Wireless sensor network coverage measurement and planning in mixed crop farming

Ndzi, David; Harun, A.; Ramli, Fitri M.; Kamarudin, Kamarulzaman; Zakaria, A.; Shakaff, Ali Yeon Md; Jaafar, Mohd Saleh; Zhou, Shikun; Farook, Rohani S. Mohamed

doi:10.1016/j.compag.2014.04.012

Cited by 67 publications

(36 citation statements)

References 25 publications

(26 reference statements)

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“…The ground-reflected signal is available in the first Fresnel zone when h O is greater than or equal to the antenna height. The ground-reflected path has a significant effect on the power of the received signal [58]. For 2.45 GHz, h O is 0.125 m at a distance of 0.5 m between beacons and the subject, 0.958 m at a distance of 30 m, and 1.24 m at a distance of 40 m. Consequently, the height of the beacons nodes must be greater than 1.24 m, and thus the signal from the beacons can arrive intact at the sensor node.…”

Section: Wireless Sensor Network For Elderly Fall Detectionmentioning

confidence: 99%

Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network

et al. 2018

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Falls are the main source of injury for elderly patients with epilepsy and Parkinson's disease. Elderly people who carry battery powered health monitoring systems can move unhindered from one place to another according to their activities, thus improving their quality of life. This paper aims to detect when an elderly individual falls and to provide accurate location of the incident while the individual is moving in indoor environments such as in houses, medical health care centers, and hospitals. Fall detection is accurately determined based on a proposed sensor-based fall detection algorithm, whereas the localization of the elderly person is determined based on an artificial neural network (ANN). In addition, the power consumption of the fall detection system (FDS) is minimized based on a data-driven algorithm. Results show that an elderly fall can be detected with accuracy levels of 100% and 92.5% for line-of-sight (LOS) and non-line-of-sight (NLOS) environments, respectively. In addition, elderly indoor localization error is improved with a mean absolute error of 0.0094 and 0.0454 m for LOS and NLOS, respectively, after the application of the ANN optimization technique. Moreover, the battery life of the FDS is improved relative to conventional implementation due to reduced computational effort. The proposed FDS outperforms existing systems in terms of fall detection accuracy, localization errors, and power consumption.Energies 2018, 11, 2866 2 of 32 to detect emergency conditions and enable caregivers to respond efficiently. A fall is one of the key factors that can lead to injuries and decrease quality of life, at times resulting in the death of elderly persons. People's rate of falling increases with their age. Falls occur frequently in medical health care centers, hospitals, or houses, with approximately 30% of falls causing injury. Falls in hospitals occur in the rooms of the patients (84%) and during transfer from one place to another (19%). Furthermore, the majority of falls occur in areas adjacent to chairs and beds [2]. Most people who experience falls need special care in a nursing home or hospital, thereby restricting their life activities. The hazard issues of fall or slight fall, especially of the elderly, can be aggravated by chronic diseases, such as osteoporosis, delirium, and dementia [3]. The degree of danger from a fall for aging persons is frequently decided by the location of the fall, time of fall detection, duration and time of transfer and rescue services. Therefore, automatic detection of elderly people's falls along with the locations of the incident is important so that medical rescue staff can be dispatched immediately and so that the family of the elderly can be informed about the incident through a specific wireless network or mobile telephone.The development of microelectromechanical technologies allows the integration of different sensors, and a wireless network is commonly used. Wireless sensor networks (WSNs) comprise a number of tiny and small sensor nodes which are deployed ...

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Section: Wireless Sensor Network For Elderly Fall Detectionmentioning

confidence: 99%

Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network

et al. 2018

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“…Recently, the wireless sensor network (WSN) has been applied to gather the variability of environmental parameters in the agriculture fields, thus precision management can be applied to improve the yield [1]. For the gathering application, several sensor nodes are distributed around a wide geographical area in which the reliability of wireless communication in terms of network coverage is essential [2]. [2] showed that information of radio propagation channel parameters such as path loss helps achieve this requirement.…”

Section: Introductionmentioning

confidence: 99%

“…For the gathering application, several sensor nodes are distributed around a wide geographical area in which the reliability of wireless communication in terms of network coverage is essential [2]. [2] showed that information of radio propagation channel parameters such as path loss helps achieve this requirement.…”

Section: Introductionmentioning

confidence: 99%

Path Loss Measurement and Prediction in Outdoor Fruit Orchard for Wireless Sensor Network at 2.4 GHZ Band

Srisooksai¹,

Kaemarungsi²,

Takada³

et al. 2019

PIER C

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This work describes the path loss of radio propagation for wireless sensor network in the outdoor fruit orchard which is one of the common agriculture environments. The measurement was conducted in the jackfruit orchard in the 2.45 GHz band. Unlike other studies conducted in the fruit orchard environments, the variation of path loss over the relative angles between the plant rows and the line-of-sight direction from the transmitter to the receiver is identified. The equivalent vegetation obstruction model is proposed as the function of the equivalent number of trees along the line-of-sight to better represent the angular path loss variation. This leads to the proposal of the path loss prediction approach at any point in the fruit orchard by using a few measurement efforts. This work also introduces the Monte Carlo simulation using the numerical electromagnetic scattering computation called hybrid T-matrix method to evaluate the relative angular vegetation loss of a single tree that is used as the input to determine the equivalent number of trees. The evaluation results suggest that it can further reduce the measurement workload required for the proposed path loss prediction approach.

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“…Moreover, Azevedo et al [6] researched the propagation characteristics of wireless radio in some complex agricultural environments such as forests with dozens of tree species. Ndzi et al [7] conducted the wireless sensor network coverage measurements in a mixed crop farmland, and proposed a log-linear model. The existing empirical model regard the propagation environment as isotropic homogeneous.…”

Section: Introductionmentioning

confidence: 99%

The Irregularity Propagation Characteristics of Radio Signals For Wireless Sensor Network In Farmland

Zhu

Zhang

et al. 2016

ITM Web Conf.

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Abstract. This work aims to investigate the irregular propagation characteristics of wireless sensor network (WSN) at frequency of 433 MHz. Through the analysis of the received signal strength indicator (RSSI), it is found that the variance in received signal strength is largely random, along with a continuous change with incremental changes in direction. For the transceiver distance is 20 m, the packet loss rate (PLR) in all directions is relatively small except for the east direction, indicating that the signal is still strong in all directions. While for the transceiver distance is 40 m, it can be found that there is about 90% packet loss in the east direction. That is, the received signal strength in the east direction is lower than that in the other directions. Moreover, the communication range varies with the degree of receiver direction ranging from 0 to 359. Through the regression analysis in Matlab, we find that the optimal fitting models in different directions are different. The optimal fitting model in east and west direction is the modified exponential decay, and in south and north direction is the linear logarithmic model. The values of R 2 vary from 0.935 to 0.961, and the values of RMSE range from 1.75 to 2.31.

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Wireless sensor network coverage measurement and planning in mixed crop farming

Cited by 67 publications

References 25 publications

Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network

Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network

Path Loss Measurement and Prediction in Outdoor Fruit Orchard for Wireless Sensor Network at 2.4 GHZ Band

The Irregularity Propagation Characteristics of Radio Signals For Wireless Sensor Network In Farmland

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