A Safety-Aware Location Privacy-Preserving IoV Scheme with Road Congestion-Estimation in Mobile Edge Computing

By leveraging the conventional Vehicular Ad-hoc Networks (VANETs), the Internet of Vehicles (IoV) paradigm has attracted the attention of different research and development bodies. However, IoV deployment is still at stake as many security and privacy issues are looming; location tracking using overheard safety messages is a good example of such issues. In the context of location privacy, many schemes have been deployed to mitigate the adversary’s exploiting abilities. The most appealing schemes are those using the silent period feature, since they provide an acceptable level of privacy. Unfortunately, the cost of silent periods in most schemes is the trade-off between privacy and safety, as these schemes do not consider the timing of silent periods from the perspective of safety. In this paper, and by exploiting the nature of public transport and role vehicles (overseers), we propose a novel location privacy scheme, called OVR, that uses the silent period feature by letting the overseers ensure safety and allowing other vehicles to enter into silence mode, thus enhancing their location privacy. This scheme is inspired by the well-known war strategy “Give up a Pawn to Save a Chariot”. Additionally, the scheme does support road congestion estimation in real time by enabling the estimation locally on their On-Board Units that act as mobile edge servers and deliver these data to a static edge server that is implemented at the cell tower or road-side unit level, which boosts the connectivity and reduces network latencies. When OVR is compared with other schemes in urban and highway models, the overall results show its beneficial use

Synthese et application d'oligomeres telecheliques aux resines polyurethannes et polyurees

SIGLEINIST T 77057 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Numerical simulation of fluidised cohesive powders

International audienc

Photocatalytic decolorization of Gentian Violet with Na-doped (SnO2 and ZnO)

Photocatalysis is a technique used for the purification and decolorization of water. In this work, the photocatalytic decolorization of aqueous solutions of Gentian Violet has been investigated. The photocatalysts used for the study are tin dioxide (SnO2) and zinc oxide (ZnO) doped with sodium and prepared by sol-gel process. Photocatalysts were synthesised by sol-gel process and characterized by several techniques such as X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), Brunauer, Emmett and Teller (BET) method and UV-vis Spectroscopy. The results of photocatalytic activity of gentian violet degradation under ultraviolet irradiation, indicated that the synthesised photocatalyst exhibit good photocatalytic performance

Synthesis, characterization and photocatalytic properties of alkali metals doped tin dioxide

In order to improve the photocatalytic properties of tin dioxide, crystallized powders of SnO(2) photocatalysts doped by alkali metals (Li, Na and K) were synthesized by sol-gel process. The physical properties of these materials were characterized by X-ray diffraction, nitrogen adsorption-desorption, Scanning electron microscopy and Ultraviolet-visible diffuse reflection spectroscopy. The photocatalytic tests under UV radiation conducted on four aromatic compounds (phenol, paranitrophenol, pentachlorophenol and benzoic acid) showed that tin dioxide modified by sodium possesses good photocatalytic activity; The Li-doped SnO(2) is moderately active, while modification by potassium does not improve this activity. (C) 2011 Elsevier B.V. All rights reserved

Unknown input estimation algorithms for a class of LPV/nonlinear systems with application to wastewater treatment process

International audienceThis paper addresses the problem of unknown input estimation for a class of nonlinear systems with mixed nonlinear terms, namely Linear Parameter Varying (LPV) parts and purely Lipschitz nonlinearities. Three new unknown input estimation algorithms are proposed, where each algorithm depends on the distribution of the unknown inputs in the system. These algorithms provide estimation of the maximum possible unknown inputs in a system, contrarily to the methods available in the literature, which consider only particular cases. Before introducing these estimation algorithms, a general LMI-based [Formula: see text] observer design methodology is provided, as a preliminary result, for a class of nonlinear descriptor systems with nonlinear outputs. To this end, a specific Lyapunov function is exploited to avoid derivatives of the disturbances. The proposed LMI conditions are less conservative than those existing in the literature. This is due to the specific Lyapunov function, the use of Young inequality in a judicious way, and the reformulation of the Lipschitz inequality. The proposed algorithms are applied to a wastewater treatment model to show their effectiveness and performances

Analysis of groundwater quality in the lower Soummam Valley, North-East of Algeria

Analysis of groundwater quality in the alluvial aquifer of the lower Soummam Valley, North-East of Algeria, was realised through the application of multivariate statistical methods: hierarchical cluster analysis (HCA) in Q and R modes, factorial correspondence analysis (FCA), and principal component analysis (PCA), to hydrochemical data from 51 groundwater samples, collected from 17 boreholes during periods of June, September 2016 and March 2017. The objectives of this approach are to characterise the water quality and to know the factors which govern its evolution by processes controlling its chemical composition. The Piper diagram shows two hydrochemical facies: calcium chloride and sodium bicarbonate. Statistical techniques HCA, PCA, and FCA reveal two groups of waters: the first (EC, Ca2+, Mg2+, Cl-, SO42- and NO3-) of evaporitic origin linked to the dissolution processes of limestone rocks, leaching of saliferous soils and anthropogenic processes, namely contamination wastewater and agricultural activity, as well marine intrusion; and the second group (Na+, K+, and HCO3-) of carbonated origin influenced by the dissolution of carbonate formations and the exchange of bases. The hermodynamic study has shown that all groundwater is undersaturated with respect to evaporitic minerals. On the other hand, it is supersaturated with respect to carbonate minerals, except for water from boreholes F9, F14, and F16, which possibly comes down to the lack of dissolution and arrival of these minerals. The results of this study clearly demonstrate the utility of multivariate statistical methods in the analysis of groundwater quality

APOLLO: A Proximity-Oriented, Low-Layer Orchestration Algorithm for Resources Optimization in Mist Computing

The fusion of satellite technologies with the Internet of Things (IoT) has propelled the evolution of mobile computing, ushering in novel communication paradigms and data management strategies. Within this landscape, the efficient management of computationally intensive tasks in satellite-enabled mist computing environments emerges as a critical challenge. These tasks, spanning from optimizing satellite communication to facilitating blockchain-based IoT processes, necessitate substantial computational resources and timely execution. To address this challenge, we introduce APOLLO, a novel low-layer orchestration algorithm explicitly tailored for satellite mist computing environments. APOLLO leverages proximity-driven decision-making and load balancing to optimize task deployment and performance. We assess APOLLO’s efficacy across various configurations of mist layer devices while employing a round-robin principle for equitable task distribution among the close low-layer satellites. Our findings underscore APOLLO’s promising outcomes in terms of reduced energy consumption, minimized end-to-end delay, and optimized network resource utilization, particularly in targeted scenarios. However, the evaluation also reveals avenues for refinement, notably in CPU utilization and slightly low tasks success rates. Our work contributes substantial insights into advancing task orchestration in satellite-enabled mist computing with more focus on energy and end-to-end sensitive applications, paving the way for more efficient, reliable, and sustainable satellite communication systems