Big Data Analytics for Wireless and Wired Network Design: A Survey

Currently, the world is witnessing a mounting avalanche of data due to the increasing number of mobile network subscribers, Internet websites, and online services. This trend is continuing to develop in a quick and diverse manner in the form of big data. Big data analytics can process large amounts of raw data and extract useful, smaller-sized information, which can be used by different parties to make reliable decisions. In this paper, we conduct a survey on the role that big data analytics can play in the design of data communication networks. Integrating the latest advances that employ big data analytics with the networks’ control/traffic layers might be the best way to build robust data communication networks with refined performance and intelligent features. First, the survey starts with the introduction of the big data basic concepts, framework, and characteristics. Second, we illustrate the main network design cycle employing big data analytics. This cycle represents the umbrella concept that unifies the surveyed topics. Third, there is a detailed review of the current academic and industrial efforts toward network design using big data analytics. Forth, we identify the challenges confronting the utilization of big data analytics in network design. Finally, we highlight several future research directions. To the best of our knowledge, this is the first survey that addresses the use of big data analytics techniques for the design of a broad range of networks

The statistical behavior of PM10 events over guadeloupean archipelago: stationarity, modelling and extreme events

International audienc

Particle Size Analysis of African Dust Haze over the Last 20 Years: A Focus on the Extreme Event of June 2020

Over the last decades, the impact of mineral dust from African deserts on human health and climate has been of great interest to the scientific community. In this paper, the climatological analysis of dusty events of the past 20 years in the Caribbean area has been performed using a particulate approach. The focus is made on June 2020 extreme event dubbed “Godzilla”. To carry out this study, different types of data were used (ground-based, satellites, model, and soundings) on several sites in the Caribbean islands. First, the magnitude of June 2020 event was clearly highlighted using satellite imagery. During the peak of this event, the value of particulate matter with an aerodynamic diameter of less than 10 μμm (PM10) reached a value 9 times greater than the threshold recommended by the World Health Organization in one day. Thereafter, the PM10, the aerosol optical depth, and the volume particle size distribution analyses exhibited their maximum values for June 2020. We also highlighted the exceptional characteristics of the Saharan air layer in terms of thickness and wind speed for this period. Finally, our results showed that the more the proportion of particulate matter with an aerodynamic diameter of less than 2.5 μμm (PM2.5) in PM10 increases, the more the influence of sea salt aerosols is significant

THE RELATIONSHIP BETWEEN COASTAL WEST AFRICAN DUST LEVEL AND CARIBBEAN ISLAND DUST

International audienc

Desert dust over a Caribbean island with North West Atlantic air masses

International audienc

Investigation of local correlations between particulate matter ( P M 10 ) and air temperature in the Caribbean basin using Ensemble Empirical Mode Decomposition

International audienc

Quantifying Spatio-Temporal Dynamics of African Dust Detection Threshold for PM10 Concentrations in the Caribbean Area Using Multiscale Decomposition

International audienceDue to African dust, the Caribbean area is known to have one of the highest incidences of asthma on the planet. Consequently, it is crucial to dissociate the impact of local sources from large scale sources in this region. The aim of this study was to estimate the PM 10 detection threshold for dusty events using a statistical approach and a dynamic approach. To carry out this analysis, PM 10 time series from Martinique (MAR), Guadeloupe (GPE) and Puerto-Rico (PR) were used between 2006 and 2016. The statistical analysis highlighted that the distance from the African coast is a key feature for PM 10 concentrations distribution with the highest at MAR (26.52 μg/m 3 ) and the lowest at PR (24.42 μg/m 3 ). The probability density function analysis showed that MAR-GPE-PR distributions converge towards a same point between the first and the second maximum probability value at 28 μg/m 3 . The dynamical analysis with the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and the Improved CEEMDAN (ICEEMDAN) validated the 28 μg/m 3 found with the statistical analysis. The analysis of HYSPLIT back trajectories confirmed this threshold. Thus, our results indicated that 28 μg/m 3 is the PM 10 detection threshold for African dust in the Caribbean basin. It will therefore be a good indicator allowing the competent authorities to take the appropriate decisions to protect vulnerable populations during dusty events