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Journal articles on the topic 'Energy Harvesting and Management'

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Published: 7 July 2024

Last updated: 7 July 2024

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1

S, Kumaravel, Mohamed Thufail H, Manoj Kumar R, Karunyamani V, and Mukesh Kumar M.K. "Energy Harvesting and Management from Ambient RF Radiation." SIJ Transactions on Computer Networks & Communication Engineering 05, no. 02 (April 18, 2017): 05–08. http://dx.doi.org/10.9756/sijcnce/v5i2/05010030101.

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2

Garg, Ritu, and Neha Garg. "Energy Management in a Multi-Source Energy Harvesting IoT System." Journal of Information Technology Research 13, no. 2 (April 2020): 42–59. http://dx.doi.org/10.4018/jitr.2020040103.

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To guarantee the uninterrupted operation of an IoT node, IoT nodes are installed with energy harvesting techniques to prolong their lifetime and recharge their batteries. Mostly energy harvesting systems collect energy from sunlight and wind. However, the energy harvested from the sunlight is non-continuous and energy harvested from the wind is insufficient for continuously powering an IoT node. Thus, to resolve this problem, authors proposed an energy harvesting system namely SWEH which harvests energy from solar light and wind. In this article, authors proposed a scheduling algorithm to balance the energy produced by SWEH and the energy consumption of an IoT node that results in the energy neutral system. Results from simulation analysis clearly manifest that the proposed SWEH system extracts more energy as compared to energy produced by a single solar panel or wind turbine. With the help of simulation results, authors also show that the proposed algorithm leaves the system in energy neutral state at the end of particular time frame.

3

Lee, Kisong, and Hyun-Ho Choi. "Energy-Efficient Resource Management for Energy Harvesting Interference Channel." Journal of Korean Institute of Communications and Information Sciences 44, no. 9 (September 30, 2019): 1682–85. http://dx.doi.org/10.7840/kics.2019.44.9.1682.

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4

Sharma, Vinod, Utpal Mukherji, Vinay Joseph, and Shrey Gupta. "Optimal energy management policies for energy harvesting sensor nodes." IEEE Transactions on Wireless Communications 9, no. 4 (April 2010): 1326–36. http://dx.doi.org/10.1109/twc.2010.04.080749.

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5

Moser, Clemens, Jian-Jia Chen, and Lothar Thiele. "An energy management framework for energy harvesting embedded systems." ACM Journal on Emerging Technologies in Computing Systems 6, no. 2 (June 2010): 1–21. http://dx.doi.org/10.1145/1773814.1773818.

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6

Kansal, Aman, Jason Hsu, Sadaf Zahedi, and Mani B. Srivastava. "Power management in energy harvesting sensor networks." ACM Transactions on Embedded Computing Systems 6, no. 4 (September 2007): 32. http://dx.doi.org/10.1145/1274858.1274870.

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7

Dickson, Andrew Jordan, Sarah Burton, Michael Shepertycky, Yan-Fei Liu, and Qingguo Li. "Digitally Controlled Energy Harvesting Power Management System." IEEE Journal of Emerging and Selected Topics in Power Electronics 4, no. 1 (March 2016): 303–17. http://dx.doi.org/10.1109/jestpe.2015.2489925.

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8

Ju, Qianao, Hongsheng Li, and Ying Zhang. "Power Management for Kinetic Energy Harvesting IoT." IEEE Sensors Journal 18, no. 10 (May 15, 2018): 4336–45. http://dx.doi.org/10.1109/jsen.2018.2820644.

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9

Yun, Eun Jeong, Hyeon Joong Kim, and Chong Gun Yu. "A multi-input energy harvesting system with independent energy harvesting block and power management block." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (December 1, 2021): 1379. http://dx.doi.org/10.11591/ijeecs.v24.i3.pp1379-1391.

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In the conventional approach widely used for multi-input energy harvesting (MIEH), energy harvesting, energy combining, and power conversion are performed integrally in an inductor sharing block through time multiplexing operations, which is not suitable for hot-pluggable systems. In the MIEH system proposed in this paper, an energy harvesting block (EHB) and a power management block (PMB) are independent of each other to increase the modularity of the system. Therefore, the EHB can be optimized to extract maximum power from energy sources, and the PMB can be focused on combining input energies and converting power effectively. This paper mainly focuses on the design and implementation of the EHB. For light, vibration, and thermal energy, the measured peak power efficiencies of the EHB implemented using a 0.35 μm CMOS process are 95.2%, 92.5%, and 95.5%, respectively. To confirm the functionality and effectiveness of the proposed MIEH system, a PMB composed of simple charge pump circuits and a power management unit has also been implemented and verified with the designed EHB.

10

Heo, Kwan-Jun, and Sung-Jin Kim. "Intelligent Energy Harvesting Power Management and Advanced Energy Storage System." Journal of the Korean Institute of Electrical and Electronic Material Engineers 27, no. 7 (July 1, 2014): 417–27. http://dx.doi.org/10.4313/jkem.2014.27.7.417.

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11

Yadav, Animesh, Mathew Goonewardena, Wessam Ajib, Octavia A. Dobre, and Halima Elbiaze. "Energy Management for Energy Harvesting Wireless Sensors With Adaptive Retransmission." IEEE Transactions on Communications 65, no. 12 (December 2017): 5487–98. http://dx.doi.org/10.1109/tcomm.2017.2734882.

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12

Wang, Wensi, Victor Cionca, Ningning Wang, Mike Hayes, Brendan O'Flynn, and Cian O'Mathuna. "Thermoelectric Energy Harvesting for Building Energy Management Wireless Sensor Networks." International Journal of Distributed Sensor Networks 9, no. 6 (January 2013): 232438. http://dx.doi.org/10.1155/2013/232438.

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13

Rodway, James, and Petr Musilek. "Harvesting-Aware Energy Management for Environmental Monitoring WSN." Energies 10, no. 5 (May 1, 2017): 607. http://dx.doi.org/10.3390/en10050607.

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14

Patel, Shwetak, Steve Hodges, and Joseph Paradiso. "Energy Harvesting and Power Management [Guest editors' introduction]." IEEE Pervasive Computing 15, no. 4 (October 2016): 26–27. http://dx.doi.org/10.1109/mprv.2016.73.

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15

Weddell, Alex S., Geoff V. Merrett, Nick R. Harris, and Bashir M. Al-Hashimi. "Energy Harvesting and Management for Wireless Autonomous Sensors." Measurement and Control 41, no. 4 (May 2008): 104–8. http://dx.doi.org/10.1177/002029400804100402.

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16

Pelegri-Sebastia, Jose, Manel Gasulla, and Gennaro Boggia. "Energy harvesting and management for distributed sensor networks." International Journal of Distributed Sensor Networks 13, no. 6 (June 2017): 155014771771283. http://dx.doi.org/10.1177/1550147717712837.

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17

Choi, Hayeon, Youngkyoung Koo, and Sangsoo Park. "Adaptive operating mode management model for efficient energy harvesting systems." International Journal of Distributed Sensor Networks 16, no. 2 (February 2020): 155014772090780. http://dx.doi.org/10.1177/1550147720907801.

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Energy harvesting technology is becoming popular concerning efficient use of Internet of Things devices, which collect energy present in nature and use it to power themselves. Although the technology is eco-friendly, it is dependent on the vagaries of the surrounding environment; the amount of energy produced is sensitive to the weather and terrain, and intermittent power threatens the system’s stability. Thus, it is essential to collect data that can determine the circumstances of the surrounding environment. Furthermore, these systems should be designed efficiently for continuous energy harvesting. This efficiency can vary depending on the system’s configuration. Core voltage levels and frequencies typically influence efficiency. To maximize system efficiency, power management with an appropriate combination of controllable factors is necessary. We design an energy harvesting system for real-time data acquisition. We propose a methodology to guide the optimal operating power stage considering various adjustable factors for efficient operation. Also, we propose an adaptive operating power mode management model, which involves selecting the optimal operating power step and the transition to a low-power mode (LPM) during idle time. The proposed model was applied to an actual energy harvesting system to demonstrate its effectiveness and facilitated the operation of the harvesting system at low power.

18

Arnold, M., C. A. Featherston, Matthew R. Pearson, J. Lees, and Aleksander Kural. "Energy Management Systems for Energy Harvesting in Structural Health Monitoring Applications." Key Engineering Materials 518 (July 2012): 137–53. http://dx.doi.org/10.4028/www.scientific.net/kem.518.137.

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Autonomous structural health monitoring systems with independent power sources and wireless sensor nodes are increasingly seen as the best solution for monitoring a diverse range of machines and structures including pumps, bridges and aircraft. Powering these systems using harvested energy from ambient sources provides an attractive alternative to the use of batteries which may be either inaccessible for routine maintenance or unsuitable (for example in aerospace applications). A number of techniques are currently being considered including harvesting energy from vibration and thermal gradients. Harvesting energy can however lead to a highly variable power supply in opposition to the requirements of a wireless sensor node which requires continuous standby power with an additional capacity for power peaks during transmission of data. A power management system with embedded energy storage is therefore necessary in order to match supply and demand. Due to the low levels of power harvested in a number of applications, an important factor in the design of such a system is its efficiency to ensure sufficient power reaches the sensor node. Based on the requirements for a simple power management system for thermoelectric power harvesting consisting of a rectifier, a DC/DC convertors and a battery, this paper first examines the possibilities in terms of basic components with a number of commercially available units tested and characterised. Potential designs for a management system incorporating these components are then discussed and a blueprint for an optimal system is suggested.

19

Gleonec, Philip-Dylan, Jeremy Ardouin, Matthieu Gautier, and Olivier Berder. "Energy Allocation for LoRaWAN Nodes with Multi-Source Energy Harvesting." Sensors 21, no. 8 (April 19, 2021): 2874. http://dx.doi.org/10.3390/s21082874.

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Many connected devices are expected to be deployed during the next few years. Energy harvesting appears to be a good solution to power these devices but is not a reliable power source due to the time-varying nature of most energy sources. It is possible to harvest energy from multiple energy sources to tackle this problem, thus increasing the amount and the consistency of harvested energy. Additionally, a power management system can be implemented to compute how much energy can be consumed and to allocate this energy to multiple tasks, thus adapting the device quality of service to its energy capabilities. The goal is to maximize the amount of measured and transmitted data while avoiding power failures as much as possible. For this purpose, an industrial sensor node platform was extended with a multi-source energy-harvesting circuit and programmed with a novel energy-allocation system for multi-task devices. In this paper, a multi-source energy-harvesting LoRaWAN node is proposed and optimal energy allocation is proposed when the node runs different sensing tasks. The presented hardware platform was built with off-the-shelf components, and the proposed power management system was implemented on this platform. An experimental validation on a real LoRaWAN network shows that a gain of 51% transmitted messages and 62% executed sensing tasks can be achieved with the multi-source energy-harvesting and power-management system, compared to a single-source system.

20

Guo, Xiao Gang. "Energy Assessment for China Nuclear Waste Based on Energy Harvesting." Advanced Materials Research 893 (February 2014): 738–41. http://dx.doi.org/10.4028/www.scientific.net/amr.893.738.

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Chinas nuclear waste disposal is Developing country in the world in both technology and management mode, especially the spent fuel final disposal plan is the weaknesses. After a brief overview of the China nuclear waste disposal, the article focuses on analysis nuclear waste management experience. China model characteristics include perfect planning and system, the concept of absolute safety and requirements, and in the whole process of the public participation and dialogue. China model brings us a lot of valuable enlightenment for nuclear waste disposal and management in other country.

21

Shevantikar, Pradnesh Amar, Avinash Prabhakar Udata, Vinayak Ravindra Jadhav, Om Ramesh Dixit, and Abhishek Siddharam Korachagao. "Smart Footstep Energy Harvesting System." Journal of Firewall Software‎ and Networking 2, no. 1 (April 5, 2024): 17–19. http://dx.doi.org/10.48001/jofsn.2024.2117-19.

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This research paper introduces a novel approach to footstep energy harvesting by integrating piezoelectric sensors into flooring surfaces. The project aims to generate sustainable electrical power from footsteps, contributing to the development of energy-efficient systems. The proposed system incorporates a unique feature that enables users to easily identify and address sensor failures or damages. The core components of the system include piezoelectric sensors strategically placed in high-traffic areas, an Arduino microcontroller for signal processing and energy management, and a rechargeable battery for energy storage. A rectifier circuit is employed to convert AC signals generated by the sensors to DC, while a voltage regulator ensures a stable output voltage. The energy harvested is stored in the battery, which is connected to a USB charging module capable of charging electronic devices. To enhance the system's usability and maintenance, LEDs are integrated with each piezoelectric sensor. These LEDs act as indicators for sensor status, allowing users to visually identify any damaged or failed sensors. The Arduino code is designed to continuously monitor sensor values and activate the corresponding LED when a sensor falls below a predefined threshold, signaling a potential issue. This unique feature enhances user awareness, simplifies troubleshooting, and aids in the efficient maintenance of the energy harvesting infrastructure. The research focuses on the practical implementation of the system, exploring its effectiveness in real-world scenarios. Various aspects, such as energy generation efficiency, adaptability to different environments, and the ease of sensor failure identification, are evaluated through extensive experimentation. The paper discusses the system's potential applications, including sustainable power generation for small electronic devices and its integration into smart infrastructure. The proposed Smart Footstep Energy Harvesting System with Sensor Failure Identification addresses a crucial aspect of sustainability and smart infrastructure management. The findings from this research contribute to the growing field of energy harvesting technologies and pave the way for future developments in efficient, user-friendly, and maintainable footstep energy harvesting systems.

22

Ma, Kaisheng, Xueqing Li, Huichu Liu, Xiao Sheng, Yiqun Wang, Karthik Swaminathan, Yongpan Liu, Yuan Xie, John Sampson, and Vijaykrishnan Narayanan. "Dynamic Power and Energy Management for Energy Harvesting Nonvolatile Processor Systems." ACM Transactions on Embedded Computing Systems 16, no. 4 (September 15, 2017): 1–23. http://dx.doi.org/10.1145/3077575.

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23

Kassan, Sara, Jaafar Gaber, and Pascal Lorenz. "Autonomous Energy Management System Achieving Piezoelectric Energy Harvesting in Wireless Sensors." Mobile Networks and Applications 25, no. 2 (June 26, 2019): 794–805. http://dx.doi.org/10.1007/s11036-019-01303-w.

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24

Abbas, Muhammad Mazhar, Zia Muhammad, Khalid Saleem, Nazar Abbas Saqib, and Hasan Mahmood. "Energy Harvesting and Management in Wireless Networks for Perpetual Operations." Journal of Circuits, Systems and Computers 24, no. 03 (February 10, 2015): 1550041. http://dx.doi.org/10.1142/s0218126615500413.

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Ad hoc wireless networks are self-generating and self-organizing networks consisting of mobile and static nodes, which are small and have limited power resources. In a typical setup, these nodes communicate with each other through wireless medium and may act as source, destination and/or relaying nodes. As the power of the remote nodes is depleted very quickly, it is important to have a renewable energy source to support the network operations and increase lifetime. The availability of energy from the environment is unpredictable, random and uncertain, therefore energy harvesting with appropriate management plays an important role in continuous operations of ad hoc networks. In this paper, an energy harvesting and management model is presented for ad hoc networks. Along with harvesting energy, the proposed model ensures the connectivity requirements of the network for its perpetual operation.

25

Marchiori, Leonardo, Maria Vitoria Morais, André Studart, António Albuquerque, Luis Andrade Pais, Luis Ferreira Gomes, and Victor Cavaleiro. "Energy Harvesting Opportunities in Geoenvironmental Engineering." Energies 17, no. 1 (December 30, 2023): 215. http://dx.doi.org/10.3390/en17010215.

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Geoenvironmental engineering involves defining solutions for complex problems, such as containment systems management, contaminant transport control, wastewater management, remediation of contaminated sites and valorization of geomaterials and wastes. In the last years, energy harvesting (EH)—or energy scavenging—methods and technologies have been developed to reduce the dependence on traditional energy sources, namely fossil fuels, and nuclear power, also responding to the increase in energy demands for human activities and to fulfill sustainable development goals. EH in geoenvironmental works and the surrounding soil and water environment includes a set of processes for capturing and accumulating energy from several sources considered wasted or unusable associated with soil dynamics; the stress and strain of geomaterials, hydraulic, vibrations, biochemical, light, heating and wind sources can be potential EH systems. Therefore, this work presents a review of the literature and critical analysis on the main opportunities for EH capturing, accumulating and use in geoenvironmental works, among basic electric concepts and mechanisms, analyzing these works in complex conditions involving biological-, chemical-, mechanical-, hydraulic- and thermal-coupled actions, concluding with the main investigation and challenges within geoenvironmental aspects for EH purposes.

26

Zouari, Manel, Slim Naifar, Ghada Bouattour, Nabil Derbel, and Olfa Kanoun. "Energy management based on fractional open circuit and P-SSHI techniques for piezoelectric energy harvesting." tm - Technisches Messen 86, no. 1 (January 28, 2019): 14–24. http://dx.doi.org/10.1515/teme-2017-0121.

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AbstractSelf-powered energy management circuits make energy harvesting converters more efficient and more reliable. This paper presents an improvement of a Maximum Power Point Tracking (MPPT) technique applied on a Parallel Synchronized Switch Harvesting on Inductor (P-SSHI) technique for piezoelectric vibration converters. The aims are to detect the unstable vibrational state, optimize the output voltage and maximize the output power of the piezoelectric transducer.First, the P-SSHI technique is implemented without an MPPT technique. Then, an MPPT technique based on Fractional Open Circuit (FOC) voltage method is implemented. An improvement of the FOC method is proposed to enhance the capability of the Piezoelectric Energy Harvesting (PEH) system. The comparison between different simulation results shows that by using the same input parameters, the maximum efficiency for the PEH system based on the P-SSHI technique implemented without MPPT is 8.82 % whereas the maximum efficiency of the system based on the (FOC) voltage MPPT method is 13.77 %. A significant improvement of the PEH system is obtained by using the modified (FOC) method, where the efficiency reached 24.59 %.

27

Sivakumar, K., and A. Ashikali. "Hierarchical Energy Harvesting Aware Adaptive Fuzzy Routing with Data Compression for Energy Harvesting WSN." International Journal of Membrane Science and Technology 10, no. 5 (October 6, 2023): 834–43. http://dx.doi.org/10.15379/ijmst.v10i5.3552.

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In present scenario of the world moving towards the smart systems. Smart systems and applications are developed using sensor nodes for data collection, data aggregation and decision making process. Wireless sensor nodes are configured in the physical environment for communications between the devices and the user. The number of limitations affecting the performance of wireless sensor based application. The various factors affecting the performance of the WSN based applications, one the major factor is energy efficiency of the sensors. Sensor nodes are battery powered devices and the sensors are dropping their energy during the transmissions. So we need a solution to overcome the energy efficiency issue of WSN based applications. In this paper, we proposed a methodology called Hierarchical energy harvesting adaptive fuzzy routing algorithm with uniform data quality compression to manage the energy efficiency of the sensor nodes. It uses a new energy management framework and allocating the energy budget to sensors and reduces the consumption of each node. The existing energy harvesting approaches only concentrate on find the best path and forward the data to base station. The performance evaluation shows the effective use of proposed method in WSN based applications.

28

Hussein, Dina, Ganapati Bhat, and Janardhan Rao Doppa. "Adaptive Energy Management for Self-Sustainable Wearables in Mobile Health." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 11935–44. http://dx.doi.org/10.1609/aaai.v36i11.21451.

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Wearable devices that integrate multiple sensors, processors, and communication technologies have the potential to transform mobile health for remote monitoring of health parameters. However, the small form factor of the wearable devices limits the battery size and operating lifetime. As a result, the devices require frequent recharging, which has limited their widespread adoption. Energy harvesting has emerged as an effective method towards sustainable operation of wearable devices. Unfortunately, energy harvesting alone is not sufficient to fulfill the energy requirements of wearable devices. This paper studies the novel problem of adaptive energy management towards the goal of self-sustainable wearables by using harvested energy to supplement the battery energy and to reduce manual recharging by users. To solve this problem, we propose a principled algorithm referred as AdaEM. There are two key ideas behind AdaEM. First, it uses machine learning (ML) methods to learn predictive models of user activity and energy usage patterns. These models allow us to estimate the potential of energy harvesting in a day as a function of the user activities. Second, it reasons about the uncertainty in predictions and estimations from the ML models to optimize the energy management decisions using a dynamic robust optimization (DyRO) formulation. We propose a light-weight solution for DyRO to meet the practical needs of deployment. We validate the AdaEM approach on a wearable device prototype consisting of solar and motion energy harvesting using real-world data of user activities. Experiments show that AdaEM achieves solutions that are within 5% of the optimal with less than 0.005% execution time and energy overhead.

29

Miao, Xingyu, and Yongqi Ge. "Energy Management for Energy Harvesting-Based Embedded Systems: A Systematic Mapping Study." Journal of Electrical and Computer Engineering 2020 (October 30, 2020): 1–19. http://dx.doi.org/10.1155/2020/5801850.

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Energy management for energy harvesting-based embedded systems (EHES) is an emerging field, which aims to collect renewable energy from the environment to power an embedded system. In this work, we use the systematic mapping method to study the relevant literature, with the objective of exploring and analysing the state of the art in energy management for EHES, as well as to provide assistance for subsequent literature reviews. To this end, we conducted extensive searches to find articles related to energy harvesting, embedded systems, energy consumption, and energy management. We searched for papers from January 2005 to July 2019 from three mainstream databases, ACM, IEEE Xplore, and Web of Science, and found more than 3000 papers about EHES. Finally, we selected 142 eligible papers. We have completed the system mapping research from five aspects, namely, (1) research type (validation research, evaluation research, solution proposal, philosophical paper, opinion, and experience), (2) research goals (application or theory), (3) application scenarios, (4) tools or methods, and (5) paper distribution, such as publication year and authors’ nationality. The results showed that the major research type of the EHES papers is validation research, accounting for 65%, which indicated research is still in the theoretical stage and many researchers focus on how to improve the efficiency of harvesting energy, develop a reasonable energy supply plan, and adapt EHES for real-world requirements. Furthermore, this work reviews the tools used for EHES. As the future development direction, it is indispensable to provide tools to EHES for research, testing, development, and so on. The results of our analysis provide significant contributions to understanding the existing knowledge and highlighting potential future research opportunities in the EHES field.

30

Cavalheiro, David, Francesc Moll, and Stanimir Valtchev. "TFET-Based Power Management Circuit for RF Energy Harvesting." IEEE Journal of the Electron Devices Society 5, no. 1 (January 2017): 7–17. http://dx.doi.org/10.1109/jeds.2016.2619908.

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31

Abbas, Muhammad Mazhar, Mohamed A. Tawhid, Khalid Saleem, Zia Muhammad, Nazar Abbas Saqib, Hafiz Malik, and Hasan Mahmood. "Solar Energy Harvesting and Management in Wireless Sensor Networks." International Journal of Distributed Sensor Networks 10, no. 7 (January 2014): 436107. http://dx.doi.org/10.1155/2014/436107.

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32

Reddy, Srinivas, and Chandra R. Murthy. "Dual-Stage Power Management Algorithms for Energy Harvesting Sensors." IEEE Transactions on Wireless Communications 11, no. 4 (April 2012): 1434–45. http://dx.doi.org/10.1109/twc.2012.032812.110623.

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33

Ghosh, Sayanti, Soumen Mondal, Sanjay Dhar Roy, and Sumit Kundu. "D2D communication with energy harvesting relays for disaster management." International Journal of Electronics 107, no. 8 (February 19, 2020): 1272–90. http://dx.doi.org/10.1080/00207217.2020.1726488.

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34

Din, A. U., M. Kamran, W. Mahmood, F. U. Din, M. S. Khan, M. T. Gul, and M. A. Abid. "A Multisource Power Management Interface for Energy Harvesting Circuits." Acta Physica Polonica A 135, no. 5 (May 2019): 946–48. http://dx.doi.org/10.12693/aphyspola.135.946.

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35

Fernandez Gambin, Angel, Maria Scalabrin, and Michele Rossi. "Online Power Management Strategies for Energy Harvesting Mobile Networks." IEEE Transactions on Green Communications and Networking 3, no. 3 (September 2019): 721–38. http://dx.doi.org/10.1109/tgcn.2019.2903330.

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36

Stankovic, John A., and Tian He. "Energy management in sensor networks." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 370, no. 1958 (January 13, 2012): 52–67. http://dx.doi.org/10.1098/rsta.2011.0195.

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This paper presents a holistic view of energy management in sensor networks. We first discuss hardware designs that support the life cycle of energy, namely: (i) energy harvesting, (ii) energy storage and (iii) energy consumption and control. Then, we discuss individual software designs that manage energy consumption in sensor networks. These energy-aware designs include media access control, routing, localization and time-synchronization. At the end of this paper, we present a case study of the VigilNet system to explain how to integrate various types of energy management techniques to achieve collaborative energy savings in a large-scale deployed military surveillance system.

37

Salama, Ramiz, and Fadi Al-Turjman. "Sustainable Energy Production in Smart Cities." Sustainability 15, no. 22 (November 17, 2023): 16052. http://dx.doi.org/10.3390/su152216052.

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Finding a method to provide the installed Internet of Things (IoT) nodes with energy that is both ubiquitous and long-lasting is crucial for ensuring continuous smart city optimization. These and other problems have impeded new research into energy harvesting. After the COVID-19 pandemic and the lockdown that all but ended daily activity in many countries, the ability of human remote connections to enforce social distancing became crucial. Since they lay the groundwork for surviving a lockdown, Internet of Things (IoT) devices are once again widely recognised as crucial elements of smart cities. The recommended solution of energy collection would enable IoT hubs to search for self-sustaining energy from ecologically large sources. The bulk of urban energy sources that could be used were examined in this work, according to descriptions made by researchers in the literature. Given the abundance of free resources in the city covered in this research, we have also suggested that energy sources can be application-specific. This implies that energy needs for various IoT devices or wireless sensor networks (WSNs) for smart city automation should be searched for near those needs. One of the important smart, ecological and energy-harvesting subjects that has evolved as a result of the advancement of intelligent urban computing is intelligent cities and societies. Collecting and exchanging Internet of Things (IoT) gadgets and smart applications that improve people’s quality of life is the main goal of a sustainable smart city. Energy harvesting management, a key element of sustainable urban computing, is hampered by the exponential rise of Internet of Things (IoT) sensors, smart apps, and complicated populations. These challenges include the requirement to lower the associated elements of energy consumption, power conservation, and waste management for the environment. However, the idea of energy-harvesting management for sustainable urban computing is currently expanding at an exponential rate and requires attention due to regulatory and economic constraints. This study investigates a variety of green energy-collecting techniques in relation to edge-based intelligent urban computing’s smart applications for sustainable and smart cities. The four categories of energy-harvesting strategies currently in use are smart grids, smart environmental systems, smart transportation systems, and smart cities. In terms of developed algorithms, evaluation criteria, and evaluation environments, this review’s objective is to discuss the technical features of energy-harvesting management systems for environmentally friendly urban computing. For sustainable smart cities, which specifically contribute to increasing the energy consumption of smart applications and human life in complex and metropolitan areas, it is crucial from a technical perspective to examine existing barriers and unexplored research trajectories in energy harvesting and waste management.

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Chen, Yee Ming, and Yi Jen Peng. "Thermal Energy Harvesting Aware Routing for Wireless Body Area Network in Medical Healthcare System." Applied Mechanics and Materials 394 (September 2013): 482–86. http://dx.doi.org/10.4028/www.scientific.net/amm.394.482.

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Extracting an electrical energy from various environmental sources, called energy harvesting (or energyscavenging), has been an issue and attracting researchers attention in energy replenish networks.Thermal energy harvesting holds a promising future for generatinga small amount of electrical power to drive partial circuits in wirelessly communicatingelectronics devices. Reducing power consumption has also become a major challenge in wireless body area network (WBAN). The reliability ofthe WBAN is greatly dependent on the life span of theenergyaware routing in networks.In this paper presented a fuzzy controller for thermal energyaware routing in WBAN.The paper concludes with performance analysis of the thermal energy harvesting aware routing, comparison of fuzzy and other traditional energy management techniques,while also looking at open research areas of thermoelectric harvesting and management for wireless body area networks.

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Cai, Wen, and Ryan L. Harne. "Electrical power management and optimization with nonlinear energy harvesting structures." Journal of Intelligent Material Systems and Structures 30, no. 2 (November 2, 2018): 213–27. http://dx.doi.org/10.1177/1045389x18808390.

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In recent years, great advances in understanding the opportunities for nonlinear vibration energy harvesting systems have been achieved giving attention to either the structural or electrical subsystems. Yet, a notable disconnect appears in the knowledge on optimal means to integrate nonlinear energy harvesting structures with effective nonlinear rectifying and power management circuits for practical applications. Motivated to fill this knowledge gap, this research employs impedance principles to investigate power optimization strategies for a nonlinear vibration energy harvester interfaced with a bridge rectifier and a buck-boost converter. The frequency and amplitude dependence of the internal impedance of the harvester structure challenges the conventional impedance matching concepts. Instead, a system-level optimization strategy is established and validated through simulations and experiments. Through careful studies, the means to optimize the electrical power with partial information of the electrical load is revealed and verified in comparison to the full analysis. These results suggest that future study and implementation of optimal nonlinear energy harvesting systems may find effective guidance through power flow concepts built on linear theories despite the presence of nonlinearities in structures and circuits.

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K.Selvakumar and M. Senthil Kumar. "AMBIENT RF SIGNAL AND HEAT RADIATION ENERGY HARVESTING AND MANAGEMENT." EDXJL International Journal on Innovations and Advanced Research 01, no. 01 (2023): 15–21. http://dx.doi.org/10.59599/edxjl-ijiar.2022.1103.

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The study is about using an RF source to capture energy. The matching circuit is used to transfer the power from the antenna to this location. So that more power can be obtained from the tower, the rectifier circuit converts the incoming RF signal to a DC signal that is supplied into the battery, and efficient rectification boosts the output power. Wind, solar, vibration, heat, and radio frequency (RF) energy harvesting are developing as attractive alternatives to traditional energy resources. Energy harvesting is the method of electronically catching RF signals and heat radiation from a mobile phone, storing the energy in a battery, and using it as needed.

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Akin, Sami, and M. Cenk Gursoy. "On the Energy and Data Storage Management in Energy Harvesting Wireless Communications." IEEE Transactions on Communications 67, no. 11 (November 2019): 8056–71. http://dx.doi.org/10.1109/tcomm.2019.2934451.

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Newell, D., and M. Duffy. "Improved Energy Management System for Low-Voltage, Low-Power Energy Harvesting Sources." Journal of Physics: Conference Series 773 (November 2016): 012106. http://dx.doi.org/10.1088/1742-6596/773/1/012106.

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Babayo, Aliyu Aliyu, Mohammad Hossein Anisi, and Ihsan Ali. "A Review on energy management schemes in energy harvesting wireless sensor networks." Renewable and Sustainable Energy Reviews 76 (September 2017): 1176–84. http://dx.doi.org/10.1016/j.rser.2017.03.124.

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Peng, S., and C. P. Low. "Prediction free energy neutral power management for energy harvesting wireless sensor nodes." Ad Hoc Networks 13 (February 2014): 351–67. http://dx.doi.org/10.1016/j.adhoc.2013.08.015.

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Bengheni, Abdelmalek, Fedoua Didi, and Ilyas Bambrik. "EEM-EHWSN: Enhanced Energy Management Scheme in Energy Harvesting Wireless Sensor Networks." Wireless Networks 25, no. 6 (March 9, 2018): 3029–46. http://dx.doi.org/10.1007/s11276-018-1701-8.

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Kim, Kyung-Bum, Jae Yong Cho, Hamid Jabbar, Jung Hwan Ahn, Seong Do Hong, Sang Bum Woo, and Tae Hyun Sung. "Optimized composite piezoelectric energy harvesting floor tile for smart home energy management." Energy Conversion and Management 171 (September 2018): 31–37. http://dx.doi.org/10.1016/j.enconman.2018.05.031.

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Ascencio-Hurtado, Carlos Roberto, Roberto C. Ambrosio Lázaro, Johan Jair Estrada-López, and Alfonso Torres Jacome. "Review of Si-Based Thin Films and Materials for Thermoelectric Energy Harvesting and Their Integration into Electronic Devices for Energy Management Systems." Eng 4, no. 2 (May 15, 2023): 1409–31. http://dx.doi.org/10.3390/eng4020082.

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Energy harvesters are autonomous systems capable of capturing, processing, storing, and utilizing small amounts of free energy from the surrounding environment. Such energy harvesters typically involve three fundamental stages: a micro-generator or energy transducer, a voltage booster or power converter, and an energy storage component. In the case of harvesting mechanical vibrations from the environment, piezoelectric materials have been used as a transducer. For instance, PZT (lead zirconate titanate) is a widely used piezoelectric ceramic due to its high electromechanical coupling factor. However, the integration of PZT into silicon poses certain limitations, not only in the harvesting stage but also in embedding a power management electronics circuit. On the other hand, in thermoelectric (TE) energy harvesting, a recent approach involves using abundant, eco-friendly, and low-cost materials that are compatible with CMOS technology, such as silicon-based compound nanostructures for TE thin film devices. Thus, this review aims to present the current advancements in the fabrication and integration of Si-based thin-film devices for TE energy harvesting applications. Moreover, this paper also highlights some recent developments in electronic architectures that aim to enhance the overall efficiency of the complete energy harvesting system.

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Wang, Fan, and Jia-Jun Li. "Optimal energy-management of two cylinders under flow-induced motion using dynamic programming." Modern Physics Letters B 33, no. 34 (December 10, 2019): 1950424. http://dx.doi.org/10.1142/s0217984919504244.

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In this paper, we performed a work combining numerical and theoretical studies to investigate the optimal energy-management of two cylinders under flow-induced motion using dynamic programming. First, we considered the energy generation of two freely oscillated cylinders with different configurations at Reynolds number of 200. Then, we constructed a theoretical model to maximize the energy-harvesting capacity of two cylinders using dynamic programming. This work can provide helpful suggestions for designing the energy-harvesting apparatus.

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Dai, Qingbin, Jingui Qian, Shun Li, and Li Tao. "Green Energy Harvesting and Management Systems in Intelligent Buildings for Cost-Effective Operation." Buildings 14, no. 3 (March 12, 2024): 769. http://dx.doi.org/10.3390/buildings14030769.

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Nowadays, the rise of Internet of Things (IoT) devices is driving technological upgrades and transformations in the construction industry, the integration of IoT devices in buildings is crucial for both the buildings themselves and the intelligent cities. However, large-scale IoT devices increase energy consumption and bring higher operating costs to buildings. Therefore, harvesting the ambient cost-effective and clean energy sources is essential for the future development of intelligent buildings. In this work, we investigate the feasibility of integrating a typical triboelectric droplet energy harvester (DEH) into buildings. We demonstrate the energy harvesting capabilities of DEH on different sloped roof surfaces and complex curved building surfaces by simulating rainy weather with various rainfall intensities. The results indicate energy harvesting efficiency increases with larger tilt angles, which guides future smart architectural designs. This work is significant for the future integration of diversified, all-weather green energy collection and management systems, including raindrop energy, wind power generation, and solar energy, which will contribute to energy conservation and cost control in the next generation of smart buildings.

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Elahi, Hassan, Khushboo Munir, Marco Eugeni, Sofiane Atek, and Paolo Gaudenzi. "Energy Harvesting towards Self-Powered IoT Devices." Energies 13, no. 21 (October 22, 2020): 5528. http://dx.doi.org/10.3390/en13215528.

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The internet of things (IoT) manages a large infrastructure of web-enabled smart devices, small devices that use embedded systems, such as processors, sensors, and communication hardware to collect, send, and elaborate on data acquired from their environment. Thus, from a practical point of view, such devices are composed of power-efficient storage, scalable, and lightweight nodes needing power and batteries to operate. From the above reason, it appears clear that energy harvesting plays an important role in increasing the efficiency and lifetime of IoT devices. Moreover, from acquiring energy by the surrounding operational environment, energy harvesting is important to make the IoT device network more sustainable from the environmental point of view. Different state-of-the-art energy harvesters based on mechanical, aeroelastic, wind, solar, radiofrequency, and pyroelectric mechanisms are discussed in this review article. To reduce the power consumption of the batteries, a vital role is played by power management integrated circuits (PMICs), which help to enhance the system’s life span. Moreover, PMICs from different manufacturers that provide power management to IoT devices have been discussed in this paper. Furthermore, the energy harvesting networks can expose themselves to prominent security issues putting the secrecy of the system to risk. These possible attacks are also discussed in this review article.