Ocean propagation models

It is possible to produce mobile phone worms, which are computer viruses with the ability to command the running of cell phones by taking advantage of their flaws, to be transmitted from one device to the other with increasing numbers. In our day, one of the services to gain currency for circulating these malignant worms is SMS. The distinctions of computers from mobile devices render the existing propagation models of computer worms unable to start operating instantaneously in the mobile network, and this is particularly valid for the SMS framework. The susceptible–affected–infectious–suspended–recovered model with a classical derivative (abbreviated as SAIDR) was coined by Xiao et al., (2017) in order to correctly estimate the spread of worms by means of SMS. This study is the first to implement an Atangana–Baleanu (AB) derivative in association with the fractional SAIDR model, depending upon the SAIDR model. The existence and uniqueness of the drinking model solutions together with the stability analysis are shown through the Banach fixed point theorem. The special solution of the model is investigated using the Laplace transformation and then we present a set of numeric graphics by varying the fractional-order θ with the intention of showing the effectiveness of the fractional derivative.

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Opportunistic Large Array Propagation Models: A Comprehensive Survey

Sensors ◽

10.3390/s21124206 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4206

Author(s):

Farhan Nawaz ◽

Hemant Kumar ◽

Syed Ali Hassan ◽

Haejoon Jung

Keyword(s):

Energy Efficient ◽

Large Scale ◽

Performance Metrics ◽

Experimental Studies ◽

Cooperative Transmission ◽

Analytical Models ◽

Future Research ◽

Large Array ◽

Propagation Models ◽

Comprehensive Survey

Enabled by the fifth-generation (5G) and beyond 5G communications, large-scale deployments of Internet-of-Things (IoT) networks are expected in various application fields to handle massive machine-type communication (mMTC) services. Device-to-device (D2D) communications can be an effective solution in massive IoT networks to overcome the inherent hardware limitations of small devices. In such D2D scenarios, given that a receiver can benefit from the signal-to-noise-ratio (SNR) advantage through diversity and array gains, cooperative transmission (CT) can be employed, so that multiple IoT nodes can create a virtual antenna array. In particular, Opportunistic Large Array (OLA), which is one type of CT technique, is known to provide fast, energy-efficient, and reliable broadcasting and unicasting without prior coordination, which can be exploited in future mMTC applications. However, OLA-based protocol design and operation are subject to network models to characterize the propagation behavior and evaluate the performance. Further, it has been shown through some experimental studies that the most widely-used model in prior studies on OLA is not accurate for networks with networks with low node density. Therefore, stochastic models using quasi-stationary Markov chain are introduced, which are more complex but more exact to estimate the key performance metrics of the OLA transmissions in practice. Considering the fact that such propagation models should be selected carefully depending on system parameters such as network topology and channel environments, we provide a comprehensive survey on the analytical models and framework of the OLA propagation in the literature, which is not available in the existing survey papers on OLA protocols. In addition, we introduce energy-efficient OLA techniques, which are of paramount importance in energy-limited IoT networks. Furthermore, we discuss future research directions to combine OLA with emerging technologies.

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Appropriate selection of urban vehicle-to-vehicle radio propagation models

2014 IEEE 21st Symposium on Communications and Vehicular Technology in the Benelux (SCVT) ◽

10.1109/scvt.2014.7046698 ◽

2014 ◽

Cited By ~ 1

Author(s):

Tristan Gaugel ◽

Hannes Hartenstein

Keyword(s):

Radio Propagation ◽

Propagation Models ◽

Vehicle To Vehicle ◽

Selection Of

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Urban soundscape maps modelled with geo-referenced data

Noise Mapping ◽

10.1515/noise-2016-0020 ◽

2016 ◽

Vol 3 (1) ◽

Cited By ~ 7

Author(s):

Catherine Lavandier ◽

Pierre Aumond ◽

Saul Gomez ◽

Catherine Dominguès

Keyword(s):

Information Systems ◽

Geographic Information Systems ◽

Public Space ◽

Road Traffic ◽

Sound Quality ◽

Acoustic Measurements ◽

Propagation Models ◽

Urban Public Space ◽

Eu Directive ◽

The Eu

AbstractThe noise maps that are currently proposed as part of the EU Directive are based on the calculation of the Lday, Levening and Lnight. These levels are calculated from emission and propagation models that are expensive in time. These noise maps are criticized for being distant from the perception of city users. Thus, calculation models of sound quality have been proposed, for being closer to city users’ perception. They are either based on perceptual variables, or on acoustic measurements, or on georeferenced data, the latter being often already integrated into the Geographic Information Systems of most French metropolises. Considering 89 Parisian situations, this article proposes to compare the sound quality really perceived, with those from models using geo-referenced data. It also looks at the modeling of perceptual variables that influence the sound quality, such as perceived loudness, the perceived time ratio of traffic, voices and birds. To do this, such geo-referenced data as road traffic, the presence of gardens, food shops, restaurants, bars, schools, markets, are transformed into core densities. Being quick and easy to calculate, these densities predict effectively sound quality in the urban public space. Visualization of urban soundscape maps are proposed in this paper.

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