Reliability Monitoring of GNSS Aided Positioning for Land Vehicle Applications in Urban Environments

Cette thèse porte sur les défis en matière de surveillance de la fiabilité de la navigation par GNSS pour les applications de véhicules terrestres dans les milieux urbains. L'objectif principal de cette recherche est de développer des méthodes de positionnement avec confiance en utilisant des mesures GNSS et des mesures de confiance pour l'utilisateur dans des environnements urbains contraintes. Dans la première partie de la thèse, les erreurs NLOS en milieu urbain sont caractérisées par un modèle 3D de l'environnement urbain. Dans la deuxième partie de la thèse, nous avons proposé une technique de surveillance de la fiabilité dans le domaine des mesures GNSS pour l'environnement urbain en utilisant un capteur de vitesse fiable. Enfin, nous avons développé une nouvelle expérimentale de surveillance de l'intégrité pour le positionnement en milieu urbain. En surveillant de la statistique de test contre un seuil spécifique, l'intégrité et la continuité de positionnement sont fixés à un certain niveau de confiance. En outre, le calcul de niveau de protection horizontale (HPL) en utilisant une approche composite a également été proposé.This thesis addresses the challenges in reliability monitoring of GNSS aided navigation for land vehicle applications in urban environments. The main objective of this research is to develop methods of trusted positioning using GNSS measurements and confidence measures for the user in constrained urban environments. In the first part of the thesis, the NLOS errors in urban settings are characterized by means of a 3D model of the urban surrounding. For the second part of the thesis, the work proposes a reliability monitoring technique in the range domain for urban environ ment using a trusted velocity sensor. Finally, the research developed a novel experimental scheme in integrity monitoring for positioning in urban environment. By monitoring the test statistic against a specific threshold, the positioning integrity and continuity are met at a certain level of confidence. In addition, the Horizontal Protection Level (HPL) computation using a composite approach has also been proposed

Characterization of GNSS Receiver Position Errors for User Integrity Monitoring in Urban Environments

International audienceThe characterization of GNSS position errors in urban environments is an important issue for integrity monitoring and classification of receivers’ performance. However, these errors are not observable directly by the receiver, therefore RAIM methods use statistics based on the pseudorange residuals (i.e. observable errors). In this work, we focus on the modelling and analysis of navigation errors in the position-domain rather than individual range-domain errors that are difficult to model in urban environments due to multipath and non-line-of-sight (NLOS) signals. Using a trajectory of reference we compute the horizontal position errors (HPE) and its non-parametric distribution function given by the empirical Cumulative Distribution Function (CDF). According to the results, we observe that these errors have a heavy-tailed distribution, and then we propose to fit the empirical CDF with the CDF of the generalized Pareto distribution (GPD). We use an inflated version of the fitted Pareto model to overbound the CDF of the HPE for the calculation of Horizontal Protection Level (HPL), i.e., bounding the radial position errors

A secure and dependable trust assessment (SDTS) scheme for industrial communication networks

Abstract Due to tamper-resistant sensor nodes and wireless media, Industrial Wireless Sensor Networks (WSNs) are susceptible to various security threats that severely affect industrial/business applications. The survival of sensor networks is highly dependent on the flourishing collaboration of sensor nodes. Trust management schemes seem to be realistic and promising techniques to improve security as well as cooperation (dependability) among sensor nodes by estimating the trust level (score) of individual sensor nodes. This research paper presents a well-organized and motivating secure, dependable trust assessment (SDTS) scheme for industrial WSNs to cope with unexpected behavior such as an on–off attack, bad-mouthing attack, garnished attack, etc., by employing robust trust evaluation components based on success ratio and node misbehaviour. SDTS incorporates an interesting trust evaluation function in which the trust range can be adjusted in accordance with the application requirement. SDTS include direct communication trust, indirect communication trust, data trust, and misbehavior-based trust to defend the multiple internal attacks. SDTS works according to the behavior of nodes, i.e., whether the sensor nodes are interacting frequently or not. Moreover, abnormal attenuation and dynamic slide lengths are incorporated in the proposed model (SDTS) to deal with various natural calamities and internal attacks. SDTS is compared against three recent state-of-the-art methods and found efficient in terms of ease of trust assessment, false-positive rate (2.5%), false-negative rate (2%), attack detection rate (90%), detection accuracy (91%), average energy consumption (0.40 J), and throughput (108 Kbps) under the load of 500 sensor nodes with 50% malicious nodes. Investigational results exhibit the potency of the proposed scheme

Employing an Energy Harvesting Strategy to Enhance the Performance of a Wireless Emergency Network

Establishing a wireless communication network (WCN) is critical to saving people’s lives during disasters. Since the user equipment (UE) must transfer their information to the functioning area, their batteries will be significantly drained. Thus, technologies that can compensate for battery power consumption, such as the energy harvesting (EH) strategy, are highly required. This paper proposes a framework that employs EH at the main cluster head (MCH) selected by the enhanced clustering technique (CFT) and simultaneously transmits information and power wirelessly to prolong the lifetime of the energy-constrained network. MCH harvests energy from the radio frequency signal via the relay station (RS) and uses the harvested energy for D2D communications. The suggested framework was evaluated by analyzing the EH outage probability and estimating the energy efficiency performance, which is expected to improve the stability of the network. Compared to the UAV scenario, the simulation findings show that when RS is in its optimal location, it enhances the network EH outage probability performance by 26.3%. Finally, integrating CFT with wireless communications links into cellular networks is an effective technique for maintaining communication services for mission-critical applications

PAPQ: Predictive analytics of product quality in industry 4.0

In e-commerce, Industry 4.0 is all about combining analytics, artificial intelligence, and machine learning to simplify procedures and enable product quality review. In addition, the importance of anticipating client behavior in the context of e-commerce is growing as individuals migrate from visiting physical businesses to shopping online. By providing a more personalized purchasing experience, it can increase consumer satisfaction and sales, leading to improved conversion rates and competitive advantage. Using data from e-commerce platforms such as Flipkart and Amazon, it is possible to build models for forecasting customer behavior. This study examines machine learning techniques for product quality prediction and gives an insight into the performance differences of machine learning-based models by doing descriptive data analysis and training each model separately on three datasets viz Mobile, Health Equipments, and Book Datasets. Support Vector Machine, Nave Bayes, k-Nearest Neighbors, Random Forest, and Random Tree were the machine learning methods utilized in this work. The results indicate that a Support Vector Machine Model provides the greatest fit for the prediction task, with the best performance, reasonable latency, comprehensibility, and resilience for the first two datasets, but Random Forest provides the highest performance for the Book dataset

Fast hybrid-MixNet for security and privacy using NTRU algorithm

Security and privacy-enhancing techniques are developed in order to provide strong protection over the Internet. These techniques aim to enable the users to keep their identities secret during the communication when they are forwarding an email, making payments online, and web browsing or posting to newsgroups. MixNet is the most practical solution for concealing the identities of messages and senders. The anonymous channel contains numerous routing protocols that consist of anonymous messages forwarding between nodes. The structure of MixNet is also based on this way to achieve anonymity. After the proposed work of Chaumian MixNet, many practical implementations of MixNet using many approaches and techniques are accomplished until now. In this paper, we propose the hybrid MixNet using NTRU asymmetric cryptosystem. We designed a mechanism for anonymous communication in which more people can participate in their secure communication. It hides the relation between input and output in each phase of Mix servers. The performance of Hybrid MixNet using NTRU is better than Hybrid Mixnet using ElGamal and ECC. In our proposed system, Hybrid MixNet using NTRU takes average time 16.4 milliseconds while Hybrid MixNet using ElGamal and ECC take 93.4 milliseconds and 182.2 milliseconds respectively

An Experimental Investigation on the Effect of Ferrous Ferric Oxide Nano-Additive and Chicken Fat Methyl Ester on Performance and Emission Characteristics of Compression Ignition Engine

In recent years, industries have been investing to develop a potential alternative fuel to substitute the depleting fossil fuels which emit noxious emissions. Present work investigated the effect of ferrous ferric oxide nano-additive on performance and emission parameters of compression ignition engine fuelled with chicken fat methyl ester blends. The nano-additive was included with various methyl ester blends at different ppm of 50, 100, and 150 through the ultrasonication process. Probe sonicator was utilized for nano-fuel preparation to inhibit the formation of agglomeration of nanoparticles in base fuel. Experimental results revealed that the addition of 100 ppm dosage of ferrous ferric oxide nanoparticles in blends significantly improves the combustion performance and substantially decrease the pernicious emissions of the engine. It is also found from an experimental results analysis that brake thermal efficiency (BTE) improved by 4.84%, a reduction in brake specific fuel consumption (BSFC) by 10.44%, brake specific energy consumption (BSEC) by 9.44%, exhaust gas temperature (EGT) by 19.47%, carbon monoxides (CO) by 53.22%, unburned hydrocarbon (UHC) by 21.73%, nitrogen oxides (NOx) by 15.39%, and smoke by 14.73% for the nano-fuel B20FFO100 blend. By seeing of analysis, it is concluded that the doping of ferrous ferric oxide nano-additive in chicken fat methyl ester blends shows an overall development in engine characteristics

Impact of ZnO nanoparticles as additive on performance and emission characteristics of a diesel engine fueled with waste plastic oil

Neat waste plastic oil (WPO) application as a fuel in engines reduces BTE and increases deleterious emissions of CO, UHC, NOx, and smoke due to the presence of insufficient oxygen and unbreakable hydrocarbon chains in WPO. Present investigation was performed to evaluate the impact of ZnO nanoparticles on the performance and emission characteristics of a diesel engine operated with the waste plastic oil (WPO20) blend. The objective of doping ZnO nanoparticles with WPO20 was to enhance the oxidation reaction and heat transfer rate between fuel droplets during combustion, which aids in completing the combustion. The sol-gel technique was adopted to successfully synthesize the ZnO nanoparticles using zinc acetate (Zn(CH3CO2)2.2H2O) and sodium hydroxide (NaOH) precursors. The structure and morphology of resulted particles were studied by XRD and FESEM tests. Both results indicate the stable formation of ZnO, and exhibit the crystallinity nature, spherical surface, and size consistency. The synthesized ZnO nanoparticles were infused in WPO20 blend in the amounts of 50, 100, and 150 ppm with the aid of the ultrasonication technique. Engine test was conducted with diesel fuel, WPO20 blend, and nano-infused fuels at a constant speed of 1500 rpm under various loads. The disparities in performance and emission characteristics were examined and compared with pure diesel fuel. The findings demonstrated that adding nanoparticles to WPO20 significantly lowers the smoke, CO, UHC, and NOx emissions and simultaneously improves the BTE and decreases the BSFC of the diesel engine. Optimum results were obtained for 100 ppm concentration of ZnO nanoparticles. Reduction of smoke by 11.86%, CO by 5.7%, UHC by 28%, and NOx by 14.93%, along with the enhancement of BTE by 2.47%, were noticed at maximum load with 100 ppm particles. Based on the test results, it is concluded that ZnO nanoparticles can be used as a suitable additive in WPO blends to improve the overall engine characteristics. Further scope of the present work is to study the effect of organic nanoparticles with WPO on engine behaviour, the detailed combustion of nanoparticles infused WPO, and the nanoparticles doped WPO on engine wear and corrosion