Path Loss Results for Wireless Sensor Network Deployment in a Sparse Tree Environment

Alsayyari, Abdulaziz; Aldosary, Abdallah

doi:10.1109/isncc.2019.8909137

“…In the case of processes to evaluate the effectiveness of the proposed new models, the coefficient of determination, also called R 2 , is usually used, which is a widely used metric when applying regressions. In this case, the closer the R 2 is to a value of one, the better the correlation with the real values [ 104 ]. The coefficient of determination is calculated using Equation (12) [ 46 ]:

…”

Section: Discussionmentioning

confidence: 99%

Modeling Radio Wave Propagation for Wireless Sensor Networks in Vegetated Environments: A Systematic Literature Review

Barrios-Ulloa

¹

,

Ariza-Colpas

²

,

Sánchez-Moreno

³

et al. 2022

Sensors

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The use of wireless sensor networks (WSN) for monitoring variables in agricultural environments and natural forests has been increasing in recent years. However, the sizing of these systems is affected by the inaccuracy of the radio wave propagation models used, leading to possible increased costs and measurement errors. This systematic literature review (SLR) aims to identify propagation models widely used in WSN deployments in agricultural or naturally vegetated environments and their effectiveness in estimating signal losses. We also identified today’s wireless technologies most used in precision agriculture (PA) system implementations. In addition, the results of studies focused on the development of new propagation models for different environments are evaluated. Scientific and technical analysis is presented based on articles consulted in different specialized databases, which were selected according to different combinations of criteria. The results show that, in most of the application cases, vegetative models present high error values when estimating attenuation.

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“…In practice, the performance of the algorithm for finding the PBs' positions depends on how well the path loss model fits the actual conditions. For instance, recent measurement campaigns in wireless sensor networks corroborates the dependence of the path loss exponent on the environment's characteristics [39]. Hence, different sub-regions of the network might require different path loss models.…”

Section: Practical Implementation Considerationsmentioning

confidence: 94%

On the Optimal Deployment of Power Beacons for Massive Wireless Energy Transfer

Rosabal,

López,

Alves

et al. 2020

Preprint

0

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Wireless energy transfer (WET) is emerging as an enabling green technology for Internet of Things (IoT) networks. WET allows the IoT devices to wirelessly recharge their batteries with energy from external sources such as dedicated radio frequency transmitters called power beacons (PBs). In this paper, we investigate the optimal deployment of PBs that guarantees a network-wide energy outage constraint. Optimal positions for the PBs are determined by maximizing the average incident power for the worst location in the service area since no information about the sensor deployment is provided. Such network planning guarantees the fairest harvesting performance for all the IoT devices. Numerical simulations evidence that our proposed optimization framework improves the energy supply reliability compared to benchmark schemes. Additionally, we show that although both, the number of deployed PBs and the number of antennas per PB, introduce performance improvements, the former has a dominant role. Finally, our proposal allows to extend the coverage area while keeping the total power budget fixed, which additionally reduces the level of electromagnetic radiation in the vicinity of PBs.

show abstract

“…In practice, the performance of the algorithm for finding the PBs' positions depends on how well the path loss model fits the actual conditions. For instance, recent measurement campaigns in wireless sensor networks corroborate the dependence of the path loss exponent on the environment's characteristics [39]. Hence, different sub-regions of the network might require different path loss models.…”

Section: Practical Implementation Considerationsmentioning

confidence: 97%

On the Optimal Deployment of Power Beacons for Massive Wireless Energy Transfer

Rosabal

¹

,

López

²

,

Alves

³

et al. 2021

IEEE Internet Things J.

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Wireless energy transfer (WET) is emerging as an enabling green technology for Internet of Things (IoT) networks. WET allows the IoT devices to wirelessly recharge their batteries with energy from external sources such as dedicated radio frequency transmitters called power beacons (PBs). In this paper, we investigate the optimal deployment of PBs that guarantees a network-wide energy outage constraint. Optimal positions for the PBs are determined by maximizing the average incident power for the worst location in the service area since no information about the sensor deployment is provided. Such network planning guarantees the fairest harvesting performance for all the IoT devices. Numerical simulations evidence that our proposed optimization framework improves the energy supply reliability compared to benchmark schemes. Additionally, we show that although both, the number of deployed PBs and the number of antennas per PB, introduce performance improvements, the former has a dominant role. Finally, our proposal allows to extend the coverage area while keeping the total power budget fixed, which additionally reduces the level of electromagnetic radiation in the vicinity of PBs.

show abstract

Path Loss Results for Wireless Sensor Network Deployment in a Sparse Tree Environment

Cited by 7 publications

References 17 publications

Modeling Radio Wave Propagation for Wireless Sensor Networks in Vegetated Environments: A Systematic Literature Review

Modeling Radio Wave Propagation for Wireless Sensor Networks in Vegetated Environments: A Systematic Literature Review

On the Optimal Deployment of Power Beacons for Massive Wireless Energy Transfer

On the Optimal Deployment of Power Beacons for Massive Wireless Energy Transfer

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