Path-Loss Modeling for Wireless Sensor Networks: A review of models and comparative evaluations

Kurt, Sinan; Tavlı, Bülent

doi:10.1109/map.2016.2630035

Cited by 139 publications

(75 citation statements)

References 57 publications

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“…However, there is a trade-off between accuracy and simulation speed: the more accurate we want to be, the slower the simulation will be and so at every level, from the SoC evaluation [4] to the radio model [5]. Moreover, some models are not exempt from bugs and some others are not accurate enough because of abstractions, simplifications and underestimated effects, especially for wireless communications [6], [7].…”

Section: Introductionmentioning

confidence: 99%

FlexNode: a reconfigurable Internet of Things node for design evaluation

Patrigeon

Leloup

Benoit

et al. 2019

2019 IEEE Sensors Applications Symposium (SAS)

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Accurate evaluation of Ultra Low Power Systems on Chip (ULP SoC) is a huge challenge for designers and developers. In embedded applications, especially for Internet of Things endnode devices, ULP SoCs have to interact with their environment, but modelling a complete SoC and the peripheral components, their interaction and low-power policies, can be very complex in terms of developments and benchmarking. In order to cope with this challenge, an approach is to implement the desired system on FPGA with a monitoring infrastructure dedicated to fast and accurate evaluation. This paper presents a reconfigurable prototyping platform used for SoC architecture exploration and real-time application evaluation.

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Section: Introductionmentioning

confidence: 99%

FlexNode: a reconfigurable Internet of Things node for design evaluation

Patrigeon

Leloup

Benoit

et al. 2019

2019 IEEE Sensors Applications Symposium (SAS)

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“…These sensor nodes are responsible from acquiring measurements on physical phenomena and conveying the data towards the sink node that collects, filters, aggregates, and transports the refined information to other entities for further processing. Since sensor nodes have limited battery energy, every aspect of WSNs should be designed with utmost care to dissipate the limited energy to maximize the network lifetime . In general, WSNs can be categorized into 4 broad classes according to the deployment environments: terrestrial WSNs (TWSNs), underwater WSNs (UWSNs), wireless underground sensor networks (WUSNs), and body area sensor networks (BASNs).…”

Section: Introductionmentioning

confidence: 99%

A survey on packet size optimization for terrestrial, underwater, underground, and body area sensor networks

Yiğit

Yildiz

Kurt

et al. 2018

Int J Communication

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Summary Packet size optimization is a critical issue in wireless sensor networks (WSNs) for improving many performance metrics (eg, network lifetime, delay, throughput, and reliability). In WSNs, longer packets may experience higher loss rates due to harsh channel conditions. On the other hand, shorter packets may suffer from greater overhead. Hence, the optimal packet size must be chosen to enhance various performance metrics of WSNs. To this end, many approaches have been proposed to determine the optimum packet size in WSNs. In the literature, packet size optimization studies focus on a specific application or deployment environment. However, there is no comprehensive and recent survey paper that categorizes these different approaches. To address this need, in this paper, recent studies and techniques on data packet size optimization for terrestrial WSNs, underwater WSNs, wireless underground sensor networks, and body area sensor networks are reviewed to motivate the research community to further investigate this promising research area. The main objective of this paper is to provide a better understanding of different packet size optimization approaches used in different types of sensor networks and applications as well as introduce open research issues and challenges in this area.

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“…Transmission of signal through a wireless radio channel is affected by path loss which mainly depends on the distance between the receiver's antenna and transmitter's antenna, antenna's characteristics, and operating frequencies [1][2][3][4][5][6][7][8][9][10]. Furthermore, the behaviors of obstructing objects in the radio channel such as walls, terrain, buildings, vegetation, and other objects have an impact on the path loss [3,[11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…Since signal through a wireless radio channel propagates through environments where it can be reflected, scattered, and diffracted by walls, terrain, buildings, and other objects, full information of signal transmission through wireless radio channels can only be calculated by solving Maxwell's equations with boundary conditions that express the physical characteristics of these obstructing objects [1,2,4,12]. Since the calculation of Maxwell's equations is difficult and the necessary parameters (permeability μ and permittivity ε) are often not available, there are a number of studies in the literature [5][6][7][8][9][10] to approximate a radio wave propagation without adopting Maxwell's equations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Radio Wave Propagation Modeling Method Using System Identification Technique over Wireless Links in East Africa

Girma¹,

Konditi

Maina

2018

International Journal of Antennas and Propagation

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Transmission of a radio signal through a wireless radio channel is affected by refraction, diffraction and reflection, free space loss, object penetration, and absorption that corrupt the originally transmitted signal before radio wave arrives at a receiver antenna. Even though there are many factors affecting wireless radio channels, there are still a number of radio wave propagation models such as Okumura, Hata, free space model, and COST-231 to predict the received signal level at the receiver antenna. However, researchers in the field of radio wave propagation argue that there is no universally accepted propagation model to guarantee a universal recommendation. Thus, this research is aimed at determining the difference between the measured received signal levels and the received signal level calculated from the free space propagation model. System identification method has been proposed to determine this unknown difference. Measured received signal levels were collected from three randomly selected urban areas in Ethiopia using a computer, Nemo test tool, Actix software, Nokia phone, and GPS. The result from the simulations was validated against the received experimental signal level measurement taken in a different environment. From the simulation results, the mean square error (MSE) was 4.169 dB, which is much smaller than the minimum acceptable MSE value of 6 dB for good signal propagation, and 74.76% fit to the estimation data. The results clearly showed that the proposed radio wave propagation model predicts the received signal levels at 900 MHz and 1800 MHz in the study region.

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Path-Loss Modeling for Wireless Sensor Networks: A review of models and comparative evaluations

Cited by 139 publications

References 57 publications

FlexNode: a reconfigurable Internet of Things node for design evaluation

FlexNode: a reconfigurable Internet of Things node for design evaluation

A survey on packet size optimization for terrestrial, underwater, underground, and body area sensor networks

A Novel Radio Wave Propagation Modeling Method Using System Identification Technique over Wireless Links in East Africa

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