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Energy Harvesting and Self-Powered Sensors
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Energy Harvesting and Self-Powered Sensors

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

Deadline for manuscript submissions: 20 May 2025 | Viewed by 4812

Special Issue Editors

Prof. Dr. Long Liu

E-Mail Website
Guest Editor
School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China
Interests: internet of things; self-powered sensor; energy harvesting technology; human-machine interaction; micro-nano electronic
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Bingyong Guo

E-Mail Website
Guest Editor
Ocean Institute, Northwestern Polytechnical University, Taicang 215400, China
Interests: wave energy technology; hydrodynamic modelling and control
Special Issues, Collections and Topics in MDPI journals
Dr. Lu Wang

E-Mail Website
Guest Editor
School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: energy harvesting; MEMS sensors and actuators; structural vibration

Special Issue Information

Dear Colleagues,

Energy harvesting technology developments have attracted significant attention from researchers and engineers exploring solutions for microwatt- or milliwatt-level power supplies. Triboelectric, piezoelectric, electromagnetic, and photoelectric technologies, among others, are utilized to harvest renewable energies from the natural environment, industrial production, or human activities. Therefore, sensors, sensor nodes, and even sensing systems have been developed with the highlighted advantage of self-powered ability. Impressive research progress is promising to address the cost-effective considerations of sensor design and deployment.

In light of the above, this Special Issue solicits research involving energy harvesting technologies and self-powered sensors. Related surveys and reviews are also welcome.

Prof. Dr. Long Liu
Prof. Dr. Bingyong Guo
Dr. Lu Wang
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.

Keywords

  • energy harvesting
  • wave energy
  • vibration energy
  • solar energy
  • self-powered sensors
  • wireless sensor networks
  • wearable sensors
  • power management
  • internet of things applications

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Published Papers (6 papers)

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Research

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16 pages, 6429 KiB  
Article
Rotational Triboelectric Nanogenerator with Machine Learning for Monitoring Speed
by Chun Zhang, Junjie Liu, Yilin Shao, Xingyi Ni, Jiaheng Xie, Hongchun Luo and Tao Yang
Sensors 2025, 25(8), 2533; https://doi.org/10.3390/s25082533 - 17 Apr 2025
Viewed by 251
Abstract
The triboelectric nanogenerator (TENG) is an efficient mechanical energy harvesting device that exhibits excellent performance in the fields of micro-nano energy harvesting and self-powered sensing. In practical application scenarios, it is very important to monitor the speed of rotational machinery in real time. [...] Read more.
The triboelectric nanogenerator (TENG) is an efficient mechanical energy harvesting device that exhibits excellent performance in the fields of micro-nano energy harvesting and self-powered sensing. In practical application scenarios, it is very important to monitor the speed of rotational machinery in real time. In order to monitor a wider range of rotational speeds, the TENG based on a machine learning algorithm is designed in this paper. The peak power of the TENG reaches a maximum of 6.6 mW and can instantly light up 65 LEDs connected in series. The results show that machine learning can detect speed, greatly improving the speed detection range. The neural network is trained and tested based on the collected electrical signals at different speeds so as to monitor the health of the machine. For the analysis of the collected experimental data, normalization data and a more practical label assignment method of Gaussian soft coding were considered. The study found that after data normalization, the classification prediction accuracy for different speeds is above 0.9, and the prediction results are stable and efficient. Therefore, the machine learning prediction model for speed monitoring proposed by us can be applied to the early warning and monitoring of rotating machinery speed in actual engineering projects. Full article
(This article belongs to the Special Issue Energy Harvesting and Self-Powered Sensors)
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18 pages, 7319 KiB  
Article
Parametric Aerodynamic Study of Galloping Piezoelectric Energy Harvester with Arcuate Protruding and Depressed Features
by Xiaokang Yang, Bingke Xu, Zhendong Shang, Chunyang Liu, Haichao Cai and Xiangyi Hu
Sensors 2025, 25(6), 1657; https://doi.org/10.3390/s25061657 - 7 Mar 2025
Viewed by 409
Abstract
This study explores the potential effect of a cross-sectional shape with an arcuate protruding and depressed features on the performance. The geometric configurations include two feature types (protruding and depressed), each with six distinct perimeter arrangements and three depths per arrangement, yielding thirty-six [...] Read more.
This study explores the potential effect of a cross-sectional shape with an arcuate protruding and depressed features on the performance. The geometric configurations include two feature types (protruding and depressed), each with six distinct perimeter arrangements and three depths per arrangement, yielding thirty-six different cross-sectional shapes for systematic evaluation. The aerodynamic characteristics and electrical performance are numerically analyzed, using a computational fluid dynamics model and a distributed parameter electromechanical coupling model, respectively. A smooth protruding feature on the front, top, or bottom side suppresses the electrical output; however, when located on the rear side, it significantly increases the slope of the power versus wind speed curve. Depressed features on the rear, top, or bottom side only reduce the critical wind speed and the power enhancement positively correlates with the feature depth. Compared to a square, a harvester with depressed feature on both top and bottom sides exhibits a significant jump in power at the critical wind speed, greatly improving the power. These findings provide important design guidelines for structural optimization of galloping piezoelectric energy harvesters, enabling them to match the wind energy distribution characteristics of specific regions with optimal performance. Full article
(This article belongs to the Special Issue Energy Harvesting and Self-Powered Sensors)
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17 pages, 4042 KiB  
Article
Detecting Excitations of Pipes, Ropes, and Bars Using Piezo Sensors and Collecting Information Remotely
by Matteo Cirillo, Enzo Reali and Giuseppe Soda
Sensors 2025, 25(5), 1444; https://doi.org/10.3390/s25051444 - 27 Feb 2025
Viewed by 420
Abstract
An investigation of a non-invasive method to detect defects and localize excitations in metallic structures is presented. It is shown how signals generated by very sensitive piezo sensor assemblies, secured to the metallic elements, can allow for space localization of excitations and defects [...] Read more.
An investigation of a non-invasive method to detect defects and localize excitations in metallic structures is presented. It is shown how signals generated by very sensitive piezo sensor assemblies, secured to the metallic elements, can allow for space localization of excitations and defects in the analyzed structures. The origin of the piezo excitations are acoustic modes generated by light percussive excitations whose strength is of the order of tenths of a newton and that provide piezo signal amplitudes of a few hundred millivolts. Tests of the detection scheme of the excitations are performed on steel ropes, iron pipes, and bars with lengths in the range of 1–6 m with the sensor output signal shaped in the form of a clean pulse. It is shown that the signals generated by the piezo assemblies, when adequately shaped, can feed the input of an RF transmitter, which in turn transfers information to a remote receiver whose readout allows for remotely analyzing information collected on the metallic elements. Considering the voltage amplitude of the signals (of the order of 300 mV) generated by the piezo sensors as a result of very light percussive excitations, the low power required for transmitting data, and the low cost of the sensing and transmitting assembly, it is conceivable that our devices could detect excitations generated even tens of kilometers away and allow for setting up an array of sensors for controlling in real time the status of pipe networks. Full article
(This article belongs to the Special Issue Energy Harvesting and Self-Powered Sensors)
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21 pages, 13073 KiB  
Article
Research on the Performance of Thermoelectric Self−Powered Systems for Wireless Sensor Based on Industrial Waste Heat
by Yong Jiang, Yupeng Wang, Junhao Yan, Limei Shen and Jiang Qin
Sensors 2024, 24(18), 5983; https://doi.org/10.3390/s24185983 - 15 Sep 2024
Cited by 1 | Viewed by 1487
Abstract
The issue of energy supply for wireless sensors is becoming increasingly severe with the advancement of the Fourth Industrial Revolution. Thus, this paper proposed a thermoelectric self−powered wireless sensor that can harvest industrial waste heat for self−powered operations. The results show that this [...] Read more.
The issue of energy supply for wireless sensors is becoming increasingly severe with the advancement of the Fourth Industrial Revolution. Thus, this paper proposed a thermoelectric self−powered wireless sensor that can harvest industrial waste heat for self−powered operations. The results show that this self−powered wireless sensor can operate stably under the data transmission cycle of 39.38 s when the heat source temperature is 70 °C. Only 19.57% of electricity generated by a thermoelectric power generation system (TPGS) is available for use. Before this, the power consumption of this wireless sensor had been accurately measured, which is 326 mW in 0.08 s active mode and 5.45 μW in dormant mode. Then, the verified simulation model was established and used to investigate the generation performance of the TPGS under the Dirichlet, Neumann, and Robin boundary conditions. The minimum demand for a heat source is cleared for various data transmission cycles of wireless sensors. Low−temperature industrial waste heat is enough to drive the wireless sensor with a data transmission cycle of 30 s. Subsequently, the economic benefit of the thermoelectric self−powered system was also analyzed. The cost of one thermoelectric self−powered system is EUR 9.1, only 42% of the high−performance battery cost. Finally, the SEPIC converter model was established to conduct MPPT optimization for the TEG module and the output power can increase by up to approximately 47%. This thermoelectric self−powered wireless sensor can accelerate the process of achieving energy independence for wireless sensors and promote the Fourth Industrial Revolution. Full article
(This article belongs to the Special Issue Energy Harvesting and Self-Powered Sensors)
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16 pages, 627 KiB  
Article
Enhancing Reliability and Stability of BLE Mesh Networks: A Multipath Optimized AODV Approach
by Muhammad Rizwan Ghori, Tat-Chee Wan, Gian Chand Sodhy, Mohammad Aljaidi, Amna Rizwan, Ali Safaa Sadiq and Omprakash Kaiwartya
Sensors 2024, 24(18), 5901; https://doi.org/10.3390/s24185901 - 11 Sep 2024
Cited by 2 | Viewed by 1454
Abstract
Bluetooth Low Energy (BLE) mesh networks provide flexible and reliable communication among low-power sensor-enabled Internet of Things (IoT) devices, enabling them to communicate in a flexible and robust manner. Nonetheless, the majority of existing BLE-based mesh protocols operate as flooding-based piconet or scatternet [...] Read more.
Bluetooth Low Energy (BLE) mesh networks provide flexible and reliable communication among low-power sensor-enabled Internet of Things (IoT) devices, enabling them to communicate in a flexible and robust manner. Nonetheless, the majority of existing BLE-based mesh protocols operate as flooding-based piconet or scatternet overlays on top of existing Bluetooth star topologies. In contrast, the Ad hoc On-Demand Distance Vector (AODV) protocol used primarily in wireless ad hoc networks (WAHNs) is forwarding-based and therefore more efficient, with lower overheads. However, the packet delivery ratio (PDR) and link recovery time for AODV performs worse compared to flooding-based BLE protocols when encountering link disruptions. We propose the Multipath Optimized AODV (M-O-AODV) protocol to address these issues, with improved PDR and link robustness compared with other forwarding-based protocols. In addition, M-O-AODV achieved a PDR of 88%, comparable to the PDR of 92% for flooding-based BLE, unlike protocols such as Reverse-AODV (R-AODV). Also, M-O-AODV was able to perform link recovery within 3700 ms in the case of node failures, compared with other forwarding-based protocols that require 4800 ms to 6000 ms. Consequently, M-O-AODV-based BLE mesh networks are more efficient for wireless sensor-enabled IoT environments. Full article
(This article belongs to the Special Issue Energy Harvesting and Self-Powered Sensors)
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Review

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30 pages, 17202 KiB  
Review
Exploration of Advanced Applications of Triboelectric Nanogenerator-Based Self-Powered Sensors in the Era of Artificial Intelligence
by Yifeng Su, Dezhi Yin, Xinmao Zhao, Tong Hu and Long Liu
Sensors 2025, 25(8), 2520; https://doi.org/10.3390/s25082520 - 17 Apr 2025
Viewed by 290
Abstract
The integration of Deep Learning with sensor technologies has significantly advanced the field of intelligent sensing and decision making by enhancing perceptual capabilities and delivering sophisticated data analysis and processing functionalities. This review provides a comprehensive overview of the synergy between Deep Learning [...] Read more.
The integration of Deep Learning with sensor technologies has significantly advanced the field of intelligent sensing and decision making by enhancing perceptual capabilities and delivering sophisticated data analysis and processing functionalities. This review provides a comprehensive overview of the synergy between Deep Learning and sensors, with a particular focus on the applications of triboelectric nanogenerator (TENG)-based self-powered sensors combined with artificial intelligence (AI) algorithms. First, the evolution of Deep Learning is reviewed, highlighting the advantages, limitations, and application domains of several classical models. Next, the innovative applications of intelligent sensors in autonomous driving, wearable devices, and the Industrial Internet of Things (IIoT) are discussed, emphasizing the critical role of neural networks in enhancing sensor precision and intelligent processing capabilities. The review then delves into TENG-based self-powered sensors, introducing their self-powered mechanisms based on contact electrification and electrostatic induction, material selection strategies, novel structural designs, and efficient energy conversion methods. The integration of TENG-based self-powered sensors with Deep Learning algorithms is showcased through their groundbreaking applications in motion recognition, smart healthcare, smart homes, and human–machine interaction. Finally, future research directions are outlined, including multimodal data fusion, edge computing integration, and brain-inspired neuromorphic computing, to expand the application of self-powered sensors in robotics, space exploration, and other high-tech fields. This review offers theoretical and technical insights into the collaborative innovation of Deep Learning and self-powered sensor technologies, paving the way for the development of next-generation intelligent systems. Full article
(This article belongs to the Special Issue Energy Harvesting and Self-Powered Sensors)
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