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Energy Harvesting Technologies for Wireless Sensors
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Energy Harvesting Technologies for Wireless Sensors

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 4375

Special Issue Editor

Dr. Vishwas Bedekar

E-Mail Website
Guest Editor
Department of Engineering Technology, Middle Tennessee State University, 1301 E Main St., Murfreesboro, TN 37132, USA
Interests: electronic materials, energy harvesting, piezoelectric materials, magnetoelectric research; wireless sensors

Special Issue Information

Dear Colleagues,

Sensors is pleased to announce a Special Issue on "Energy Harvesting Technologies for Wireless Sensors". This Special Issue aims to explore the advancements and applications of energy harvesting technologies providing on-board solutions for wireless sensors. It seeks to shed light on the innovative solutions and cutting-edge research that contribute to sustainable energy generation and utilization.

This Special Issue invites researchers, scientists, and practitioners from academia and industry to share their original research, reviews, and case studies focusing on the development, implementation, and utilization of energy harvesting technologies from various sources and employing various mechanisms to realize self-powered wireless sensors. This Special Issue aims to foster interdisciplinary collaboration and knowledge exchange to address the challenges and opportunities in this rapidly evolving field.

Contributions to this Special Issue may cover a wide range of topics related to energy harvesting technologies for wireless sensors, including (but not limited to) the following:

  • Piezoelectric energy harvesting systems for wireless sensors.
  • Harvesting energy from ambient sources using smart materials.
  • Wind energy harvesting technologies for wireless sensors.
  • Magnetoelectric sensors and energy harvesters.
  • Broadband energy harvesting devices for sensing applications.
  • Energy management and optimization in smart sensor networks.
  • Integration of energy harvesting with Internet of Things (IoT) devices.
  • Energy-aware data collection and transmission in sensor networks.
  • Energy harvesting for self-powered wireless sensor networks.
  • Multimodal energy harvesting technologies for wireless sensors.

Dr. Vishwas Bedekar
Guest Editor

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
  • wireless sensors
  • energy management
  • energy optimization
  • Internet of Things (IoT)
  • energy-aware data collection
  • self-powered wireless sensor networks
  • multimodal energy harvesting

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Published Papers (3 papers)

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Research

29 pages, 8434 KiB  
Article
Petri-Net-Based Charging Scheduling Optimization in Rechargeable Sensor Networks
by Huaiyu Qin, Wei Ding, Lei Xu and Chenzhi Ruan
Sensors 2024, 24(19), 6316; https://doi.org/10.3390/s24196316 - 29 Sep 2024
Viewed by 361
Abstract
In order to express the energy flow, motion flow, and control flow in wireless rechargeable sensor networks accurately and intuitively, and to maximize the charging benefit of MVs (mobile vehicles), a type of MTS-HACO (Mobile Transition Sequence Hybrid Ant Colony Optimization) is proposed. [...] Read more.
In order to express the energy flow, motion flow, and control flow in wireless rechargeable sensor networks accurately and intuitively, and to maximize the charging benefit of MVs (mobile vehicles), a type of MTS-HACO (Mobile Transition Sequence Hybrid Ant Colony Optimization) is proposed. Firstly, node places are grouped according to the firing time of node’s energy consumption transition to ensure that in each time slot, MV places only enable charging transitions for the node places with lower remaining lifetimes. Then, the FSOMCT (Firing Sequence Optimization of Mobile Charging Transition) problem is formulated under the constraints of MV places capacity, travelling arc weight, charging arc weight, and so on. The elite strategy and the Max–Min Ant Colony system are further introduced to improve the ant colony algorithm, while the improved FWA (fireworks algorithm) optimizes the path constructed by each ant. Finally, the optimal mobile charging transition firing sequence and charging times are obtained, ensuring that MVs have sufficient energy to return to the base station. Simulation results indicate that, compared with the periodic algorithm and the PE-FWA algorithm, the proposed method can improve charging benefit by approximately 48.7% and 26.3%, respectively. Full article
(This article belongs to the Special Issue Energy Harvesting Technologies for Wireless Sensors)
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31 pages, 8804 KiB  
Article
Node Role Selection and Rotation Scheme for Energy Efficiency in Multi-Level IoT-Based Heterogeneous Wireless Sensor Networks (HWSNs)
by Tamoor Shafique, Abdel-Hamid Soliman, Anas Amjad, Lorna Uden and Debi Marie Roberts
Sensors 2024, 24(17), 5642; https://doi.org/10.3390/s24175642 - 30 Aug 2024
Viewed by 2964
Abstract
The emergence of Internet of Things (IoT)-based heterogeneous wireless sensor network (HWSN) technology has become widespread, playing a significant role in the development of diverse human-centric applications. The role of efficient resource utilisation, particularly energy, becomes further critical in IoT-based HWSNs than it [...] Read more.
The emergence of Internet of Things (IoT)-based heterogeneous wireless sensor network (HWSN) technology has become widespread, playing a significant role in the development of diverse human-centric applications. The role of efficient resource utilisation, particularly energy, becomes further critical in IoT-based HWSNs than it was in WSNs. Researchers have proposed numerous approaches to either increase the provisioned resources on network devices or to achieve efficient utilisation of these resources during network operations. The application of a vast proportion of such methods is either limited to homogeneous networks or to a single parameter and limited-level heterogeneity. In this work, we propose a multi-parameter and multi-level heterogeneity model along with a cluster-head rotation method that balances energy and maximizes lifetime. This method achieves up to a 57% increase in throughput to the base station, owing to improved intra-cluster communication in the IoT-based HWSN. Furthermore, for inter-cluster communication, a mathematical framework is proposed that first assesses whether the single-hop or multi-hop inter-cluster communication is more energy efficient, and then computes the region where the next energy-efficient hop should occur. Finally, a relay-role rotation method is proposed among the potential next-hop nodes. Results confirm that the proposed methods achieve 57.44%, 51.75%, and 17.63% increase in throughput of the IoT-based HWSN as compared to RLEACH, CRPFCM, and EERPMS, respectively. Full article
(This article belongs to the Special Issue Energy Harvesting Technologies for Wireless Sensors)
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31 pages, 4611 KiB  
Article
A Fuzzy Logic-Based Directional Charging Scheme for Wireless Rechargeable Sensor Networks
by Yuhan Ma, Chao Sha, Yue Wang, Jingwen Wang and Ruchuan Wang
Sensors 2024, 24(15), 5070; https://doi.org/10.3390/s24155070 - 5 Aug 2024
Viewed by 696
Abstract
Wireless Power Transfer (WPT) has become a key technology to extend network lifetime in Wireless Rechargeable Sensor Networks (WRSNs). The traditional omnidirectional recharging method has a wider range of energy radiation, but it inevitably results in more energy waste. By contrast, the directional [...] Read more.
Wireless Power Transfer (WPT) has become a key technology to extend network lifetime in Wireless Rechargeable Sensor Networks (WRSNs). The traditional omnidirectional recharging method has a wider range of energy radiation, but it inevitably results in more energy waste. By contrast, the directional recharging mode enables most of the energy to be focused in a predetermined direction that achieves higher recharging efficiency. However, the MC (Mobile Charger) in this mode can only supply energy to a few nodes in each direction. Thus, how to set the location of staying points of the MC, its service sequence and its charging orientation are all important issues related to the benefit of energy replenishment. To address these problems, we propose a Fuzzy Logic-based Directional Charging (FLDC) scheme for Wireless Rechargeable Sensor Networks. Firstly, the network is divided into adjacent regular hexagonal grids which are exactly the charging regions for the MC. Then, with the help of a double-layer fuzzy logic system, a priority of nodes and grids is obtained that dynamically determines the trajectory of the MC during each round of service, i.e., the charging sequence. Next, the location of the MC’s staying points is optimized to minimize the sum of charging distances between MC and nodes in the same grid. Finally, the discretized charging directions of the MC at each staying point are adjusted to further improve the charging efficiency. Simulation results show that FLDC performs well in both the charging benefit of nodes and the energy efficiency of the MC. Full article
(This article belongs to the Special Issue Energy Harvesting Technologies for Wireless Sensors)
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