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Energy Harvesting in Environmental Wireless Sensor Networks

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

Deadline for manuscript submissions: 25 November 2024 | Viewed by 10978

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Special Issue Editors

Dr. Michal Prauzek

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Guest Editor

Department of Cybernetics and Biomedical Engineering, VSB—Technical University of Ostrava, 708 00 Ostrava, Czech Republic
Interests: adaptive sensing; edge computing; energy harvesting; environmental monitoring; low-power electronics; machine learning; optimization methods

Prof. Dr. Petr Musilek

E-Mail Website
Guest Editor

Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
Interests: artificial intelligence; data analysis; energy harvesting; energy management; environmental monitoring; optimization methods

Prof. Dr. Darius Andriukaitis

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Guest Editor

Faculty of Electrical and Electronics Engineering, Department of Electronics Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania
Interests: energy harvesting; interactive electronic systems; electric vehicles; integrated information systems; indirect measurement methods; reinforcement learning
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Special Issue Information

Dear Colleagues,

The emergence of new sensors and monitoring devices brings new possibilities for research and operations in environmental monitoring. Measuring and collecting physical and biotic parameters improve our understanding of the environment and how it changes.

A number of environments not only require specific types of measurements, but also have extensive requirements regarding the performance of environmental wireless sensor networks (EWSNs). Additionally, wireless environmental sensor networks are commonly installed in remote or inaccessible locations. This creates even more stringent requirements of higher reliability and energy independence in terms of energy harvesting options.

The aim of this Special Issue is to gather the latest original research and review articles on energy harvesting in EWSNs. The topics of this Special Issue will include, but are not limited to:

Adaptive operation and sensing;
Data collection and cloud computing;
Data transmission technologies and optimization;
Edge computing and data compression methods;
Energy harvesting technologies;
Energy-efficient algorithms;
Energy management algorithms;
Energy storages and supercapacitors;
Fault and maintenance prediction;
Internet of Things and 5G networks;
Machine learning approaches;
Node coverage optimization;
Operation reliability analysis;
Optimal hardware and software designs;
Prediction of harvested energy;
Rapid prototyping and simulations;
Self-learning designs;
Signal processing methods.

Dr. Michal Prauzek
Prof. Dr. Petr Musilek
Prof. Dr. Darius Andriukaitis
Guest Editors

Manuscript Submission Information

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

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

Published Papers (7 papers)

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Research

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13 pages, 4418 KiB

Open AccessArticle

Evaluation of a Machine Learning Algorithm to Classify Ultrasonic Transducer Misalignment and Deployment Using TinyML

by Des Brennan and Paul Galvin

Sensors 2024, 24(2), 560; https://doi.org/10.3390/s24020560 - 16 Jan 2024

Viewed by 598

Abstract

The challenge for ultrasonic (US) power transfer systems, in implanted/wearable medical devices, is to determine when misalignment occurs (e.g., due to body motion) and apply directional correction accordingly. In this study, a number of machine learning algorithms were evaluated to classify US transducer misalignment, based on data signal transmissions between the transmitter and receiver. Over seven hundred US signals were acquired across a range of transducer misalignments. Signal envelopes and spectrograms were used to train and evaluate machine learning (ML) algorithms, classifying misalignment extent. The algorithms included an autoencoder, convolutional neural network (CNN) and neural network (NN). The best performing algorithm, was deployed onto a TinyML device for evaluation. Such systems exploit low power microcontrollers developed specifically around edge device applications, where algorithms were configured to run on low power, restricted memory systems. TensorFlow Lite and Edge Impulse, were used to deploy trained models onto the edge device, to classify signals according to transducer misalignment extent. TinyML deployment, demonstrated near real-time (<350 ms) signal classification achieving accuracies > 99%. This opens the possibility to apply such ML alignment algorithms to US arrays (capacitive micro-machined ultrasonic transducer (CMUT), piezoelectric micro-machined ultrasonic transducer (PMUT) devices) capable of beam-steering, significantly enhancing power delivery in implanted and body worn systems. Full article

(This article belongs to the Special Issue Energy Harvesting in Environmental Wireless Sensor Networks)

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20 pages, 4769 KiB

Open AccessArticle

A Measurement Method of Power Transferred to an Electric Vehicle Using Wireless Charging

by Žilvinas Nakutis, Robertas Lukočius, Viktoras Girdenis and Kaspars Kroičs

Sensors 2023, 23(24), 9636; https://doi.org/10.3390/s23249636 - 05 Dec 2023

Cited by 2 | Viewed by 891

Abstract

The increasing number of zero-emission vehicles on the roads demands novel vehicle charging solutions that ensure convenience, safety, increased charging infrastructure availability, and aesthetics. Wireless charging technology is seen as the one that could assure these desirable properties and could be applied not just in conventional implementations but also in off-grid solutions together with roadway energy harvesting systems. Both approaches require proper transfer of energy metering methods. In this paper, a method for measuring the power transferred to the load in a wireless charging system is presented, and its systematic error is assessed in the relevant range of influencing factors. The novelty of the method is that it does not require any metrologically certified measurement instrumentation on the receiver side of the wireless charging system. The error analysis is performed using a numerical simulation. Considered error-influencing factors included secondary side electrical load, coils’ coupling coefficient and quality factor, current and voltage quantization resolution, and compensation topology type (serial-serial (SS) and serial-parallel (SP)). It was determined that the systematic error of the power assessment does not exceed 0.7% for SS and 1.1% for SP topologies when the coupling coefficient is in the range of 0.05 to 0.4 and the quality factor of the resonant system is in the range of 100 to 800. Full article

(This article belongs to the Special Issue Energy Harvesting in Environmental Wireless Sensor Networks)

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26 pages, 4858 KiB

Open AccessArticle

Discussion on Secure Standard Network of Sensors Powered by Microbial Fuel Cells

by Helbert da Rocha, Paolo Caruso, João Pereira, Pedro Serra and Antonio Espirito Santo

Sensors 2023, 23(19), 8227; https://doi.org/10.3390/s23198227 - 03 Oct 2023

Viewed by 825

Abstract

Everyday tasks use sensors to monitor and provide information about processes in different scenarios, such as monitoring devices in manufacturing or homes. Sensors need to communicate, with or without wires, while providing secure information. Power can be derived from various energy sources, such as batteries, electrical power grids, and energy harvesting. Energy harvesting is a promising way to provide a sustainable and renewable source to power sensors by scavenging and converting energy from ambient energy sources. However, low energy is harvested through these methods. Therefore, it is becoming a challenge to design and deploy wireless sensor networks while ensuring the sensors have enough power to perform their tasks and communicate with each other through careful management and optimization, matching energy supply with demand. For this reason, data cryptography and authentication are needed to protect sensor communication. This paper studies how energy harvested with microbial fuel cells can be employed in algorithms used in data protection during sensor communication. Full article

(This article belongs to the Special Issue Energy Harvesting in Environmental Wireless Sensor Networks)

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20 pages, 6706 KiB

Open AccessArticle

Investigation of Self-Powered IoT Sensor Nodes for Harvesting Hybrid Indoor Ambient Light and Heat Energy

by Heng Xiao, Nanjian Qi, Yajiang Yin, Shijie Yu, Xiangzheng Sun, Guozhe Xuan, Jie Liu, Shanpeng Xiao, Yuan Li and Yizheng Li

Sensors 2023, 23(8), 3796; https://doi.org/10.3390/s23083796 - 07 Apr 2023

Cited by 10 | Viewed by 2533

Abstract

Sensor nodes are critical components of the Internet of Things (IoT). Traditional IoT sensor nodes are typically powered by disposable batteries, making it difficult to meet the requirements for long lifetime, miniaturization, and zero maintenance. Hybrid energy systems that integrate energy harvesting, storage, and management are expected to provide a new power source for IoT sensor nodes. This research describes an integrated cube-shaped photovoltaic (PV) and thermal hybrid energy-harvesting system that can be utilized to power IoT sensor nodes with active RFID tags. The indoor light energy was harvested using 5-sided PV cells, which could generate 3 times more energy than most current studies using single-sided PV cells. In addition, two vertically stacked thermoelectrical generators (TEG) with a heat sink were utilized to harvest thermal energy. Compared to one TEG, the harvested power was improved by more than 219.48%. In addition, an energy management module with a semi-active configuration was designed to manage the energy stored by the Li-ion battery and supercapacitor (SC). Finally, the system was integrated into a 44 mm × 44 mm × 40 mm cube. The experimental results showed that the system was able to generate a power output of 192.48 µW using indoor ambient light and the heat from a computer adapter. Furthermore, the system was capable of providing stable and continuous power for an IoT sensor node used for monitoring indoor temperature over a prolonged period. Full article

(This article belongs to the Special Issue Energy Harvesting in Environmental Wireless Sensor Networks)

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10 pages, 1454 KiB

Open AccessArticle

Wireless Capacitive Liquid-Level Detection Sensor Based on Zero-Power RFID-Sensing Architecture

by Shaheen Ahmad, Ramin Khosravi, Ashwin K. Iyer and Rashid Mirzavand

Sensors 2023, 23(1), 209; https://doi.org/10.3390/s23010209 - 25 Dec 2022

Cited by 4 | Viewed by 2110

Abstract

In this paper, a new method for the wireless detection of liquid level is proposed by integrating a capacitive IDC-sensing element with a passive three-port RFID-sensing architecture. The sensing element transduces changes in the liquid level to corresponding fringe-capacitance variations, which alters the phase of the RFID backscattered signal. Variation in capacitance also changes the resonance magnitude of the sensing element, which is associated with a high phase transition. This change in the reactive phase is used as a sensing parameter by the RFID architecture for liquid-level detection. Practical measurements were conducted in a real-world scenario by placing the sensor at a distance of approximately 2 m (with a maximum range of about 7 m) from the RFID reader. The results show that the sensor node offers a high sensitivity of 2.15°/mm to the liquid-level variation. Additionally, the sensor can be used within or outside the container for the accurate measurement of conductive- or non-conductive-type liquids due to the use of polyethylene coating on the sensitive element. The proposed sensor increases the reliability of the current level sensors by eliminating the internal power source as well as complex signal-processing circuits, and it offers real-time response, linearity, high sensitivity, and excellent repeatability, which are suitable for widespread deployment of sensor node applications. Full article

(This article belongs to the Special Issue Energy Harvesting in Environmental Wireless Sensor Networks)

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13 pages, 4293 KiB

Open AccessArticle

Erroneous Vehicle Velocity Estimation Correction Using Anisotropic Magnetoresistive (AMR) Sensors

by Donatas Miklusis, Vytautas Markevicius, Dangirutis Navikas, Mantas Ambraziunas, Mindaugas Cepenas, Algimantas Valinevicius, Mindaugas Zilys, Krzysztof Okarma, Inigo Cuinas and Darius Andriukaitis

Sensors 2022, 22(21), 8269; https://doi.org/10.3390/s22218269 - 28 Oct 2022

Cited by 4 | Viewed by 979

Abstract

Magnetic field sensors installed in the road infrastructure can be used for autonomous traffic flow parametrization. Although the main goal of such a measuring system is the recognition of the class of vehicle and classification, velocity is the essential parameter for further calculation and it must be estimated with high reliability. In-field test campaigns, during actual traffic conditions, showed that commonly accepted velocity estimation methods occasionally produce highly erroneous results. For anomaly detection, we propose a criterion and two different correction algorithms. Non-linear signal rescaling and time-based segmentation algorithms are presented and compared for faulty result mitigation. The first one consists of suppressing the highly distorted signal peaks and looking for the best match with cross-correlation. The second approach relies on signals segmentation according to the feature points and multiple cross-correlation comparisons. The proposed two algorithms are evaluated with a dataset of over 300 magnetic signatures of a vehicle from unconstraint traffic conditions. Results show that the proposed criteria highlight all greatly faulty results and that the correction algorithms reduce the maximum error by twofold, but due to the increased mean error, mitigation technics shall be used explicitly with distorted signals. Full article

(This article belongs to the Special Issue Energy Harvesting in Environmental Wireless Sensor Networks)

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Review

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26 pages, 1017 KiB

Open AccessReview

IoT Sensor Challenges for Geothermal Energy Installations Monitoring: A Survey

by Michal Prauzek, Tereza Kucova, Jaromir Konecny, Monika Adamikova, Karolina Gaiova, Miroslav Mikus, Pavel Pospisil, Darius Andriukaitis, Mindaugas Zilys, Birgitta Martinkauppi and Jiri Koziorek

Sensors 2023, 23(12), 5577; https://doi.org/10.3390/s23125577 - 14 Jun 2023

Cited by 1 | Viewed by 2022

Abstract

Geothermal energy installations are becoming increasingly common in new city developments and renovations. With a broad range of technological applications and improvements in this field, the demand for suitable monitoring technologies and control processes for geothermal energy installations is also growing. This article identifies opportunities for the future development and deployment of IoT sensors applied to geothermal energy installations. The first part of the survey describes the technologies and applications of various sensor types. Sensors that monitor temperature, flow rate and other mechanical parameters are presented with a technological background and their potential applications. The second part of the article surveys Internet-of-Things (IoT), communication technology and cloud solutions applicable to geothermal energy monitoring, with a focus on IoT node designs, data transmission technologies and cloud services. Energy harvesting technologies and edge computing methods are also reviewed. The survey concludes with a discussion of research challenges and an outline of new areas of application for monitoring geothermal installations and innovating technologies to produce IoT sensor solutions. Full article

(This article belongs to the Special Issue Energy Harvesting in Environmental Wireless Sensor Networks)

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