaken by RTEs (including more holistic effects, e.g., lowering fuel/energy consumption and negative environmental impacts). Current research studies refer to the efficiency of some particular modules, but there have not been any publications focused on describing the efficiency of telematics systems in a more integrated (holistic) way, due to the lack of a universal tool that could be applied to provide this type of measurement. In this paper, an attempt at filling the identified cognitive gap is presented through empirical research analysing the original matrix developed by the authors that refers to the efficiency rates of organisational activities undertaken by RTEs. The purpose of this paper is to present a tool that has been designed to provide a holistic evaluation of efficiency of telematics systems in RTE operational management. The results are presented in a form of an individual (ontogenetic) matrix of the analysed companies, for which a determinant was calculated with the use of Sarrus’ rule. Obtained in such a way, the set of values identified for the determinants of the subsequent ontogenetic matrices came as an arithmetic progression that characterised the scope and the level of the influence exerted by the implemented IT (information technology) systems on the organisational efficiency of operational activities undertaken by the analysed RTEs. We present a hypothesis stating that the originally developed matrix can be viewed as a reliable tool used for comparative analysis in the field of efficiency of telematics systems in RTEs, and this hypothesis was positively verified during the research. The obtained results prove the significant potential for the wide application of the discussed matrix, which can be used as a universal tool for the analysis and comparison of efficiency indicated by the integrated IT systems in the operational activities undertaken by RTEs. Full article

(This article belongs to the Special Issue IoT and Sensor Networks in Industry and Society)

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14 pages, 366 KiB

Open AccessFeature PaperArticle

Data-Intensive Task Scheduling for Heterogeneous Big Data Analytics in IoT System

by Xin Li, Liangyuan Wang, Jemal H. Abawajy, Xiaolin Qin, Giovanni Pau and Ilsun You

Energies 2020, 13(17), 4508; https://doi.org/10.3390/en13174508 - 1 Sep 2020

Cited by 3 | Viewed by 2607

Abstract

Efficient big data analysis is critical to support applications or services in Internet of Things (IoT) system, especially for the time-intensive services. Hence, the data center may host heterogeneous big data analysis tasks for multiple IoT systems. It is a challenging problem since the data centers usually need to schedule a large number of periodic or online tasks in a short time. In this paper, we investigate the heterogeneous task scheduling problem to reduce the global task execution time, which is also an efficient method to reduce energy consumption for data centers. We establish the task execution for heterogeneous tasks respectively based on the data locality feature, which also indicate the relationship among the tasks, data blocks and servers. We propose a heterogeneous task scheduling algorithm with data migration. The core idea of the algorithm is to maximize the efficiency by comparing the cost between remote task execution and data migration, which could improve the data locality and reduce task execution time. We conduct extensive simulations and the experimental results show that our algorithm has better performance than the traditional methods, and data migration actually works to reduce th overall task execution time. The algorithm also shows acceptable fairness for the heterogeneous tasks. Full article

(This article belongs to the Special Issue IoT and Sensor Networks in Industry and Society)

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13 pages, 843 KiB

Open AccessArticle

IoT-Based Smart Plug for Residential Energy Conservation: An Empirical Study Based on 15 Months’ Monitoring

by Jooseok Oh

Energies 2020, 13(15), 4035; https://doi.org/10.3390/en13154035 - 4 Aug 2020

Cited by 14 | Viewed by 5286

Abstract

The study examines the implications of educating prosumers regarding Internet of Things (IoT) use and monitoring to reduce power consumption in the home and encourage energy conservation, sustainable living, and behavior change. Over 15 months, 125 households and household owners received training regarding IoT plug equipment, usage monitoring, and energy reduction. A face to face survey was then conducted regarding power consumption reductions, frequency of monitoring, and user satisfaction compared to the previous year. The study found that participating households used around 5% less energy compared to average households. The reduction rate was found to have increased when more appliances were connected to smart plugs and their power usage was monitored more frequently. Power usage also fell in a greater level when participants were more satisfied with being given smart plugs and related education. Moreover, energy reduction rates increase when smart plugs were used for cooling and heating appliances as well as video, audio, and related devices. The results suggest that this program can be used to reduce energy use, which can be beneficial for smart homes and smart cities. The study demonstrates the importance of education from the perspective of energy conservation and related policies. Full article

(This article belongs to the Special Issue IoT and Sensor Networks in Industry and Society)

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19 pages, 685 KiB

Open AccessArticle

Combining Blockchain and IoT: Food-Chain Traceability and Beyond

by Jacopo Grecuccio, Edoardo Giusto, Fabio Fiori and Maurizio Rebaudengo

Energies 2020, 13(15), 3820; https://doi.org/10.3390/en13153820 - 25 Jul 2020

Cited by 53 | Viewed by 8939

Abstract

Recently, the interest around the Blockchain concept has grown faster and, as a consequence, several studies about the possibility of exploiting such technology in different application domains have been conducted. Most of these studies highlighted the benefits that the use of the blockchain could bring in those contexts where integrity and authenticity of the data are important, e.g., for reasons linked to regulations about consumers’ healthcare. In such cases, it would be important to collect data, coming in real-time through sensors, and then store them in the blockchain, so that they can become immutable and tamper-proof. In this paper, the design and development of a software framework that allows Internet-of-Things (IoT) devices to interact directly with an Ethereum-based blockchain are reported. The proposed solution represents an alternative way for integrating a wide category of IoT devices without relying on a centralized intermediary and third-party services. The main application scenario for which the project has been conceived regards food-chain traceability in the Industry 4.0 domain. Indeed, the designed system has been integrated into the depiction of a use case for monitoring the temperature of fish products within a warehouse and during the delivery process. Full article

(This article belongs to the Special Issue IoT and Sensor Networks in Industry and Society)

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35 pages, 15537 KiB

Open AccessArticle

An Alertness-Adjustable Cloud/Fog IoT Solution for Timely Environmental Monitoring Based on Wildfire Risk Forecasting

by Athanasios Tsipis, Asterios Papamichail, Ioannis Angelis, George Koufoudakis, Georgios Tsoumanis and Konstantinos Oikonomou

Energies 2020, 13(14), 3693; https://doi.org/10.3390/en13143693 - 17 Jul 2020

Cited by 27 | Viewed by 5826

Abstract

Internet of Things (IoT) appliances, especially those realized through wireless sensor networks (WSNs), have been a dominant subject for heavy research in the environmental and agricultural sectors. To address the ever-increasing demands for real-time monitoring and sufficiently handle the growing volumes of raw data, the cloud/fog computing paradigm is deemed a highly promising solution. This paper presents a WSN-based IoT system that seamlessly integrates all aforementioned technologies, having at its core the cloud/fog hybrid network architecture. The system was intensively validated using a demo prototype in the Ionian University facilities, focusing on response time, an important metric of future smart applications. Further, the developed prototype is able to autonomously adjust its sensing behavior based on the criticality of the prevailing environmental conditions, regarding one of the most notable climate hazards, wildfires. Extensive experimentation verified its efficiency and reported on its alertness and highly conforming characteristics considering the use-case scenario of Corfu Island’s 2019 fire risk severity. In all presented cases, it is shown that through fog leveraging it is feasible to contrive significant delay reduction, with high precision and throughput, whilst controlling the energy consumption levels. Finally, a user-driven web interface is highlighted to accompany the system; it is capable of augmenting the data curation and visualization, and offering real-time wildfire risk forecasting based on Chandler’s burning index scoring. Full article

(This article belongs to the Special Issue IoT and Sensor Networks in Industry and Society)

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27 pages, 840 KiB

Open AccessArticle

Internet of Things (IoT) Platform for Multi-Topic Messaging

by Mahmoud Hussein, Ahmed I. Galal, Emad Abd-Elrahman and Mohamed Zorkany

Energies 2020, 13(13), 3346; https://doi.org/10.3390/en13133346 - 30 Jun 2020

Cited by 9 | Viewed by 2860

Abstract

IoT-based applications operate in a client–server architecture, which requires a specific communication protocol. This protocol is used to establish the client–server communication model, allowing all clients of the system to perform specific tasks through internet communications. Many data communication protocols for the Internet of Things are used by IoT platforms, including message queuing telemetry transport (MQTT), advanced message queuing protocol (AMQP), MQTT for sensor networks (MQTT-SN), data distribution service (DDS), constrained application protocol (CoAP), and simple object access protocol (SOAP). These protocols only support single-topic messaging. Thus, in this work, an IoT message protocol that supports multi-topic messaging is proposed. This protocol will add a simple “brain” for IoT platforms in order to realize an intelligent IoT architecture. Moreover, it will enhance the traffic throughput by reducing the overheads of messages and the delay of multi-topic messaging. Most current IoT applications depend on real-time systems. Therefore, an RTOS (real-time operating system) as a famous OS (operating system) is used for the embedded systems to provide the constraints of real-time features, as required by these real-time systems. Using RTOS for IoT applications adds important features to the system, including reliability. Many of the undertaken research works into IoT platforms have only focused on specific applications; they did not deal with the real-time constraints under a real-time system umbrella. In this work, the design of the multi-topic IoT protocol and platform is implemented for real-time systems and also for general-purpose applications; this platform depends on the proposed multi-topic communication protocol, which is implemented here to show its functionality and effectiveness over similar protocols. Full article

(This article belongs to the Special Issue IoT and Sensor Networks in Industry and Society)

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33 pages, 10652 KiB

Open AccessArticle

A Multi-Source Harvesting System Applied to Sensor-Based Smart Garments for Monitoring Workers’ Bio-Physical Parameters in Harsh Environments

by Roberto de Fazio, Donato Cafagna, Giorgio Marcuccio, Alessandro Minerba and Paolo Visconti

Energies 2020, 13(9), 2161; https://doi.org/10.3390/en13092161 - 1 May 2020

Cited by 27 | Viewed by 6030

Abstract

This paper describes the development and characterization of a smart garment for monitoring the environmental and biophysical parameters of the user wearing it; the wearable application is focused on the control to workers’ conditions in dangerous workplaces in order to prevent or reduce the consequences of accidents. The smart jacket includes flexible solar panels, thermoelectric generators and flexible piezoelectric harvesters to scavenge energy from the human body, thus ensuring the energy autonomy of the employed sensors and electronic boards. The hardware and firmware optimization allowed the correct interfacing of the heart rate and SpO₂ sensor, accelerometers, temperature and electrochemical gas sensors with a modified Arduino Pro mini board. The latter stores and processes the sensor data and, in the event of abnormal parameters, sends an alarm to a cloud database, allowing company managers to check them via a web app. The characterization of the harvesting subsection has shown that ≈ 265 mW maximum power can be obtained in a real scenario, whereas the power consumption due to the acquisition, processing and BLE data transmission functions determined that a 10 mAh/day charge is required to ensure the device’s proper operation. By charging a 380 mAh Lipo battery in a few hours by means of the harvesting system, an energy autonomy of 23 days was obtained, in the absence of any further energy contribution. Full article

(This article belongs to the Special Issue IoT and Sensor Networks in Industry and Society)

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21 pages, 1619 KiB

Open AccessArticle

iABACUS: A Wi-Fi-Based Automatic Bus Passenger Counting System

by Michele Nitti, Francesca Pinna, Lucia Pintor, Virginia Pilloni and Benedetto Barabino

Energies 2020, 13(6), 1446; https://doi.org/10.3390/en13061446 - 19 Mar 2020

Cited by 45 | Viewed by 6858

Abstract

Since the early stages of the Internet-of-Things (IoT), one of the application scenarios that have been affected the most by this new paradigm is mobility. Smart Cities have greatly benefited from the awareness of some people’s habits to develop efficient mobility services. In particular, knowing how people use public transportation services and move throughout urban infrastructure is crucial in several areas, among which the most prominent are tourism and transportation. Indeed, especially for Public Transportation Companies (PTCs), long- and short-term planning of the transit network requires having a thorough knowledge of the flows of passengers in and out vehicles. Thanks to the ubiquitous presence of Internet connections, this knowledge can be easily enabled by sensors deployed on board of public transport vehicles. In this paper, a Wi-Fi-based Automatic Bus pAssenger CoUnting System, named iABACUS, is presented. The objective of iABACUS is to observe and analyze urban mobility by tracking passengers throughout their journey on public transportation vehicles, without the need for them to take any action. Test results proves that iABACUS efficiently detects the number of devices with an active Wi-Fi interface, with an accuracy of 100% in the static case and almost 94% in the dynamic case. In the latter case, there is a random error that only appears when two bus stops are very close to each other. Full article

(This article belongs to the Special Issue IoT and Sensor Networks in Industry and Society)

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18 pages, 1600 KiB

Open AccessArticle

Improved Particle Swarm Optimization for Sea Surface Temperature Prediction

by Qi He, Cheng Zha, Wei Song, Zengzhou Hao, Yanling Du, Antonio Liotta and Cristian Perra

Energies 2020, 13(6), 1369; https://doi.org/10.3390/en13061369 - 15 Mar 2020

Cited by 20 | Viewed by 3370

Abstract

The Sea Surface Temperature (SST) is one of the key factors affecting ocean climate change. Hence, Sea Surface Temperature Prediction (SSTP) is of great significance to the study of navigation and meteorology. However, SST data is well-known to suffer from high levels of redundant information, which makes it very difficult to realize accurate predictions, for instance when using time-series regression. This paper constructs a simple yet effective SSTP model, dubbed DSL (given its origination from methods known as DTW, SVM and LSPSO). DSL is based on time-series similarity measure, multiple pattern learning and parameter optimization. It consists of three parts: (1) using Dynamic Time Warping (DTW) to mine the similarities in historical SST series; (2) training a Support Vector Machine (SVM) using the top-k similar patterns, deriving a robust SSTP model that offers a 5-day prediction window based on multiple SST input sequences; and (3) developing an improved Particle Swarm Optimization (PSO) method, dubbed LSPSO, which uses a local search strategy to achieve the combined requirement of prediction accuracy and efficiency. Our method strives for optimal model parameters (pattern length and interval step) and is suited for long-term series, leading to significant improvements in SST trend predictions. Our experimental validation shows a 16.7% reduction in prediction error, at a 76% gain in operating efficiency. We also achieve a significant improvement in prediction accuracy of non-stationary SST time series, compared to DTW, SVM, DS (i.e., DTW + SVM), and a recent deep learning method dubbed Long-Short Term Memory (LSTM). Full article

(This article belongs to the Special Issue IoT and Sensor Networks in Industry and Society)

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28 pages, 8212 KiB

Open AccessArticle

Safe and Secure Control of Swarms of Vehicles by Small-World Theory

by Nicola Roveri, Antonio Carcaterra, Leonardo Molinari and Gianluca Pepe

Energies 2020, 13(5), 1043; https://doi.org/10.3390/en13051043 - 26 Feb 2020

Cited by 7 | Viewed by 2269

Abstract

The present paper investigates a new paradigm to control a swarm of moving individual vehicles, based on the introduction of a few random long-range communications in a queue dominated by short-range car-following dynamics. The theoretical approach adapts the small-world theory, originally proposed in social sciences, to the investigation of these networks. It is shown that the controlled system exhibits properties of higher synchronization and robustness with respect to communication failures. The considered application to a vehicle swarm shows how safety and security of the related traffic dynamics are strongly increased, diminishing the collision probability even in the presence of a hacker attack to some connectivity channels. Full article

(This article belongs to the Special Issue IoT and Sensor Networks in Industry and Society)

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Review

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24 pages, 422 KiB

Open AccessReview

A Review of Energy Harvesting Techniques for Low Power Wide Area Networks (LPWANs)

by Giacomo Peruzzi and Alessandro Pozzebon

Energies 2020, 13(13), 3433; https://doi.org/10.3390/en13133433 - 3 Jul 2020

Cited by 47 | Viewed by 4711

Abstract

The emergence of Internet of Things (IoT) architectures and applications has been the driver for a rapid growth in wireless technologies for the Machine-to-Machine domain. In this context, a crucial role is being played by the so-called Low Power Wide Area Networks (LPWANs), a bunch of transmission technologies developed to satisfy three main system requirements: low cost, wide transmission range, and low power consumption. This last requirement is especially crucial as IoT infrastructures should operate for long periods on limited quantities of energy: to cope with this limitation, energy harvesting is being applied every day more frequently, and several different techniques are being tested for LPWAN systems. The aim of this survey paper is to provide a detailed overview of the the existing LPWAN systems relying on energy harvesting for their powering. In this context, the different LPWAN technologies and protocols will be discussed and, for each technology, the applied energy harvesting techniques will be described as well as the architecture of the power management units when present. Full article

(This article belongs to the Special Issue IoT and Sensor Networks in Industry and Society)

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64 pages, 3398 KiB

Open AccessReview

A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

by Dana-Mihaela Petroșanu, George Căruțașu, Nicoleta Luminița Căruțașu and Alexandru Pîrjan

Energies 2019, 12(24), 4745; https://doi.org/10.3390/en12244745 - 12 Dec 2019

Cited by 30 | Viewed by 5776

Abstract

Lately, many scientists have focused their research on subjects like smart buildings, sensor devices, virtual sensing, buildings management, Internet of Things (IoT), artificial intelligence in the smart buildings sector, improving life quality within smart homes, assessing the occupancy status information, detecting human behavior with a view to assisted living, maintaining environmental health, and preserving natural resources. The main purpose of our review consists of surveying the current state of the art regarding the recent developments in integrating supervised and unsupervised machine learning models with sensor devices in the smart building sector with a view to attaining enhanced sensing, energy efficiency and optimal building management. We have devised the research methodology with a view to identifying, filtering, categorizing, and analyzing the most important and relevant scientific articles regarding the targeted topic. To this end, we have used reliable sources of scientific information, namely the Elsevier Scopus and the Clarivate Analytics Web of Science international databases, in order to assess the interest regarding the above-mentioned topic within the scientific literature. After processing the obtained papers, we finally obtained, on the basis of our devised methodology, a reliable, eloquent and representative pool of 146 papers scientific works that would be useful for developing our survey. Our approach provides a useful up-to-date overview for researchers from different fields, which can be helpful when submitting project proposals or when studying complex topics such those reviewed in this paper. Meanwhile, the current study offers scientists the possibility of identifying future research directions that have not yet been addressed in the scientific literature or improving the existing approaches based on the body of knowledge. Moreover, the conducted review creates the premises for identifying in the scientific literature the main purposes for integrating Machine Learning techniques with sensing devices in smart environments, as well as purposes that have not been investigated yet. Full article

(This article belongs to the Special Issue IoT and Sensor Networks in Industry and Society)

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