Boreal Forest Low Antenna Height Propagation Measurements

Smith, Devin P.; Messier, Geoffrey G.; Wasson, Michael W.

doi:10.1109/tap.2016.2583490

Cited by 17 publications

(16 citation statements)

References 21 publications

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“…(37) where p, n, and m are respectively neuron numbers of the three layers. According to the reality, α is chosen in [1,10], p is set to be 10.…”

Section: E Data Preprocessing and Parameter Settingmentioning

confidence: 99%

“…WSN is a kind of net constructing by some sensors which have possibility of perception, computing, communication, and organizing through Ad Hoc, these nodes collaborate with one another [1][2][3][4][5][6], monitor, perceive, and collect every various environmental data in real-time, we can fuse these data to get detail information, then transmit them to the users [7][8]. Comparing with traditional wireless communication network, WSN shows selforganization, low-cost, and dynamic, so that it can be deployed in the forest [9][10], desert [11], battle field [12][13], and other hostile environments, as well as intelligent home [14][15], factory [16][17], traffic [18][19], and hospital [20][21], their number can be a few to tens of thousands.…”

Section: Introductionmentioning

confidence: 99%

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Number of Coherent Nodes Estimation Based on Multiple Feature Extraction in Wireless Sensor Network

Zhen

Zhang

2020

IEEE Access

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Knowing number of nodes is the precondition of their locating and estimation of other parameters in wireless sensor network, actually it can be evaluated according to the signal received by the sensor array. The popular algorithms are only appropriate for the uncorrelated signals and circumstance of Gaussian white noise, the estimation precision will deteriorate at some harsh environments. Hence a new algorithm for determining coherent node number based on multiple feature extraction in wireless sensor network is presented in this paper. First, perform spatial difference smoothing to the array data. Then the eigenvalues and eigenvectors can be acquired by eigen-decomposition, consequently multiple features which are used for training are constructed. Finally, back propagation neural network and particle swarm optimization are exploited for calculating node number. Simulation results demonstrate that the algorithms has good performances under the background of colored noise and small samples.

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“…(37) where p, n, and m are respectively neuron numbers of the three layers. According to the reality, α is chosen in [1,10], p is set to be 10.…”

Section: E Data Preprocessing and Parameter Settingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Number of Coherent Nodes Estimation Based on Multiple Feature Extraction in Wireless Sensor Network

Zhen

Zhang

2020

IEEE Access

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“…where P t and P r are the power values of the transmitter and receiver, respectively. The path loss in free-space is also given in (8). where f is the frequency in GHz and d is the distance between transmitter and receiver in meters.…”

Section: The Path Loss Model Based On Volumetric Occupancy Ratementioning

confidence: 99%

“…The scenario, which includes the receiver and transmitter at different heights, determines the effect of ground effect on the path loss. Besides, some studies are shown that the antenna height should be particularly taken into account [7][8][9]. Also, at higher frequencies, since the branches and leaves of the trees are larger than the wavelength, there can be more scattering, and propagation loss increase [2,9].…”

Section: Introductionmentioning

confidence: 99%

A new path loss model based on the volumetric occupancy rate for the pine forests at 5G frequency band

Genç

2020

Int. J. Microw. Wireless Technol.

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In this paper, a new empirical path loss model based on frequency, distance, and volumetric occupancy rate is generated at the 3.5 and 4.2 GHz in the scope of 5G frequency bands. This study aims to determine the effect of the volumetric occupancy rate on path loss depending on the foliage density of the trees in the pine forest area. Using 4.2 GHz and the effect of the volumetric occupancy rate contributes to the literature in terms of novelty. Both the reference measurements to generate a model and verification measurements to verify the proposed models are conducted in three different regions of the forest area with double ridged horn antennas. These regions of the artificial forest area consist of regularly sorted and identical pine trees. Root mean square error (RMSE) and R-squared values are calculated to evaluate the performance of the proposed model. For 3.5 and 4.2 GHz, while the RMSEs are 3.983 and 3.883, the values of R-squared are 0.967 and 0.963, respectively. Additionally, the results are compared with four path loss models which are commonly used in the forest area. The proposed one has the best performance among the other models with values 3.98 and 3.88 dB for 3.5 and 4.2 GHz.

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“…Nowadays WSN has become a research hotspot in forest information monitoring field because of its miniaturization and integration [15][16][17]. Certainly, it still faces some difficulties due to the complexity and particularity in forestry application environment, especially the power supply problem: it is very troublesome to replace the batteries as conventional WSN always takes batteries as the energy storage unit.…”

Section: Pbn System Designmentioning

confidence: 99%

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

Yin

Zhu

et al. 2018

Mobile Information Systems

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The purpose of this paper is to represent a living-tree biological energy powered wireless sensor system and introduce a novel energy aware MAC protocol based on remaining energy level, energy harvesting status, and application requirements. Conventional wireless sensor network (WSN) cannot have an infinite lifetime without battery recharge or replacement. Energy harvesting (EH), from environmental energy sources, is a promising technology to provide sustainable powering for WSN. In this paper, a sensor network system has been developed which uses living-tree bioenergy as harvesting resource and super capacitor as energy storage. Moreover, by analyzing the power recharging, task arrangement, and energy consumption rate, a novel duty cycle-based energy-neutral MAC protocol is proposed. It dynamically optimizes each wireless sensor node’s duty cycle to create a balanced, efficient, and continuous network. The scheme is implemented in a plant surface-mounted bioenergy power wireless sensor node system called PBN, which aims to monitoring the plant’s growth parameters. The results show that the proposed MAC protocol can provide sustainable and reliable data transmission under ultralow and dynamic power inputs; it also significantly improves the latency and packet loss probability compared with other MAC protocols for EH-WSN.

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Boreal Forest Low Antenna Height Propagation Measurements

Cited by 17 publications

References 21 publications

Number of Coherent Nodes Estimation Based on Multiple Feature Extraction in Wireless Sensor Network

Number of Coherent Nodes Estimation Based on Multiple Feature Extraction in Wireless Sensor Network

A new path loss model based on the volumetric occupancy rate for the pine forests at 5G frequency band

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

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