Energy-Neutral Communication Protocol for Living-Tree Bioenergy-Powered Wireless Sensor Network

Yin, Wenjie; Li, Bowen; Zhu, Yongjun; Liu, Yanyi

doi:10.1155/2018/5294026

Cited by 11 publications

(9 citation statements)

References 24 publications

(35 reference statements)

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“…Whereas it adopts a traditional MPTT scheme and gains a lower bit output. However, our system gains significant enhancement in the next stages with a best-matching TPC based converter, where the wireless sensor node has survived for 1.5 h other than the referenced 1.2 h under the working cycle mentioned in a prior achievement [37]. Moreover, certainly in this "ENO" process, the prediction module takes effect as a foundation stone while consuming a neglectable amount of energy.…”

Section: Power Conversion Efficiency Of the Whole Systemmentioning

confidence: 96%

Predictive Power Management for Wind Powered Wireless Sensor Node

Yin

Zhang

2018

Future Internet

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A conventional Wireless Sensor Network (WSN) cannot have an infinite lifetime without a battery recharge or replacement. Energy Harvesting (EH), from environmental energy sources, is a promising technology to provide sustainable powering for a WSN. In this paper, we propose and investigate a novel predictive energy management framework that combines the Maximal Power Transferring Tracking (MPTT) algorithm, a predictive energy allocation strategy, and a high efficiency transmission power control mechanism: First, the MPTT optimal working point guarantees minimum power loss of the EH-WSN system; Then, by exactly predicting the upcoming available energy, the power allocation strategy regulates EH-nodes’ duty cycle accurately to minimize the power failure time; Ultimately, the transmission power control module further improves energy efficiency by dynamically selecting the optimum matching transmission power level with minimum energy consumption. A wind energy powered wireless sensor system has been equipped and tested to validate the effectiveness of the proposed scheme. Results indicate that compared with other predictive energy managers, the proposed mechanism incurs relatively low power failure time while maintaining a high-energy conversion rate.

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Section: Power Conversion Efficiency Of the Whole Systemmentioning

confidence: 96%

Predictive Power Management for Wind Powered Wireless Sensor Node

Yin

Zhang

2018

Future Internet

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“…Reference 67 developed an efficient novel bio‐waste hybrid EH for abundantly accessible usual self‐arranged collagen leathery fish scale with a high power density of 28.5 μW/cm 2 . Similarly, Reference 68 presented a biomechanical EH for the human natural motion and Reference 69 developed bioenergy harvester from living tree are shown in Figure 9. The bar graph depicted analysis of various parameters for bioenergy harvesters in Figure 10.…”

Section: Energy Harvesting Systems For Wsnsmentioning

confidence: 99%

Energy harvesting in wireless sensor networks: A taxonomic survey

2020

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Wireless sensor networks (WSNs) have aroused the conspectus attention of scholars due to their extensive deployment in the emerging fields of the Internet of Things (IoT's) and self-driven devices. But WSNs technologies having a major bottleneck has been associated with limited energy. Mostly research in WSNs has been focused on minimizing energy usage to extend the survival time of limited power source in a network. Energy harvesting can be addressing its energy-scarcity problem of WSNs, so it is giving popularity to Energy Harvesting in Wireless Sensor Networks (EH-WSNs). The paper presents a comprehensive taxonomic survey on recently energy harvesting techniques and algorithms that proposed by various authors and also examined the work done by the various researchers in the field of EH-WSNs. For the ready reference of the researchers, a concise summary and comparative analysis of various promising techniques for energy harvesting have also been included in the systematic survey. However, many equipment developed using the hybridization method in a singular package to get full advantages of available free energy, are explored in this review. The review on hybrid energy harvesting (HEH) systems can be considered as the originality of this article. However, the outdoor photovoltaics have been provided maximum power density about ≈100 mW/cm 3 , and the piezoelectric harvesters have been given maximum voltage about 325 V but the current in very minute amount. The thermoelectric, rectenna and hybrid energy harvesters (EHs) have been given high efficiency more than 80%. Additionally, hybrid EHs have location/timeindependent characteristics which harnessed power from more than one source that can be became more popular for upcoming leading technologies of self-driven or autonomous devices shifting from battery operated devices. Finally, the survey also identifies often challenges and various significant issues that still essential to be addressed to develop a cost effective, efficient, long-lasting, and almost maintenance-free energy harvesting systems for WSNs along with trail to their possible solutions for future perspectives.

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“…In the works [41,42], the authors notice that the voltage difference between the wooden parts of the trees and the soil, in which they grow. This discovery offered another opportunity to provide power for wireless sensor networks and helped to solve the problem of energy supply.…”

Section: System Designmentioning

confidence: 99%

“…Relying on the related work [42] that the voltage difference between the wooden parts of the trees and the soil in the planted area, we designed the tree-energy generated device. In the design, we use a multi-stage capacitor and multi-switch control power transmission to harvest tree-energy.…”

Section: System Designmentioning

confidence: 99%

Wildfire Detection Using Sound Spectrum Analysis Based on the Internet of Things

Zhang

Gao

Lin

et al. 2019

Sensors

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Wildfire is a sudden and hazardous natural disaster. Currently, many schemes based on optical spectrum analysis have been proposed to detect wildfire, but obstacles in forest areas can decrease the efficiency of spectral monitoring, resulting in a wildfire detection system not being able to monitor the occurrence of wildfire promptly. In this paper, we propose a novel wildfire detection system using sound spectrum analysis based on the Internet of Things (IoT), which utilizes a wireless acoustic detection system to probe wildfire and distinguish the difference in the sound between the crown and the surface fire. We also designed a new power supply unit: tree-energy device, which utilizes the biological energy of the living trees to generate electricity. We implemented sound spectrum analysis on the data collected by sound sensors and then combined our classification algorithms. The results describe that the sound frequency of the crown fire is about 0–400 Hz, while the sound frequency of the surface fire ranges from 0 to 15,000 Hz. However, the accuracy of the classification method is affected by some factors, such as the distribution of sensors, the loss of energy in sound transmission, and the delay of data transmission. In the simulation experiments, the recognition rate of the method can reach about 70%.

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Energy-Neutral Communication Protocol for Living-Tree Bioenergy-Powered Wireless Sensor Network

Cited by 11 publications

References 24 publications

Predictive Power Management for Wind Powered Wireless Sensor Node

Predictive Power Management for Wind Powered Wireless Sensor Node

Energy harvesting in wireless sensor networks: A taxonomic survey

Wildfire Detection Using Sound Spectrum Analysis Based on the Internet of Things

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