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IoT Multi Sensors

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 82153

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors


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Guest Editor
Computers, Electronics and Automation Department, Stefan cel Mare University of Suceava, 720229 Suceava, Romania
Interests: wireless sensors networks; LPWAN; Low-Power Wide-Area Network; machine learning; large scale high-density WSN; LoRaWAN; SigFox
Special Issues, Collections and Topics in MDPI journals

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Co-Guest Editor
Computers, Electronics and Automation Department, Stefan cel Mare University of Suceava, 720229 Suceava, Romania
Interests: antenna design; Internet of Things; wireless sensor networks

E-Mail Website
Co-Guest Editor
Computers, Electronics and Automation Department, Stefan cel Mare University of Suceava, 720229 Suceava, Romania
Interests: Internet of Things; wireless sensor networks; LPWAN; RFID; antenna design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, we have witnessed continuous discussion about the Internet of Things (IoT) concept, which involves connecting the various objects that surround us in everyday life to the Internet.

In order to cope with the new challenges and IoT applications, Low-Power Wide-Area Networks (LPWANs) have been created. The IoT concept is currently the focus of the entire academic community.

The main purpose of the IoT concept, which is closely related to the Smart City topic, is to increase quality of life by contributing to the efficient use of resources and environment protection. IoT technologies are sufficiently enhanced to enable the development of integrated solutions for multi-sensors design.

This Special Issue will focus on state-of-the-art technologies, the latest findings, and current challenges in IoT with emphasis on healthcare, transportation, antenna design and disease detection.

We shall solicit papers that cover numerous topics of interest that include, but are not limited to:

  • IoT communication protocols;
  • LPWAN for IoT (Sigfox, LoRa, etc.);
  • Antenna design for IoT applications;
  • Large-scale, high-density IoT networks and architectures;
  • IoT applications and multi-sensors for transportation and traffic control;
  • IoT convergence for Smart Health;
  • Machine-learning/deep-learning algorithms for sensing IoT;
  • Machine-learning-based healthcare applications and disease detection;
  • Applications and examples of use.

The Special Issue topic is in the scope of MDPI’s Sensors journal and offers researchers the possibility of publishing their high-quality research related to the IoT concept on multi-sensor technology integration. The scope of the Special Issue is well constructed and it will be a success with a high number of published papers.

Dr. Alexandru Lavric
Guest Editor

Dr. Liliana Anchidin
Dr. Adrian I. Petrariu
Co-Guest Editors

Manuscript Submission Information

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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. Sensors 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

  • IoT
  • LoRa
  • multi sensors
  • LPWAN
  • machine learning
  • antenna design

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Related Special Issue

Published Papers (21 papers)

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23 pages, 6292 KiB  
Article
Comparative Analysis of Power Consumption between MQTT and HTTP Protocols in an IoT Platform Designed and Implemented for Remote Real-Time Monitoring of Long-Term Cold Chain Transport Operations
by Heriberto J. Jara Ochoa, Raul Peña, Yoel Ledo Mezquita, Enrique Gonzalez and Sergio Camacho-Leon
Sensors 2023, 23(10), 4896; https://doi.org/10.3390/s23104896 - 19 May 2023
Cited by 10 | Viewed by 3843
Abstract
IoT platforms for the transportation industry are portable with limited battery life and need real-time and long-term monitoring operations. Since MQTT and HTTP are widely used as the main communication protocols in the IoT, it is imperative to analyze their power consumption to [...] Read more.
IoT platforms for the transportation industry are portable with limited battery life and need real-time and long-term monitoring operations. Since MQTT and HTTP are widely used as the main communication protocols in the IoT, it is imperative to analyze their power consumption to provide quantitative results that help maximize battery life in IoT transportation systems. Although is well known that MQTT consumes less power than HTTP, a comparative analysis of their power consumption with long-time tests and different conditions has not yet been conducted. In this sense, a design and validation of an electronic cost-efficient platform system for remote real-time monitoring is proposed using a NodeMCU module, in which experimentation is carried out for HTTP and MQTT with different QoS levels to make a comparison and demonstrate the differences in power consumption. Furthermore, we characterize the behavior of the batteries in the systems and compare the theoretical analysis with real long-time test results. The experimentation using the MQTT protocol with QoS 0 and 1 was successful, resulting in power savings of 6.03% and 8.33%, respectively, compared with HTTP, demonstrating many more hours in the duration of the batteries, which could be very useful in technological solutions for the transport industry. Full article
(This article belongs to the Special Issue IoT Multi Sensors)
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<p>Request–response architecture in HTTP.</p>
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<p>Publish/subscribe architecture in MQTT protocol.</p>
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<p>Packet transmission method with QoS levels in MQTT.</p>
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<p>Devices used to design the experimental electronic platform: (<b>a</b>) NodeMCU board; (<b>b</b>) DHT11 module and GPS sensor; (<b>c</b>) LCD; (<b>d</b>) microSD card module and memory; and (<b>e</b>) portable battery.</p>
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<p>Measurement equipment: (<b>a</b>) oscilloscope; (<b>b</b>) multimeter; and (<b>c</b>) NI myDAQ.</p>
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<p>HTTP dashboard components: (<b>a</b>) displayed indicators; (<b>b</b>) real-time map; and (<b>c</b>) graphs of temperature and humidity.</p>
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<p>MQTT dashboard: (<b>a</b>) displayed indicators; (<b>b</b>) map with location and routes.</p>
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<p>Database block diagram.</p>
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<p>Database of recorded readings.</p>
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<p>Schematics for the measurements with the NI myDAQ: (<b>a</b>) current measurement; (<b>b</b>) voltage measurement; and (<b>c</b>) complete schematic for the electronic platform.</p>
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<p>LabVIEW programming: (<b>a</b>) front panel; (<b>b</b>) block diagram.</p>
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<p>System prototype: (<b>a</b>) block diagram; (<b>b</b>) physical setup for experimentation.</p>
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<p>Graph of the power consumption in HTTP and MQTT protocols.</p>
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<p>Power consumption in HTTP and MQTT: (<b>a</b>) normal distributions; (<b>b</b>) real distributions.</p>
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<p>Battery life.</p>
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<p>Energy consumption.</p>
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<p>Relative error percentage.</p>
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16 pages, 1672 KiB  
Article
Massive Data Storage Solution for IoT Devices Using Blockchain Technologies
by Alexandru A. Maftei, Alexandru Lavric, Adrian I. Petrariu and Valentin Popa
Sensors 2023, 23(3), 1570; https://doi.org/10.3390/s23031570 - 1 Feb 2023
Cited by 20 | Viewed by 4222
Abstract
The Internet of Things (IoT) concept involves connecting devices to the internet and forming a network of objects that can collect information from the environment without human intervention. Although the IoT concept offers some advantages, it also has some issues that are associated [...] Read more.
The Internet of Things (IoT) concept involves connecting devices to the internet and forming a network of objects that can collect information from the environment without human intervention. Although the IoT concept offers some advantages, it also has some issues that are associated with cyber security risks, such as the lack of detection of malicious wireless sensor network (WSN) nodes, lack of fault tolerance, weak authorization, and authentication of nodes, and the insecure management of received data from IoT devices. Considering the cybersecurity issues of IoT devices, there is an urgent need of finding new solutions that can increase the security level of WSNs. One issue that needs attention is the secure management and data storage for IoT devices. Most of the current solutions are based on systems that operate in a centralized manner, ecosystems that are easy to tamper with and provide no records regarding the traceability of the data collected from the sensors. In this paper, we propose an architecture based on blockchain technology for securing and managing data collected from IoT devices. By implementing blockchain technology, we provide a distributed data storage architecture, thus eliminating the need for a centralized network topology using blockchain advantages such as immutability, decentralization, distributivity, enhanced security, transparency, instant traceability, and increased efficiency through automation. From the obtained results, the proposed architecture ensures a high level of performance and can be used as a scalable, massive data storage solution for IoT devices using blockchain technologies. New WSN communication protocols can be easily enrolled in our data storage blockchain architecture without the need for retrofitting, as our system does not depend on any specific communication protocol and can be applied to any IoT application. Full article
(This article belongs to the Special Issue IoT Multi Sensors)
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<p>Classic wireless sensor network architecture.</p>
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<p>Proposed blockchain WSN architecture for data storage.</p>
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<p>New nodes join the request procedure to a blockchain-enabled WSN.</p>
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<p>Average latency metric for the proposed blockchain architecture: (<b>a</b>) 500, 1000, 2500, 5000 blockchain WSN nodes; (<b>b</b>) 7500, 10,000, 15,000, 20,000 blockchain WSN nodes.</p>
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<p>Throughput: (<b>a</b>) 500, 1000, 2500, 5000 blockchain WSN nodes; (<b>b</b>) 7500, 10,000, 15,000, 20,000 blockchain WSN nodes.</p>
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14 pages, 13884 KiB  
Article
The Design and Development of a Microstrip Antenna for Internet of Things Applications
by Liliana Anchidin, Alexandru Lavric, Partemie-Marian Mutescu, Adrian I. Petrariu and Valentin Popa
Sensors 2023, 23(3), 1062; https://doi.org/10.3390/s23031062 - 17 Jan 2023
Cited by 16 | Viewed by 5393
Abstract
The Internet of Things (IoT) has become a part of modern life where it is used for data acquisition and long-range wireless communications. Regardless of the IoT application profile, every wireless communication transmission is enabled by highly efficient antennas. The role of the [...] Read more.
The Internet of Things (IoT) has become a part of modern life where it is used for data acquisition and long-range wireless communications. Regardless of the IoT application profile, every wireless communication transmission is enabled by highly efficient antennas. The role of the antenna is thus very important and must not be neglected. Considering the high demand of IoT applications, there is a constant need to improve antenna technologies, including new antenna designs, in order to increase the performance level of WSNs (Wireless Sensor Networks) and enhance their efficiency by enabling a long range and a low error-rate communication link. This paper proposes a new antenna design that is able to increase the performance level of IoT applications by means of an original design. The antenna was designed, simulated, tested, and evaluated in a real operating scenario. From the obtained results, it ensured a high level of performance and can be used in IoT applications specific to the 868 MHz frequency band.By inserting two notches along x axis, we find an optimal structure of the microstrip patch antenna with a reflection coefficient of −34.3 dB and a bandwidth of 20 MHz. After testing the designed novel antenna in real IoT operating conditions, we concluded that the proposed antenna can increase the performance level of IoT wireless communications. Full article
(This article belongs to the Special Issue IoT Multi Sensors)
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<p>Design methodology.</p>
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<p>Antenna dimensions: (<b>a</b>) Front view and side view with dimensions; (<b>b</b>) Reflection coefficient.</p>
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<p>Varying the notches’ width: (<b>a</b>) reflection coefficient; (<b>b</b>) input impedance.</p>
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<p>Varying the notches’ length: (<b>a</b>) reflection coefficient; (<b>b</b>) input impedance.</p>
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<p>Patch antenna with two notches. The slot position is moved along the x axis. (<b>a</b>) Reflection coefficient; (<b>b</b>) input impedance.</p>
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<p>Feed-point position variation: (<b>a</b>) reflection coefficient; (<b>b</b>) input impedance.</p>
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<p>Varying the notches’ position: (<b>a</b>) reflection coefficient; (<b>b</b>) input impedance.</p>
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<p>A centered slot integrated in the antenna design: (<b>a</b>) reflection coefficient; (<b>b</b>) input impedance.</p>
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<p>Rectangular patch antenna: (<b>a</b>) reflection coefficient; (<b>b</b>) input impedance.</p>
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<p>The radiation pattern of the developed IoT antenna: (<b>a</b>) gain and (<b>b</b>) directivity.</p>
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<p>Transceiver influence: (<b>a</b>) reflection coefficient; (<b>b</b>) input impedance.</p>
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<p>The developed IoT antenna.</p>
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<p>The proposed test setup: (<b>a</b>) proposed novel IoT antenna; (<b>b</b>) reference omnidirectional antenna; (<b>c</b>) reference microstrip antenna.</p>
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<p>Test results: (<b>a</b>) reflection coefficient; (<b>b</b>) VSWR.</p>
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<p>Real operating scenario test setup: (<b>a</b>) Sigfox SDR certification Kit with the SigFox Radio Signal Analyzer App; (<b>b</b>) ON Semiconductors Sigfox Development Kit with the proposed antenna attached.</p>
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<p>RSSI parameter variation obtained in the real operating scenario.</p>
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16 pages, 2636 KiB  
Article
A Secure Long-Range Transceiver for Monitoring and Storing IoT Data in the Cloud: Design and Performance Study
by Nurul I. Sarkar, Asish Thomas Kavitha and Md Jahan Ali
Sensors 2022, 22(21), 8380; https://doi.org/10.3390/s22218380 - 1 Nov 2022
Cited by 4 | Viewed by 3165
Abstract
Due to the high demand for Internet of Things (IoT) and real-time data monitoring and control applications in recent years, the long-range (LoRa) communication protocols leverage technology to provide inter-cluster communications in an effective manner. A secure LoRa system is required to monitor [...] Read more.
Due to the high demand for Internet of Things (IoT) and real-time data monitoring and control applications in recent years, the long-range (LoRa) communication protocols leverage technology to provide inter-cluster communications in an effective manner. A secure LoRa system is required to monitor and store IoT data in the cloud. This paper aims to report on the design, analysis, and performance evaluation of a low-cost LoRa transceiver interface unit (433 MHz band) for the real-time monitoring and storing of IoT sensor data in the cloud. We designed and analyzed a low-cost LoRa transceiver interface unit consisting of a LoRa communication module and Wi-Fi module in the laboratory. The system was built (prototype) using radially available hardware devices from the local electronics shops at about USD 150. The transmitter can securely exchange IoT sensor data to the receiver node at about 10 km using a LoRa Wi-Fi module. The receiver node accumulates the sensor data and stores it in the cloud for processing. The performance of the proposed LoRa transceiver was evaluated by field experiments in which two transmitter nodes were deployed on the rooftop of Auckland University of Technology’s Tower building on city campus (New Zealand), and the receiver node was deployed in Liston Park, which was located 10 km away from the University Tower building. The manual incident field tests examined the accuracy of the sensor data, and the system achieved a data accuracy of about 99%. The reaction time of the transmitter nodes was determined by the data accumulation of sensor nodes within 2–20 s. Results show that the system is robust and can be used to effectively link city and suburban park communities. Full article
(This article belongs to the Special Issue IoT Multi Sensors)
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<p>Block diagram of the proposed LoRa transceiver system.</p>
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<p>LoRa transceiver (prototype) with LCD screen and Wi-Fi module.</p>
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<p>Illustrating the LoRa transmitter node–01 (prototype).</p>
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<p>Deployment of LoRa transmitter node–02 at the rooftop of the AUT building.</p>
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<p>Test results for DS18B20 fire sensor.</p>
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<p>Test results for LM393 vibration sensor.</p>
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<p>Test results for the MQ-2 gas sensor.</p>
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<p>Test results for the DHT11 temperature sensor.</p>
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<p>Test results for the DHT11 humidity Sensor.</p>
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<p>Test results for the soil moisture sensor.</p>
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21 pages, 2055 KiB  
Article
Leveraging IoT-Aware Technologies and AI Techniques for Real-Time Critical Healthcare Applications
by Angela-Tafadzwa Shumba, Teodoro Montanaro, Ilaria Sergi, Luca Fachechi, Massimo De Vittorio and Luigi Patrono
Sensors 2022, 22(19), 7675; https://doi.org/10.3390/s22197675 - 10 Oct 2022
Cited by 42 | Viewed by 6157
Abstract
Personalised healthcare has seen significant improvements due to the introduction of health monitoring technologies that allow wearable devices to unintrusively monitor physiological parameters such as heart health, blood pressure, sleep patterns, and blood glucose levels, among others. Additionally, utilising advanced sensing technologies based [...] Read more.
Personalised healthcare has seen significant improvements due to the introduction of health monitoring technologies that allow wearable devices to unintrusively monitor physiological parameters such as heart health, blood pressure, sleep patterns, and blood glucose levels, among others. Additionally, utilising advanced sensing technologies based on flexible and innovative biocompatible materials in wearable devices allows high accuracy and precision measurement of biological signals. Furthermore, applying real-time Machine Learning algorithms to highly accurate physiological parameters allows precise identification of unusual patterns in the data to provide health event predictions and warnings for timely intervention. However, in the predominantly adopted architectures, health event predictions based on Machine Learning are typically obtained by leveraging Cloud infrastructures characterised by shortcomings such as delayed response times and privacy issues. Fortunately, recent works highlight that a new paradigm based on Edge Computing technologies and on-device Artificial Intelligence significantly improve the latency and privacy issues. Applying this new paradigm to personalised healthcare architectures can significantly improve their efficiency and efficacy. Therefore, this paper reviews existing IoT healthcare architectures that utilise wearable devices and subsequently presents a scalable and modular system architecture to leverage emerging technologies to solve identified shortcomings. The defined architecture includes ultrathin, skin-compatible, flexible, high precision piezoelectric sensors, low-cost communication technologies, on-device intelligence, Edge Intelligence, and Edge Computing technologies. To provide development guidelines and define a consistent reference architecture for improved scalable wearable IoT-based critical healthcare architectures, this manuscript outlines the essential functional and non-functional requirements based on deductions from existing architectures and emerging technology trends. The presented system architecture can be applied to many scenarios, including ambient assisted living, where continuous surveillance and issuance of timely warnings can afford independence to the elderly and chronically ill. We conclude that the distribution and modularity of architecture layers, local AI-based elaboration, and data packaging consistency are the more essential functional requirements for critical healthcare application use cases. We also identify fast response time, utility, comfort, and low cost as the essential non-functional requirements for the defined system architecture. Full article
(This article belongs to the Special Issue IoT Multi Sensors)
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<p>Typical Cloud-based architecture.</p>
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<p>Proposed system architecture.</p>
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<p>Intelligent Data Acquisition Layer.</p>
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<p>Autoencoder with separated Encoder and Decoder.</p>
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<p>Simple Autoencoder structure.</p>
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<p>Test set-up.</p>
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<p>Original input vs. reconstructed decoder output.</p>
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<p>Custom BLE−enabled sensing module. (<b>a</b>) Block diagram, (<b>b</b>) prototype device to scale.</p>
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17 pages, 37264 KiB  
Article
A Historical Twist on Long-Range Wireless: Building a 103 km Multi-Hop Network Replicating Claude Chappe’s Telegraph
by Mina Rady, Jonathan Muñoz, Razanne Abu-Aisheh, Mališa Vučinić, José Astorga Tobar, Alfonso Cortes, Quentin Lampin, Dominique Barthel and Thomas Watteyne
Sensors 2022, 22(19), 7586; https://doi.org/10.3390/s22197586 - 6 Oct 2022
Cited by 4 | Viewed by 2893
Abstract
In 1794, French Engineer Claude Chappe coordinated the deployment of a network of dozens of optical semaphores. These formed “strings” that were hundreds of kilometers long, allowing for nationwide telegraphy. The Chappe telegraph inspired future developments of long-range telecommunications using electrical telegraphs and, [...] Read more.
In 1794, French Engineer Claude Chappe coordinated the deployment of a network of dozens of optical semaphores. These formed “strings” that were hundreds of kilometers long, allowing for nationwide telegraphy. The Chappe telegraph inspired future developments of long-range telecommunications using electrical telegraphs and, later, digital telecommunication. Long-range wireless networks are used today for the Internet of Things (IoT), including industrial, agricultural, and urban applications. The long-range radio technology used today offers approximately 10 km of range. Long-range IoT solutions use “star” topology: all devices need to be within range of a gateway device. This limits the area covered by one such network to roughly a disk of a 10 km radius. In this article, we demonstrate a 103 km low-power wireless multi-hop network by combining long-range IoT radio technology with Claude Chappe’s vision. We placed 11 battery-powered devices at the former locations of the Chappe telegraph towers, hanging under helium balloons. We ran a proprietary protocol stack on these devices so they formed a 10-hop multi-hop network: devices forwarded the frames from the “previous” device in the chain. This is, to our knowledge, the longest low power multi-hop wireless network built to date, demonstrating the potential of combining long-range radio technology with multi-hop technology. Full article
(This article belongs to the Special Issue IoT Multi Sensors)
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<p>The Chappe telegraph adopted by the French state as depicted in Ignace Chappe’s book. Three arms were used to convey signals and each could rotate at steps of 45°. (Source: [<a href="#B9-sensors-22-07586" class="html-bibr">9</a>]).</p>
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<p>The Chappe Telegraph network deployed between 1794 and 1846. Each dot represents a tower. (Source: Cité des Télécoms).</p>
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<p>Existing low-power wireless technologies and their indicative ranges.</p>
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<p>The OpenMote Bused in parts of the experiment.</p>
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<p>The communication protocol relays transmitted each received packet three times to increase reliability.</p>
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<p>Format of the packet format used. Source and destination addresses are used for hop-by-hop routing.</p>
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<p>Location of the experiment in the southwest of Paris.</p>
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<p>Terrain elevation is an important factor when selecting locations.</p>
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<p>Illustration of the Fresnel zone between the transmitter and receiver.</p>
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<p>An OpenMote Bwas attached to a helium balloon.</p>
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<p>One out of 11 balloons carrying a mote.</p>
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<p>Captured packets 103 km away from the transmitting computer.</p>
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<p>The RSSI at the receiving mote of each hop, when using the OpenMote B. The red bar shows the sensitivity of that radio in the configuration we used: we needed the RSSI of each hop to be above that.</p>
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18 pages, 5804 KiB  
Article
ParcEMon: IoT Platform for Real-Time Parcel Level Last-Mile Delivery Greenhouse Gas Emissions Reporting and Management
by Ali Yavari, Hamid Bagha, Harindu Korala, Irfan Mirza, Hussein Dia, Paul Scifleet, Jason Sargent and Mahnaz Shafiei
Sensors 2022, 22(19), 7380; https://doi.org/10.3390/s22197380 - 28 Sep 2022
Cited by 12 | Viewed by 3576
Abstract
Transport is Australia’s third-largest source of greenhouse gases accounting for around 17% of emissions. In recent times, and particularly as a result of the global pandemic, the rapid growth within the e-commerce sector has contributed to last-mile delivery becoming one of the main [...] Read more.
Transport is Australia’s third-largest source of greenhouse gases accounting for around 17% of emissions. In recent times, and particularly as a result of the global pandemic, the rapid growth within the e-commerce sector has contributed to last-mile delivery becoming one of the main emission sources. Delivery vehicles operating at the last-mile travel long routes to deliver to customers an array of consignment parcels in varying numbers and weights, and therefore these vehicles play a major role in increasing emissions and air pollutants. The work reported in this paper aims to address these challenges by developing an IoT platform to measure and report on real-world last-mile delivery emissions. Such evaluations help to understand the factors contributing to freight emissions so that appropriate mitigation measures are implemented. Unlike previous research that was completed in controlled laboratory settings, the data collected in this research were from a delivery vehicle under real-world traffic and driving conditions. The IoT platform was tested to provide contextualised reporting by taking into account three main contexts including vehicle, environment and driving behaviours. This approach to data collection enabled the analysis of parcel level emissions and correlation of the vehicle characteristics, road conditions, ambient temperature and other environmental factors and driving behaviour that have an impact on emissions. The raw data collected from the sensors were analysed in real-time in the IoT platform, and the results showed a trade-off between parcel weight and total distance travelled which must be considered when selecting the best delivery order for reducing emissions. Overall, the study demonstrated the feasibility of the IoT platform in collecting the desired levels of data and providing detailed analysis of emissions at the parcel level. This type of micro-level understanding provides an important knowledge base for the enhancement of delivery processes and reduction of last-mile delivery emissions. Full article
(This article belongs to the Special Issue IoT Multi Sensors)
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<p>Emission impacting contexts and related parameters.</p>
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<p>ParcEMon platform architecture.</p>
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<p>ParcEMon platform implementation.</p>
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<p>ParcEMon online dashboard.</p>
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<p>The warehouse location and the order of 13 last mile parcel deliveries.</p>
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<p>Ambient temperature and humidity during the field-trial.</p>
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<p>Gas analyser operating temperature during the field-trial.</p>
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<p>Engine’s RPM data collected during the field-trial.</p>
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<p>Vehicle’s speed data collected during the field-trial.</p>
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<p>CO<sub>2</sub> percentage in emitted gas from the tailpipe.</p>
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<p>Acceleration and deceleration of the vehicle.</p>
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<p>Engine status and vehicle movement (i.e., speed greater than zero) during each delivery in the field-trial.</p>
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<p>CO<sub>2</sub> emission percentage per delivery.</p>
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<p>Van and parcels weight during the field-trial.</p>
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<p>CO<sub>2</sub> cumulative emission percentage per consignment.</p>
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19 pages, 2815 KiB  
Article
Cost-Efficient Coverage of Wastewater Networks by IoT Monitoring Devices
by Arkadiusz Sikorski, Fernando Solano Donado and Stanisław Kozdrowski
Sensors 2022, 22(18), 6854; https://doi.org/10.3390/s22186854 - 10 Sep 2022
Cited by 1 | Viewed by 1694
Abstract
Wireless sensor networks are fundamental for technologies related to the Internet of Things. This technology has been constantly evolving in recent times. In this paper, we consider the problem of minimising the cost function of covering a sewer network. The cost function includes [...] Read more.
Wireless sensor networks are fundamental for technologies related to the Internet of Things. This technology has been constantly evolving in recent times. In this paper, we consider the problem of minimising the cost function of covering a sewer network. The cost function includes the acquisition and installation of electronic components such as sensors, batteries, and the devices on which these components are installed. The problem of sensor coverage in the sewer network or a part of it is presented in the form of a mixed-integer programming model. This method guarantees that we obtain an optimal solution to this problem. A model was proposed that can take into account either only partial or complete coverage of the considered sewer network. The CPLEX solver was used to solve this problem. The study was carried out for a practically relevant network under selected scenarios determined by artificial and realistic datasets. Full article
(This article belongs to the Special Issue IoT Multi Sensors)
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<p>Micromole ring with five modules attached for measuring sewage wastewater physical parameters. From left to right, the attached modules are: battery module, wireless communication module, pH sensor module, Electrical Conductivity sensor module, and a Water Level sensor module.</p>
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<p>EC broadening and flattening caused by dispersion as seen at different measuring points, when 50 litres of sulphuric acid are discharged 81 manholes upstream from the sink point of the network shown in <a href="#sensors-22-06854-f003" class="html-fig">Figure 3</a> in low wastewater flow conditions.</p>
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<p>Sewage network used for numerical experiments.</p>
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<p>Network coverage. Assuming that <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">V</mi> <mn>1</mn> </msub> <mo>=</mo> <mrow> <mn>2</mn> <mo>,</mo> <mn>4</mn> <mo>,</mo> <mn>6</mn> <mo>,</mo> <mn>7</mn> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">V</mi> <mn>2</mn> </msub> <mo>=</mo> <mrow> <mn>4</mn> <mo>,</mo> <mn>5</mn> </mrow> </mrow> </semantics></math>, the sensor installed in pipe 4 is enough to cover both sources. Installing it in pipe 5 would cover only the second source.</p>
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<p>Flow units propagating through the network. The number over the edge is the number of flow units in the pipe. The greater number of flow units next to the outlet node means that a bigger volume of sewage flows in that part of the network when compared to pipes next to the sources.</p>
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<p>Histogram of sampling frequencies in the network.</p>
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<p>Optimal cost of IoT equipment deployment for dataset 1. Sampling frequency in a given pipe depends on the flow and the size of the pipe.</p>
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<p>Optimal cost of IoT equipment deployment for dataset 2. Sampling frequency in a given pipe depends on the flow and the size of the pipe.</p>
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<p>Computation time. Sampling frequency in a given pipe depends on flow and the size of the pipe.</p>
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<p>Convergence of the MIP method.</p>
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25 pages, 24924 KiB  
Article
Self-Sovereignty Identity Management Model for Smart Healthcare System
by Pinky Bai, Sushil Kumar, Geetika Aggarwal, Mufti Mahmud, Omprakash Kaiwartya and Jaime Lloret
Sensors 2022, 22(13), 4714; https://doi.org/10.3390/s22134714 - 22 Jun 2022
Cited by 13 | Viewed by 4006
Abstract
An identity management system is essential in any organisation to provide quality services to each authenticated user. The smart healthcare system should use reliable identity management to ensure timely service to authorised users. Traditional healthcare uses a paper-based identity system which is converted [...] Read more.
An identity management system is essential in any organisation to provide quality services to each authenticated user. The smart healthcare system should use reliable identity management to ensure timely service to authorised users. Traditional healthcare uses a paper-based identity system which is converted into centralised identity management in a smart healthcare system. Centralised identity management has security issues such as denial of service attacks, single-point failure, information breaches of patients, and many privacy issues. Decentralisedidentity management can be a robust solution to these security and privacy issues. We proposed a Self-Sovereign identity management system for the smart healthcare system (SSI-SHS), which manages the identity of each stakeholder, including medical devices or sensors, in a decentralisedmanner in the Internet of Medical Things (IoMT) Environment. The proposed system gives the user complete control of their data at each point. Further, we analysed the proposed identity management system against Allen and Cameron’s identity management guidelines. We also present the performance analysis of SSI as compared to the state-of-the-art techniques. Full article
(This article belongs to the Special Issue IoT Multi Sensors)
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<p>IoT enabled healthcare system.</p>
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<p>SSI communication Sequence.</p>
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<p>SSI-SHS architecture.</p>
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<p>Authentication Process.</p>
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<p>SSI-SHS process flow scenario: The doctor access the IoMT data.</p>
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<p>(<b>a</b>) Registration and (<b>b</b>) authentication time of stakeholders and IoMT devices.</p>
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<p>(<b>a</b>) Registration time on network scale; (<b>b</b>) authentication time on network scale.</p>
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<p>Contract deployment analysis.</p>
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<p>Execution time analysis of off-chain storage.</p>
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<p>Performance comparison.</p>
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34 pages, 4146 KiB  
Article
Planning and Optimization of Software-Defined and Virtualized IoT Gateway Deployment for Smart Campuses
by Divino Ferreira, Jr., João Lucas Oliveira, Carlos Santos , Tércio Filho, Maria Ribeiro, Leandro Alexandre Freitas, Waldir Moreira and Antonio Oliveira-Jr
Sensors 2022, 22(13), 4710; https://doi.org/10.3390/s22134710 - 22 Jun 2022
Cited by 4 | Viewed by 2548
Abstract
The Internet of Things (IoT) is based on objects or “things” that have the ability to communicate and transfer data. Due to the large number of connected objects and devices, there has been a rapid growth in the amount of data that are [...] Read more.
The Internet of Things (IoT) is based on objects or “things” that have the ability to communicate and transfer data. Due to the large number of connected objects and devices, there has been a rapid growth in the amount of data that are transferred over the Internet. To support this increase, the heterogeneity of devices and their geographical distributions, there is a need for IoT gateways that can cope with this demand. The SOFTWAY4IoT project, which was funded by the National Education and Research Network (RNP), has developed a software-defined and virtualized IoT gateway that supports multiple wireless communication technologies and fog/cloud environment integration. In this work, we propose a planning method that uses optimization models for the deployment of IoT gateways in smart campuses. The presented models aimed to quantify the minimum number of IoT gateways that is necessary to cover the desired area and their positions and to distribute IoT devices to the respective gateways. For this purpose, the communication technology range and the data link consumption were defined as the parameters for the optimization models. Three models are presented, which use LoRa, Wi-Fi, and BLE communication technologies. The gateway deployment problem was solved in two steps: first, the gateways were quantified using a linear programming model; second, the gateway positions and the distribution of IoT devices were calculated using the classical K-means clustering algorithm and the metaheuristic particle swarm optimization. Case studies and experiments were conducted at the Samambaia Campus of the Federal University of Goiás as an example. Finally, an analysis of the three models was performed, using metrics such as the silhouette coefficient. Non-parametric hypothesis tests were also applied to the performed experiments to verify that the proposed models did not produce results using the same population. Full article
(This article belongs to the Special Issue IoT Multi Sensors)
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<p>The flowchart of the optimization model.</p>
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<p>An example of area mapping using the Institute of Informatics, UFG.</p>
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<p>An example of the results of device and gateway placement using the K-means method.</p>
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<p>The Samambaia Campus and departments of UFG: (<b>a</b>) the Samambaia Campus; (<b>b</b>) the Institute of Informatics; (<b>c</b>) the academic blocks.</p>
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<p>An example of how IoT points and devices are plotted within the desired area by generating an <span class="html-italic">x</span>,<span class="html-italic">y</span> coordinate matrix.</p>
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<p>The plots of the devices and their respective gateways with a3% data link consumption (LP method): (<b>a</b>) BLE technology with 100 devices and 35 gateways; (<b>b</b>) BLE technology with 300 devices and 37 gateways; (<b>c</b>) Wi-Fi technology with 100 devices and 4 gateways; (<b>d</b>) Wi-Fi technology with 300 devices and 10 gateways; (<b>e</b>) LoRA technology with 100 devices and 4 gateways; (<b>f</b>) LoRA technology with 300 devices and 10 gateways.</p>
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<p>The plots of the devices and their gateways (Wi-Fi technology): (<b>a</b>) the Samambaia Campus with 300 devices (outdoor environment); (<b>b</b>) the academic blocks with 300 devices (indoor environment).</p>
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<p>The plots of the devices and their gateways (LoRa technology; outdoor environment): (<b>a</b>) the Samambaia Campus with 200 devices and 7 gateways; (<b>b</b>) the Samambaia Campus 300 devices and 10 gateways.</p>
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<p>Graphs showing the results of the experiments with variations in the demand for data link consumption: (<b>a</b>) the data link consumption × quantity of gateways; (<b>b</b>) the silhouette coefficient.</p>
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<p>The clustering of IoT devices and their respective gateways using the K-means model: (<b>a</b>) BLE technology with 100 devices and 35 gateways; (<b>b</b>) BLE technology with 300 devices and 37 gateways; (<b>c</b>) Wi-Fi technology with 100 devices and 4 gateways; (<b>d</b>) Wi-Fi technology with 300 devices and 10 gateways; (<b>e</b>) LoRa technology with 100 devices and 4 gateways; (<b>f</b>) LoRa technology with 300 devices and 10 gateways.</p>
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<p>The clustering of IoT devices and their respective gateways using the PSO model (simple approach): (<b>a</b>) BLE technology with 100 devices and 35 gateways; (<b>b</b>) BLE technology with 300 devices and 37 gateways; (<b>c</b>) Wi-Fi technology with 100 devices and 4 gateways; (<b>d</b>) Wi-Fi technology with 300 devices and 10 gateways; (<b>e</b>) LoRa technology with 100 devices and 4 gateways; (<b>f</b>) LoRa technology with 300 devices and 10 gateways.</p>
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<p>The clustering of IoT devices and their respective gateways using the PSO model (simple approach): (<b>a</b>) BLE technology with 100 devices and 35 gateways; (<b>b</b>) BLE technology with 300 devices and 37 gateways; (<b>c</b>) Wi-Fi technology with 100 devices and 4 gateways; (<b>d</b>) Wi-Fi technology with 300 devices and 10 gateways; (<b>e</b>) LoRa technology with 100 devices and 4 gateways; (<b>f</b>) LoRa technology with 300 devices and 10 gateways.</p>
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<p>The clustering of IoT devices and their respective gateways using the PSO model (hybrid approach): (<b>a</b>) BLE technology with 100 devices and 35 gateways; (<b>b</b>) BLE technology with 300 devices and 37 gateways; (<b>c</b>) Wi-Fi technology with 100 devices and 4 gateways; (<b>d</b>) Wi-Fi technology with 300 devices and 10 gateways; (<b>e</b>) LoRa technology with 100 devices and 4 gateways; (<b>f</b>) LoRa technology with 300 devices and 10 gateways.</p>
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<p>The comparison of the total distances between the IoT devices and their gateways using the simple and hybrid PSO models.</p>
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21 pages, 4008 KiB  
Article
IoT-Based Multi-Sensor Healthcare Architectures and a Lightweight-Based Privacy Scheme
by Vassileios Aivaliotis, Kyriaki Tsantikidou and Nicolas Sklavos
Sensors 2022, 22(11), 4269; https://doi.org/10.3390/s22114269 - 3 Jun 2022
Cited by 18 | Viewed by 5867
Abstract
Health 4.0 is a new promising addition to the healthcare industry that innovatively includes the Internet of Things (IoT) and its heterogeneous devices and sensors. The result is the creation of numerous smart health applications that can be more effective, reliable, scalable and [...] Read more.
Health 4.0 is a new promising addition to the healthcare industry that innovatively includes the Internet of Things (IoT) and its heterogeneous devices and sensors. The result is the creation of numerous smart health applications that can be more effective, reliable, scalable and cost-efficient while facilitating people with their everyday life and health conditions. Nevertheless, without proper guidance, the employment of IoT-based health systems can be complicated, especially with regard to security challenges such susceptible application displays. An appropriate comprehension of the structure and the security demands of IoT-based multi-sensor systems and healthcare infrastructures must first be achieved. Furthermore, new architectures that provide lightweight, easily implementable and efficient approaches must be introduced. In this paper, an overview of IoT integration within the healthcare domain as well as a methodical analysis of efficient smart health frameworks, which mainly employ multiple resource and energy-constrained devices and sensors, will be presented. An additional concern of this paper will be the security requirements of these key IoT components and especially of their wireless communications. As a solution, a lightweight-based security scheme, which utilizes the lightweight cryptographic primitive LEAIoT, will be introduced. The proposed hardware-based design displays exceptional results compared to the original CPU-based implementation, with a 99.9% increase in key generation speed and 96.2% increase in encryption/decryption speed. Finally, because of its lightweight and flexible implementation and high-speed keys’ setup, it can compete with other common hardware-based cryptography architectures, where it achieves lower hardware utilization up to 87.9% with the lowest frequency and average throughput. Full article
(This article belongs to the Special Issue IoT Multi Sensors)
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<p>Health 4.0 framework.</p>
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<p>Architecture of IoT layers.</p>
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<p>A general Smart Health architecture.</p>
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<p>Lightweight-based security scheme.</p>
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<p>Proposed architecture of cryptographic primitive.</p>
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<p>The architecture of the encryption and decryption unit.</p>
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<p>Insertion and modular inverse calculation of the symmetric key with: (<b>a</b>) 32-bit; and (<b>b</b>) 256-bit size.</p>
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<p>The modular inverse calculation of the asymmetric key.</p>
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<p>Encryption and decryption operations.</p>
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17 pages, 2199 KiB  
Article
Green Communication in Internet of Things: A Hybrid Bio-Inspired Intelligent Approach
by Manoj Kumar, Sushil Kumar, Pankaj Kumar Kashyap, Geetika Aggarwal, Rajkumar Singh Rathore, Omprakash Kaiwartya and Jaime Lloret
Sensors 2022, 22(10), 3910; https://doi.org/10.3390/s22103910 - 21 May 2022
Cited by 10 | Viewed by 2840
Abstract
Clustering is a promising technique for optimizing energy consumption in sensor-enabled Internet of Things (IoT) networks. Uneven distribution of cluster heads (CHs) across the network, repeatedly choosing the same IoT nodes as CHs and identifying cluster heads in the communication range of other [...] Read more.
Clustering is a promising technique for optimizing energy consumption in sensor-enabled Internet of Things (IoT) networks. Uneven distribution of cluster heads (CHs) across the network, repeatedly choosing the same IoT nodes as CHs and identifying cluster heads in the communication range of other CHs are the major problems leading to higher energy consumption in IoT networks. In this paper, using fuzzy logic, bio-inspired chicken swarm optimization (CSO) and a genetic algorithm, an optimal cluster formation is presented as a Hybrid Intelligent Optimization Algorithm (HIOA) to minimize overall energy consumption in an IoT network. In HIOA, the key idea for formation of IoT nodes as clusters depends on finding chromosomes having a minimum value fitness function with relevant network parameters. The fitness function includes minimization of inter- and intra-cluster distance to reduce the interface and minimum energy consumption over communication per round. The hierarchical order classification of CSO utilizes the crossover and mutation operation of the genetic approach to increase the population diversity that ultimately solves the uneven distribution of CHs and turnout to be balanced network load. The proposed HIOA algorithm is simulated over MATLAB2019A and its performance over CSO parameters is analyzed, and it is found that the best fitness value of the proposed algorithm HIOA is obtained though setting up the parameters popsize=60, number of rooster Nr=0.3, number of hen’s Nh=0.6 and swarm updating frequency θ=10. Further, comparative results proved that HIOA is more effective than traditional bio-inspired algorithms in terms of node death percentage, average residual energy and network lifetime by 12%, 19% and 23%. Full article
(This article belongs to the Special Issue IoT Multi Sensors)
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<p>Block diagram of HIOA.</p>
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<p>Membership function: (<b>a</b>) Residual energy (<b><span class="html-italic">I<sub>re</sub></span></b>), (<b>b</b>) Node density (<b><span class="html-italic">I<sub>nd</sub></span></b>), (<b>c</b>) Distance to edge node (<b><span class="html-italic">I<sub>de</sub></span></b>), and (<b>d</b>) Probability chance (<b><span class="html-italic">I<sub>ch</sub></span></b>).</p>
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<p>Workflow of the HIOA Algorithm.</p>
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<p>Optimization performance of HIOA over iterations (<b>a</b>) <span class="html-italic">pop<sub>size</sub></span> (<b>b</b>) <span class="html-italic">N<sub>r</sub></span> (<b>c</b>) <span class="html-italic">N<sub>h</sub></span> (<b>d</b>) <span class="html-italic">θ</span>.</p>
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<p>Number of active nodes over round.</p>
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<p>Network lifetime over round.</p>
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<p>Average energy consumed over round.</p>
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<p>Average residual energy over rounds.</p>
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<p>Standard deviation over rounds: (<b>a</b>) residual energy and (<b>b</b>) CH load.</p>
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20 pages, 543 KiB  
Article
Cable Monitoring Using Broadband Power Line Communication
by Lukas Benesl, Petr Mlynek, Michal Ptacek, Vaclav Vycital, Jiri Misurec, Jan Slacik, Martin Rusz and Petr Musil
Sensors 2022, 22(8), 3019; https://doi.org/10.3390/s22083019 - 14 Apr 2022
Cited by 3 | Viewed by 3478
Abstract
Power line communication (PLC) is considered one of the possible communication technologies for applications in the field of smart metering, smart substations, smart homes, and recently for the management of renewable resources or micro grid control. This article deals with the use of [...] Read more.
Power line communication (PLC) is considered one of the possible communication technologies for applications in the field of smart metering, smart substations, smart homes, and recently for the management of renewable resources or micro grid control. This article deals with the use of PLC technology to determine the technical condition of the cable. This coefficient can help distribution system operators (DSO) to assess the condition of their cable routes. In this way, possible cable breakdowns and subsequent power outages can be prevented. The resulting methodology for calculating the coefficient is presented in two specific examples of routes, in which a significant benefit for DSO’s can be found. Full article
(This article belongs to the Special Issue IoT Multi Sensors)
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<p>The trend in transmission control protocol (TCP) throughput depending on communication distances between BPL modems on MV lines.</p>
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<p>Measurement of tan <span class="html-italic">δ</span> using a measuring car.</p>
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<p>Measurement of the whole route showing cable types, junction types and measurements indicating partial discharges.</p>
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<p>Measurement of the whole <b>worst route</b> showing cable types, junction types and measurements indicating partial discharges.</p>
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<p>Measurement of the whole <b>longest route</b> showing cable types, junction types and measurements indicating partial discharges.</p>
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19 pages, 4299 KiB  
Article
Fast Constant-Time Modular Inversion over Fp Resistant to Simple Power Analysis Attacks for IoT Applications
by Anissa Sghaier, Medien Zeghid, Chiraz Massoud, Hassan Yousif Ahmed, Abdellah Chehri and Mohsen Machhout
Sensors 2022, 22(7), 2535; https://doi.org/10.3390/s22072535 - 25 Mar 2022
Cited by 2 | Viewed by 2521
Abstract
The advent of the Internet of Things (IoT) has enabled millions of potential new uses for consumers and businesses. However, with these new uses emerge some of the more pronounced risks in the connected object domain. Finite fields play a crucial role in [...] Read more.
The advent of the Internet of Things (IoT) has enabled millions of potential new uses for consumers and businesses. However, with these new uses emerge some of the more pronounced risks in the connected object domain. Finite fields play a crucial role in many public-key cryptographic algorithms (PKCs), which are used extensively for the security and privacy of IoT devices, consumer electronic equipment, and software systems. Given that inversion is the most sensitive and costly finite field arithmetic operation in PKCs, this paper proposes a new, fast, constant-time inverter over prime fields Fp based on the traditional Binary Extended Euclidean (BEE) algorithm. A modified BEE algorithm (MBEEA) resistant to simple power analysis attacks (SPA) is presented, and the design performance area-delay over Fp is explored. Furthermore, the BEE algorithm, modular addition, and subtraction are revisited to optimize and balance the MBEEA signal flow and resource utilization efficiency. The proposed MBEEA architecture was implemented and tested on Xilinx FPGA Virtex #5, #6, and #7 devices. Our implementation over Fp (length of p = 256 bits) with 2035 slices achieved one modular inversion in only 1.12 μs on Virtex-7. Finally, we conducted a thorough comparison and performance analysis to demonstrate that the proposed design outperforms the competing designs, i.e., has a lower area-delay product (ADP) than the reported inverters. Full article
(This article belongs to the Special Issue IoT Multi Sensors)
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<p>Modular inversion methods in <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="double-struck">F</mi> <mi>p</mi> </msub> </mrow> </semantics></math>.</p>
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<p>UML class diagram of IoT/modular inversion/SPA.</p>
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<p>Proposed MBEEA architecture.</p>
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<p>Eight-bit G-KSA/S data-path.</p>
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<p>MBEEA state machine.</p>
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<p>Logic gates versus <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="double-struck">F</mi> <mi>p</mi> </msub> </mrow> </semantics></math>.</p>
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26 pages, 2751 KiB  
Article
An Ultra-Low-Cost RCL-Meter
by Pedro M. C. Inácio, Rui Guerra and Peter Stallinga
Sensors 2022, 22(6), 2227; https://doi.org/10.3390/s22062227 - 14 Mar 2022
Viewed by 2708
Abstract
An ultra-low-cost RCL meter, aimed at IoT applications, was developed, and was used to measure electrical components based on standard techniques without the need of additional electronics beyond the AVR® micro-controller hardware itself and high-level routines. The models and pseudo-routines required to [...] Read more.
An ultra-low-cost RCL meter, aimed at IoT applications, was developed, and was used to measure electrical components based on standard techniques without the need of additional electronics beyond the AVR® micro-controller hardware itself and high-level routines. The models and pseudo-routines required to measure admittance parameters are described, and a benchmark between the ATmega328P and ATmega32U4 AVR® micro-controllers was performed to validate the resistance and capacitance measurements. Both ATmega328P and ATmega32U4 micro-controllers could measure isolated resistances from 0.5 Ω to 80 MΩ and capacitances from 100 fF to 4.7 mF. Inductance measurements are estimated at between 0.2 mH to 1.5 H. The accuracy and range of the measurements of series and parallel RC networks are demonstrated. The relative accuracy (ar) and relative precision (pr) of the measurements were quantified. For the resistance measurements, typically ar, pr < 10% in the interval 100 Ω–100 MΩ. For the capacitance, measured in one of the modes (fast mode), ar < 20% and pr < 5% in the range 100 fF–10 nF, while for the other mode (transient mode), typically ar < 20% in the range 10 nF–10 mF and pr < 5% for 100 pF–10 mF. ar falls below 5% in some sub-ranges. The combination of the two capacitance modes allows for measurements in the range 100 fF–10 mF (11 orders of magnitude) with ar < 20%. Possible applications include the sensing of impedimetric sensor arrays targeted for wearable and in-body bioelectronics, smart agriculture, and smart cities, while complying with small form factor and low cost. Full article
(This article belongs to the Special Issue IoT Multi Sensors)
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<p>Equivalent circuits for the four operation modes available to configure each analog input/output (I/O) port. (<b>a</b>) mode 1, floating input. (<b>b</b>) mode 2, pull-up input. (<b>c</b>) mode 3, low output. (<b>d</b>) mode 4, high output. Each equivalent circuit is adapted from the schematics provided in the datasheets. (<b>e</b>) Block diagram of the proposed measurement system, including the serial communication and voltage source through USB interface to local PC, flash and SRAM memory, internal voltage source, and internal circuitry to program I/O ports to digital or alternate functionalities. (<b>f</b>) Equivalent circuit of two analog I/O ports bridged with a load impedance (<span class="html-italic">Z</span><sub>LOAD</sub>).</p>
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<p>Set-up of a pure load resistance meter (R-meter). (<b>a</b>) Reduction of the equivalent circuit to a voltage divider circuit. (<b>b</b>) Pseudo-code used to implement the resistance meter mode.</p>
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<p>Set-up of a pure load capacitance meter (<span class="html-italic">C</span>-meter). (<b>a</b>) Equivalent circuit of two analog I/O ports bridged with a load capacitance (<span class="html-italic">C</span><sub>LOAD</sub>). (<b>b</b>) Reduction of the equivalent circuit to an impedance divider. (<b>c</b>) Pseudo-code used to implement the fast acquisition mode.</p>
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<p>Set-up for recording a pure load capacitance (<span class="html-italic">C</span><sub>LOAD</sub>) through the transient acquisition mode. (<b>a</b>) Impedance representation of the reduced equivalent circuit. (<b>b</b>) Illustration of the step response in voltage measured at the input terminal of <span class="html-italic">C</span><sub>LOAD</sub>. <span class="html-italic">V</span><sub>IH</sub> is defined in <a href="#sensors-22-02227-t001" class="html-table">Table 1</a>. (<b>c</b>) Pseudo-code used to implement the transient acquisition mode.</p>
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<p>Set-up for recording a load impedance (<span class="html-italic">Z</span><sub>LOAD</sub>) formed by a serial RC network. (<b>a</b>) Illustration of the transient response to a voltage step at the input terminal (<span class="html-italic">P</span><sub>A0</sub>). (<b>b</b>) Pseudo-code used to implement the measurement of a serial RC network.</p>
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<p>Set-up for recording a load impedance (<span class="html-italic">Z</span><sub>LOAD</sub>) formed by a parallel RC network. (<b>a</b>) Illustration of the transient response to a voltage step at the input terminal (<span class="html-italic">P</span><sub>A0</sub>). (<b>b</b>) Pseudo-code used to implement the measurement of a parallel RC network.</p>
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<p>Set-up for recording an isolated load inductance (<span class="html-italic">L</span><sub>LOAD</sub>) through the transient acquisition mode. (<b>a</b>) Impedance representation of the reduced equivalent circuit. (<b>b</b>) Illustration of the step response in voltage measured at the input terminal of <span class="html-italic">L</span><sub>LOAD</sub>. <span class="html-italic">V</span><sub>IL</sub> is defined in <a href="#sensors-22-02227-t001" class="html-table">Table 1</a>. (<b>c</b>) Pseudo-code used to implement the inductance transient acquisition mode.</p>
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<p>Comparison between the ATmega328P and ATmega32U4 AVR<sup>®</sup> micro-controllers configured to record an isolated load resistance (<span class="html-italic">R</span><sub>LOAD</sub>). (<b>a</b>) Measured ADC unit discrete values at port <span class="html-italic">P</span><sub>A0</sub> (<span class="html-italic">N</span><sub>A0</sub>). Each sample consists of an average of 100 consecutive measurements. (<b>b</b>) Measured load resistance (<span class="html-italic">R</span><sub>LOAD</sub>) values according to Equation (2). The green dashed lines represent the theoretical lines. (<b>c</b>) Relative accuracy (<span class="html-italic">a</span><sub>r</sub>) and (<b>d</b>) relative precision (<span class="html-italic">p</span><sub>r</sub>) of the measurements in function of <span class="html-italic">R</span><sub>nominal</sub>. The black dashed line represents the relative uncertainty (<span class="html-italic">u</span><sub>r</sub>) of the <span class="html-italic">R</span><sub>LOAD</sub> measurements according to Equation (16). The white and grey shading areas highlight the levels of <span class="html-italic">u</span><sub>r</sub>, <span class="html-italic">a</span><sub>r</sub> and <span class="html-italic">p</span><sub>r</sub> better than 5%, 10% and 20%. A legend to describe the color scheme used in all plots of <a href="#sensors-22-02227-f008" class="html-fig">Figure 8</a> was included.</p>
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<p>Comparison between the ATmega328P and ATmega32U4 AVR<sup>®</sup> micro-controllers configured to record an isolated load capacitance (<span class="html-italic">C</span><sub>LOAD</sub>) through the fast acquisition mode. (<b>a</b>) Measured ADC values at port <span class="html-italic">P</span><sub>A0</sub> (<span class="html-italic">N</span><sub>A0</sub>). Each ADC sample consists of an average of 100 consecutive measurements. (<b>b</b>) Measured load capacitance (<span class="html-italic">C</span><sub>LOAD</sub>) given by Equation (4). The green dashed lines represent the theoretical lines. (<b>c</b>) Relative accuracy (<span class="html-italic">a</span><sub>r</sub>) and (<b>d</b>) relative precision (<span class="html-italic">p</span><sub>r</sub>) of the measurements in function of <span class="html-italic">C</span><sub>nominal</sub>. The black dashed line represents the relative uncertainty (<span class="html-italic">u</span><sub>r</sub>) of the <span class="html-italic">C</span><sub>LOAD</sub> measurements according to Equation (16). The white and grey shading areas highlight the levels of <span class="html-italic">u</span><sub>r</sub>, <span class="html-italic">a</span><sub>r</sub> and <span class="html-italic">p</span><sub>r</sub> better than 5%, 10% and 20%. A legend to describe the color scheme used in all plots of <a href="#sensors-22-02227-f009" class="html-fig">Figure 9</a> was included.</p>
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<p>Comparison between the ATmega328P and ATmega32U4 AVR<sup>®</sup> micro-controllers configured to record an isolated load capacitance (<span class="html-italic">C</span><sub>LOAD</sub>) through the transient acquisition mode. (<b>a</b>) Measured ADC value and time (Δ<span class="html-italic">t</span>) until the TTL unit changes to digital state high, logic ‘1’ at port <span class="html-italic">P</span><sub>A0</sub> (<span class="html-italic">N</span><sub>A0</sub>). Each ADC and Δ<span class="html-italic">t</span> sample consist of an average of 100 consecutive measurements. (<b>b</b>) Measured load capacitance (<span class="html-italic">C</span><sub>LOAD</sub>) given by Equation (7). The green dashed lines represent the theoretical lines. (<b>c</b>) Relative accuracy (<span class="html-italic">a</span><sub>r</sub>) and (<b>d</b>) relative precision (<span class="html-italic">p</span><sub>r</sub>) of the measurements in function of <span class="html-italic">C</span><sub>nominal</sub>. The black dashed line represents the relative uncertainty (<span class="html-italic">u</span><sub>r</sub>) of the <span class="html-italic">C</span><sub>LOAD</sub> measurements according to Equation (16). The white and grey shading areas highlight the levels of <span class="html-italic">u</span><sub>r</sub>, <span class="html-italic">a</span><sub>r</sub> and <span class="html-italic">p</span><sub>r</sub> better than 5%, 10% and 20%. A legend to describe the color scheme used in all plots of <a href="#sensors-22-02227-f010" class="html-fig">Figure 10</a> was included.</p>
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<p>Comparison between the ATmega328P and ATmega32U4 AVR<sup>®</sup> micro-controllers configured to measure a serial RC network. (<b>a</b>) Measured ADC value at <span class="html-italic">t =</span> 0. (<b>b</b>) Measured ADC value and time (Δ<span class="html-italic">t</span>) until <span class="html-italic">V</span><sub>A1</sub> ≥ <span class="html-italic">V</span><sub>C</sub>. (<b>c</b>,<b>d</b>) Obtained <span class="html-italic">R</span><sub>LOAD</sub> and <span class="html-italic">C</span><sub>LOAD</sub> values according to Equations (2) and (10), respectively. A legend to describe the color scheme used in all plots of <a href="#sensors-22-02227-f011" class="html-fig">Figure 11</a> was included. The black dashed lines always represent the theoretical lines.</p>
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<p>Comparison between the ATmega328P and ATmega32U4 AVR<sup>®</sup> micro-controllers configured to measure a parallel RC network. (<b>a</b>) Measured ADC value at <span class="html-italic">t =</span> 5<span class="html-italic">τ</span>. (<b>b</b>) Measured ADC value at <span class="html-italic">t = τ</span>. (<b>c</b>,<b>d</b>) Obtained <span class="html-italic">R</span><sub>LOAD</sub> and <span class="html-italic">C</span><sub>LOAD</sub> values according to Equations (2) and (12), respectively. A legend to describe the color scheme used in all plots of <a href="#sensors-22-02227-f012" class="html-fig">Figure 12</a> was included. The black dashed lines always represent the theoretical lines.</p>
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20 pages, 1116 KiB  
Article
Blockchain Based Authentication and Cluster Head Selection Using DDR-LEACH in Internet of Sensor Things
by Sana Amjad, Shahid Abbas, Zain Abubaker, Mohammed H. Alsharif, Abu Jahid and Nadeem Javaid
Sensors 2022, 22(5), 1972; https://doi.org/10.3390/s22051972 - 2 Mar 2022
Cited by 22 | Viewed by 4235
Abstract
This paper proposes a blockchain-based node authentication model for the Internet of sensor things (IoST). The nodes in the network are authenticated based on their credentials to make the network free from malicious nodes. In IoST, sensor nodes gather the information from the [...] Read more.
This paper proposes a blockchain-based node authentication model for the Internet of sensor things (IoST). The nodes in the network are authenticated based on their credentials to make the network free from malicious nodes. In IoST, sensor nodes gather the information from the environment and send it to the cluster heads (CHs) for additional processing. CHs aggregate the sensed information. Therefore, their energy rapidly depletes due to extra workload. To solve this issue, we proposed distance, degree, and residual energy-based low-energy adaptive clustering hierarchy (DDR-LEACH) protocol. DDR-LEACH is used to replace CHs with the ordinary nodes based on maximum residual energy, degree, and minimum distance from BS. Furthermore, storing a huge amount of data in the blockchain is very costly. To tackle this issue, an external data storage, named as interplanetary file system (IPFS), is used. Furthermore, for ensuring data security in IPFS, AES 128-bit is used, which performs better than the existing encryption schemes. Moreover, a huge computational cost is required using a proof of work consensus mechanism to validate transactions. To solve this issue, proof of authority (PoA) consensus mechanism is used in the proposed model. The simulation results are carried out, which show the efficiency and effectiveness of the proposed system model. The DDR-LEACH is compared with LEACH and the simulation results show that DDR-LEACH outperforms LEACH in terms of energy consumption, throughput, and improvement in network lifetime with CH selection mechanism. Moreover, transaction cost is computed, which is reduced by PoA during data storage on IPFS and service provisioning. Furthermore, the time is calculated in the comparison of AES 128-bit scheme with existing scheme. The formal security analysis is performed to check the effectiveness of smart contract against attacks. Additionally, two different attacks, MITM and Sybil, are induced in our system to show our system model’s resilience against cyber attacks. Full article
(This article belongs to the Special Issue IoT Multi Sensors)
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<p>Blockchain based nodes’ authentication and CHs’ selection in IoST.</p>
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<p>Interaction of buyers with IPFS.</p>
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<p>Message size.</p>
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<p>Transaction cost during registration and authentication of nodes.</p>
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<p>Energy consumption.</p>
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<p>Network throughput.</p>
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<p>Network lifetime.</p>
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<p>Average transaction cost for service provisioning.</p>
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<p>Average transaction cost for data storage in IPFS.</p>
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<p>Comparison of execution time for AES and RSA.</p>
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<p>Security analysis of attacks with the proposed solution in terms of energy consumption.</p>
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<p>Security analysis of attacks with the proposed solution in terms of throughput.</p>
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<p>Security analysis of attacks with the proposed solution in terms of network lifetime.</p>
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<p>Security analysis of smart contract during registration and authentication of nodes.</p>
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21 pages, 5778 KiB  
Article
Novel Scoring for Energy-Efficient Routing in Multi-Sensored Networks
by Wooseong Kim, Muhammad Muneer Umar, Shafiullah Khan and Muhammad Altaf Khan
Sensors 2022, 22(4), 1673; https://doi.org/10.3390/s22041673 - 21 Feb 2022
Cited by 11 | Viewed by 2709
Abstract
The seamless operation of inter-connected smart devices in Internet of Things (IoT) wireless sensor networks (WSNs) requires consistently available end-to-end routes. However, the sensor nodes that rely on a very limited power source tend to cause disconnection in multi-hop routes due to power [...] Read more.
The seamless operation of inter-connected smart devices in Internet of Things (IoT) wireless sensor networks (WSNs) requires consistently available end-to-end routes. However, the sensor nodes that rely on a very limited power source tend to cause disconnection in multi-hop routes due to power shortages in the WSNs, which eventually results in the inefficiency of the overall IoT network. In addition, the density of the available sensor nodes affects the existence of feasible routes and the level of path multiplicity in the WSNs. Therefore, an efficient routing mechanism is expected to extend the lifetime of the WSNs by adaptively selecting the best routes for the data transfer between interconnected IoT devices. In this work, we propose a novel routing mechanism to balance the energy consumption among all the nodes and elongate the WSN lifetime, which introduces a score value assigned to each node along a path as the combination of evaluation metrics. Specifically, the scoring scheme considers the information of the node density at a certain area and the node energy levels in order to represent the importance of individual nodes in the routes. Furthermore, our routing mechanism allows for incorporating non-cooperative nodes. The simulation results show that the proposed work gives comparatively better results than some other experimented protocols. Full article
(This article belongs to the Special Issue IoT Multi Sensors)
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<p>Identical CNs (<span class="html-italic">a</span> and <span class="html-italic">b</span>).</p>
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<p>Modified routing table format.</p>
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<p>Modified DSR RREQ format.</p>
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<p>Modified DSR RREP format.</p>
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<p>Information exchange through modified OLSR topology control message.</p>
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<p>Flowchart of the proposed mechanism.</p>
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<p>Initial self-configuration of nodes.</p>
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<p>Selection of relay nodes.</p>
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<p>Stages for managing non-cooperative nodes.</p>
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<p>Updating statistics through RREP.</p>
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<p>Placement of 100 nodes in an area of 500 m × 500 m.</p>
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<p>λ values according to nodes’ energies and CNs’ energies.</p>
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<p>Average energy consumption by 5 protocols with a varying number of nodes.</p>
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<p>Number of dead nodes with different pause times.</p>
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<p>Throughput (packets per second) at time intervals.</p>
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<p>Average energy consumption with varying area size.</p>
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<p>Average end-to-end delays with time pauses.</p>
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16 pages, 5214 KiB  
Article
Internet of Things Concept in the Context of the COVID-19 Pandemic: A Multi-Sensor Application Design
by Alexandru Lavric, Adrian I. Petrariu, Partemie-Marian Mutescu, Eugen Coca and Valentin Popa
Sensors 2022, 22(2), 503; https://doi.org/10.3390/s22020503 - 10 Jan 2022
Cited by 19 | Viewed by 4375
Abstract
In this paper, we present the design, development and implementation of an integrated system for the management of COVID-19 patient, using the LoRaWAN communication infrastructure. Our system offers certain advantages when compared to other similar solutions, allowing remote symptom and health monitoring that [...] Read more.
In this paper, we present the design, development and implementation of an integrated system for the management of COVID-19 patient, using the LoRaWAN communication infrastructure. Our system offers certain advantages when compared to other similar solutions, allowing remote symptom and health monitoring that can be applied to isolated or quarantined people, without any external interaction with the patient. The IoT wearable device can monitor parameters of health condition like pulse, blood oxygen saturation, and body temperature, as well as the current location. To test the performance of the proposed system, two persons under quarantine were monitored, for a complete 14-day standard quarantine time interval. Based on the data transmitted to the monitoring center, the medical staff decided, after several days of monitoring, when the measured values were outside of the normal parameters, to do an RT-PCR test for one of the two persons, confirming the SARS-CoV2 virus infection. We have to emphasize the high degree of scalability of the proposed solution that can oversee a large number of patients at the same time, thanks to the LoRaWAN communication protocol used. This solution can be successfully implemented by local authorities to increase monitoring capabilities, also saving lives. Full article
(This article belongs to the Special Issue IoT Multi Sensors)
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<p>Advantages of using IoT for COVID-19 pandemic monitoring.</p>
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<p>COVID-19 disease symptoms.</p>
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<p>COVID-19 effects on the human body.</p>
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<p>COVID-19 IoT multi-sensor patient monitoring architecture.</p>
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<p>Wearable device block diagram of the multi-sensor approach.</p>
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<p>Physical implementation of the wearable device. (<b>a</b>) IoT multi sensor configuration. (<b>b</b>) IoT wearable device installed on patient.</p>
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<p>Field coverage measurements—single GW configuration.</p>
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<p>Field coverage measurements of the LoRaWAN gateways installed on the roof top of Suceava Hospital.</p>
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<p>Field coverage measurements—multiple GW configuration.</p>
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<p>IoT multi-sensor patient monitoring application dashboard.</p>
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<p>Patients’ vital signs measurement obtained from the IoT wearable device. (<b>a</b>) SpO<sub>2</sub> and pulse (Patient A). (<b>b</b>) Body temperature (Patient A). (<b>c</b>) SpO<sub>2</sub> and pulse (Patient B). (<b>d</b>) Body temperature (Patient B).</p>
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16 pages, 5867 KiB  
Article
Large-Scale Internet of Things Multi-Sensor Measurement Node for Smart Grid Enhancement
by Adrian I. Petrariu, Eugen Coca and Alexandru Lavric
Sensors 2021, 21(23), 8093; https://doi.org/10.3390/s21238093 - 3 Dec 2021
Cited by 6 | Viewed by 3436
Abstract
Electric power infrastructure has revolutionized our world and our way of living has completely changed. The necessary amount of energy is increasing faster than we realize. In these conditions, the grid is forced to run against its limitations, resulting in more frequent blackouts. [...] Read more.
Electric power infrastructure has revolutionized our world and our way of living has completely changed. The necessary amount of energy is increasing faster than we realize. In these conditions, the grid is forced to run against its limitations, resulting in more frequent blackouts. Thus, urgent solutions need to be found to meet this greater and greater energy demand. By using the internet of things infrastructure, we can remotely manage distribution points, receiving data that can predict any future failure points on the grid. In this work, we present the design of a fully reconfigurable wireless sensor node that can sense the smart grid environment. The proposed prototype uses a modular developed hardware platform that can be easily integrated into the smart grid concept in a scalable manner and collects data using the LoRaWAN communication protocol. The designed architecture was tested for a period of 6 months, revealing the feasibility and scalability of the system, and opening new directions in the remote failure prediction of low voltage/medium voltage switchgears on the electric grid. Full article
(This article belongs to the Special Issue IoT Multi Sensors)
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<p>Technologies identified in the smart grid concept.</p>
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<p>The LoRaWAN communication stack.</p>
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<p>The proposed smart grid architecture based on the LoRaWAN communication.</p>
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<p>Proposed MSMN architecture.</p>
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<p>Hardware implementation of the MSMN node.</p>
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<p>Communication architecture for the coverage estimation.</p>
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<p>LoRaWAN coverage map measurement.</p>
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<p>LoRaWAN coverage map simulation using RadioPlanner.</p>
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<p>LoRaWAN Coverage Scenario 1.</p>
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<p>Losses due to the geographical terrain variation for Scenario 1.</p>
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<p>LoRaWAN Coverage Scenario 2.</p>
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<p>Losses due to the geographical terrain variation for Scenario 2.</p>
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<p>Environmental parameters from the MSMN (temperature, relative humidity, and dew point).</p>
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<p>Ozone level received from the MSMN node.</p>
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10 pages, 2854 KiB  
Communication
Antenna Impedance Matching Using Deep Learning
by Jae Hee Kim and Jinkyu Bang
Sensors 2021, 21(20), 6766; https://doi.org/10.3390/s21206766 - 12 Oct 2021
Cited by 10 | Viewed by 4805
Abstract
We propose a deep neural network (DNN) to determine the matching circuit parameters for antenna impedance matching. The DNN determines the element values of the matching circuit without requiring a mathematical description of matching methods, and it approximates feasible solutions even for unimplementable [...] Read more.
We propose a deep neural network (DNN) to determine the matching circuit parameters for antenna impedance matching. The DNN determines the element values of the matching circuit without requiring a mathematical description of matching methods, and it approximates feasible solutions even for unimplementable inputs. For matching, the magnitude and phase of impedance should be known in general. In contrast, the element values of the matching circuit can be determined only using the impedance magnitude using the proposed DNN. A gamma-matching circuit consisting of a series capacitor and a parallel capacitor was applied to a conventional inverted-F antenna for impedance matching. For learning, the magnitude of input impedance S11 of the antenna was extracted according to the element values of the matching circuit. A total of 377 training samples and 66 validation samples were obtained. The DNN was then constructed considering the magnitude of impedance S11 as the input and the element values of the matching circuit as the output. During training, the loss converged as the number of epochs increased. In addition, the desired matching values for unlearned square and triangular waves were obtained during testing. Full article
(This article belongs to the Special Issue IoT Multi Sensors)
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<p>Antenna structure for impedance simulation.</p>
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<p>Simulated antenna impedance without matching circuit (<b>a</b>) Smith chart, (<b>b</b>) real and imaginary parts.</p>
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<p>Gamma-matching circuit.</p>
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<p>Magnitude of input impedance <span class="html-italic">S</span><sub>11</sub> for (<b>a</b>) all training and (<b>b</b>) all validation samples according to the combination of series capacitor <span class="html-italic">C<sub>S</sub></span> and parallel capacitor <span class="html-italic">C<sub>P</sub></span>.</p>
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<p>Architecture of the proposed DNN for antenna impedance matching.</p>
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<p>Training and validation losses according to epochs.</p>
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<p>Comparison of results from ground truth and DNN output.</p>
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<p>Comparison of ideal <span class="html-italic">S</span><sub>11</sub> waveform with that obtained from DNN results.</p>
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Review

Jump to: Research

18 pages, 1007 KiB  
Review
A Smarter Health through the Internet of Surgical Things
by Francesk Mulita, Georgios-Ioannis Verras, Christos-Nikolaos Anagnostopoulos and Konstantinos Kotis
Sensors 2022, 22(12), 4577; https://doi.org/10.3390/s22124577 - 17 Jun 2022
Cited by 53 | Viewed by 3858
Abstract
(1) Background: In the last few years, technological developments in the surgical field have been rapid and are continuously evolving. One of the most revolutionizing breakthroughs was the introduction of the IoT concept within surgical practice. Our systematic review aims to summarize the [...] Read more.
(1) Background: In the last few years, technological developments in the surgical field have been rapid and are continuously evolving. One of the most revolutionizing breakthroughs was the introduction of the IoT concept within surgical practice. Our systematic review aims to summarize the most important studies evaluating the IoT concept within surgical practice, focusing on Telesurgery and surgical Telementoring. (2) Methods: We conducted a systematic review of the current literature, focusing on the Internet of Surgical Things in Telesurgery and Telementoring. Forty-eight (48) studies were included in this review. As secondary research questions, we also included brief overviews of the use of IoT in image-guided surgery, and patient Telemonitoring, by systematically analyzing fourteen (14) and nineteen (19) studies, respectively. (3) Results: Data from 219 patients and 757 healthcare professionals were quantitively analyzed. Study designs were primarily observational or based on model development. Palpable advantages from the IoT incorporation mainly include less surgical hours, accessibility to high quality treatment, and safer and more effective surgical education. Despite the described technological advances, and proposed benefits of the systems presented, there are still identifiable gaps in the literature that need to be further explored in a systematic manner. (4) Conclusions: The use of the IoT concept within the surgery domain is a widely incorporated but less investigated concept. Advantages have become palpable over the past decade, yet further research is warranted. Full article
(This article belongs to the Special Issue IoT Multi Sensors)
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<p>Connected IoST entities and workflow of an IoST-based Telementoring/Telesurgery System.</p>
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<p>Connected IoST entities and workflow of an IoST-based Image-Guided Surgical System.</p>
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<p>Connected IoST entities and workflow of an IoST-based Telemonitoring System.</p>
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