Special Issue "IoT and Sensor Networks in Industry and Society"

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

Deadline for manuscript submissions: 20 August 2020.

Special Issue Editors

Prof. Dr. Daniele D. Giusto
Website
Guest Editor
Italian University Consortium for Telecommunications – Cagliari Research Unit, 09123 Cagliari, Italy
Interests: communication systems; sensor networks and IoT; smart cities and smart living; digital media
Prof. Dr. Virginia Pilloni
Website
Guest Editor
Department of Electrical and Electronic Engineering, University of Cagliari, 09124 Cagliari, Italy
Interests: Internet of Things; ad hoc networks; efficient resource allocation; smart buildings; crowdsensing/crowdsourcing

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This Special Issue encourages high-quality unpublished contributions on recent advances in IoT and sensor networks towards the implementation of Industry 4.0 and Society 5.0.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 papers will be 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 1800 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

Published Papers (7 papers)

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Research

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Open AccessArticle
Internet of Things (IoT) Platform for Multi-Topic Messaging
Energies 2020, 13(13), 3346; https://doi.org/10.3390/en13133346 - 30 Jun 2020
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 [...] Read more.
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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Open AccessArticle
A Multi-Source Harvesting System Applied to Sensor-Based Smart Garments for Monitoring Workers’ Bio-Physical Parameters in Harsh Environments
Energies 2020, 13(9), 2161; https://doi.org/10.3390/en13092161 - 01 May 2020
Cited by 1
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 [...] Read more.
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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Open AccessArticle
iABACUS: A Wi-Fi-Based Automatic Bus Passenger Counting System
Energies 2020, 13(6), 1446; https://doi.org/10.3390/en13061446 - 19 Mar 2020
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 [...] Read more.
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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Open AccessArticle
Improved Particle Swarm Optimization for Sea Surface Temperature Prediction
Energies 2020, 13(6), 1369; https://doi.org/10.3390/en13061369 - 15 Mar 2020
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 [...] Read more.
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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Open AccessArticle
Safe and Secure Control of Swarms of Vehicles by Small-World Theory
Energies 2020, 13(5), 1043; https://doi.org/10.3390/en13051043 - 26 Feb 2020
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 [...] Read more.
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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Open AccessReview
A Review of Energy Harvesting Techniques for Low Power Wide Area Networks (LPWANs)
Energies 2020, 13(13), 3433; https://doi.org/10.3390/en13133433 (registering DOI) - 03 Jul 2020
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), [...] Read more.
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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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
Energies 2019, 12(24), 4745; https://doi.org/10.3390/en12244745 - 12 Dec 2019
Cited by 2
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 [...] Read more.
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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