SPIN: Simulated Poisoning and Inversion Network for Federated Learning-Based 6G Vehicular Networks

The applications concerning vehicular networks benefit from the vision of beyond 5G and 6G technologies such as ultra-dense network topologies, low latency, and high data rates. Vehicular networks have always faced data privacy preservation concerns, which lead to the advent of distributed learning techniques such as federated learning. Although federated learning has solved data privacy preservation issues to some extent, the technique is quite vulnerable to model inversion and model poisoning attacks. We assume that the design of defense mechanism and attacks are two sides of the same coin. Designing a method to reduce vulnerability requires the attack to be effective and challenging with real-world implications. In this work, we propose simulated poisoning and inversion network (SPIN) that leverages the optimization approach for reconstructing data from a differential model trained by a vehicular node and intercepted when transmitted to roadside unit (RSU). We then train a generative adversarial network (GAN) to improve the generation of data with each passing round and global update from the RSU, accordingly. Evaluation results show the qualitative and quantitative effectiveness of the proposed approach. The attack initiated by SPIN can reduce up to 22% accuracy on publicly available datasets while just using a single attacker. We assume that revealing the simulation of such attacks would help us find its defense mechanism in an effective manner.Comment: 6 pages, 4 figure

AI-enabled privacy-preservation phrase with multi-keyword ranked searching for sustainable edge-cloud networks in the era of industrial IoT

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Towards soft real-time fault diagnosis for edge devices in industrial IoT using deep domain adaptation training strategy

Abstract: Artificial intelligence and industrial internet of things (IIoT) have been rejuvenating the fault diagnosis systems in Industry 4.0 for avoiding major financial losses caused by faults in rotating machines. Meanwhile, the diagnostic systems are provided with a number of sensory inputs that introduce variations in input space which causes difficulty for the algorithms in edge devices. This issue is generally dealt with bi-view cross-domain learning approach. We propose a soft real-time fault diagnosis system for edge devices using domain adaptation training strategy. The investigation is carried out using deep learning models that can learn representations irrespective of input dimensions. A comparative analysis is performed on a publicly available dataset to evaluate the efficacy of the proposed approach which achieved accuracy of 88.08%. The experimental results show that our method using long short-term memory network achieves the best results for the bearing fault detection in an IIoT environmental setting. © 2021 Elsevier Inc. All rights reserve

DBNS: A Distributed Blockchain-Enabled Network Slicing Framework for 5G Networks

5G technology is expected to enable many innovative applications in different verticals. These applications have heterogeneous performance requirements (e.g., high data rate, low latency, high reliability, and high availability). In order to meet these requirements, 5G networks endorse network flexibility through the deployment of new emerging technologies, mainly network slicing and mobile edge computing. This article introduces a distributed blockchain-enabled network slicing (DBNS) framework that enables service and resource providers to dynamically lease resources to ensure high performance for their end-to-end services. The key component of our framework is global service provisioning, which provides admission control for incoming service requests along with dynamic resource assignment by means of a blockchain-based bidding system. The goal is to improve users’ experience with diverse services and reduce providers’ capital and operational expenditure

Ambient backcom in beyond 5G NOMA networks: A multi-cell resource allocation framework

The research of Non-Orthogonal Multiple Access (NOMA) is extensively used to improve the capacity of networks beyond the fifth-generation. The recent merger of NOMA with ambient Backscatter Communication (BackCom), though opening new possibilities for massive connectivity, poses several challenges in dense wireless networks. One of such challenges is the performance degradation of ambient BackCom in multi-cell NOMA networks under the effect of inter-cell interference. Driven by providing an efficient solution to the issue, this article proposes a new resource allocation framework that uses a duality theory approach. Specifically, the sum rate of the multi-cell network with backscatter tags and NOMA user equipments is maximized by formulating a joint optimization problem. To find the efficient base station transmit power and backscatter reflection coefficient in each cell, the original problem is first divided into two subproblems, and then the closed form solution is derived. A comparison with the Orthogonal Multiple Access (OMA) ambient BackCom and pure NOMA transmission has been provided. Simulation results of the proposed NOMA ambient BackCom indicate a significant improvement over the OMA ambient BackCom and pure NOMA in terms of sum-rate gains

Influence of a legume green manure crop on barley straw/stubble decomposition, and soil nitrogen retention and availability

The incorporation of cereal straw/stubble often immobilises nitrogen (N). This can help conserve N in soil in organic forms, thus reducing loss through leaching over dormant winter periods. However, N-depressions that arise during decomposition can reduce crop yield. The inclusion of a legume green manure can supply fixed-N, thus alleviating the low N availability to crops. In this study, the effect of lupin (Lupinus angustifolius L.) green manure incorporation on barley (Hordeum vulgare L.) straw/stubble decomposition, and N availability was investigated. A field experiment was used to determine the effects of the green manure on decomposition. Decomposition of straw/stubble was monitored using the litterbag technique. Following green manure incorporation, soil cores were incubated in a glasshouse to determine mineral-N availability. Though not significant, the inclusion of lupin green manure seemed to increase the decomposition of straw/stubble during the growth period, then slowing it after its incorporation at 110 d. This was described by a logarithmic pattern of loss of - 4.97 g AFDW residue day⁻¹, with 60% remaining after 140 d. Treatments without lupin had a linear decomposition of - 0.12 g AFDW residue day⁻¹, with 49% remaining after 140 d. The loss of cellulose confirmed the differences in decomposition with the inclusion of lupin resulting in 2.79% less cellulose remaining in straw/stubble after 140 d compared to its exclusion. Lupin significantly increased pot oat N uptake and DM yield by 55 % and 46 %, respectively, compared to its exclusion. However, this effect was not observed in field sown wheat yields and the soil mineral-N measurements made. This study showed that the potential of lupin to increase straw/stubble decomposition by improving the retention and availability of N, leading to long-term yield benefits, needed further investigation

Political Culture in Narratives

Political culture in narratives Abstract Thesis "Political culture in narratives" aims on the phenomenom of Political culture as was defined by Almond and Verba. Base of this thesis are researches on Political culture by Almond and Verba and other authors who done researches on this topic including political culture researches done within context in the Czech Republic. The research in this thesis is based on ten semi-structured qualitative interviews with informants, who grew up during the communist era in former Czechoslovakia. Interviews aimed on informants' experiences during the communism era, Velvet revolution, economic transformation and present day. Informants evaluates their understanding of political situation during mentioned eras, their political activity and their life situation. Thases aims on behalf of this data to discover how growing up during communist era, Velvet revolution and economical transformation influenced informants' political attitudes and how it influenced their political culture. It was discovered that during the communsit era subject political culture dominated. This changed right after Velvet revolution, when participant attitudes dominated. After the series of dissapointments in political situation during the economic transformation, informants showed anomic attitudes..

Decision support system for selecting software frameworks for cross-platform mobile application development

Tato diplomová práce se zabývá hodnocením softwarových frameworků pro vývoj multiplatformních mobilních aplikací. Cílem bylo vytvořit nástroj, který dokáže pomoci při rozhodování o výběru konkrétního frameworku a jehož vstupní data jsou dostupná pro další úpravy odbornou veřejností. S využitím obecného přístupu doporučovacích systémů a odborných článků zabývajících se hodnocením frameworků podle stanovených kritérií byla vyvinuta veřejně dostupná webová aplikace, která na základě vyplněných požadavků a jejich vah dokáže vyhodnotit frameworky podle míry shody se skutečnými hodnotami kritérií. Platnost a použitelnost vytvořené webové aplikace byla ověřena pomocí dvou případových studií představující mobilní aplikace s odlišnými požadavky a prioritami. Nástroj je schopen vyhodnotit a seřadit frameworky dle očekávání — hybridní řešení dosahují zpravidla nižšího skóre než multiplatformní interpretované frameworky. Při ověřování nástroje byla identifikována kritéria, která nástroj přímo nezohledňuje, konkrétně relativní počet vývojářů na trhu se znalostí technologií použitých ve frameworcích a rozdíl v pracnosti při transformaci stávající webové aplikace do multiplatformního frameworku oproti integraci s hybridním řešením. Otevřený zdrojový kód webové aplikace a zdrojová data však dávají prostor dalšímu rozvoji nástroje odbornou veřejností.This thesis focuses on the evaluation of software frameworks for cross-platform mobile application development. The goal of the thesis was to create a tool that can help with decision-making about selecting a specific framework, and whose input data are publicly available for editing by experts. Based on a knowledge-based recommender system approach and previous articles dealing with criteria-based framework evaluation, a public web application was developed. The application is able to evaluate a set of cross-platform frameworks according to user-entered requirements and their relative weights, compared to actual criteria data. The validity and applicability of the tool were evaluated by employing two case studies representing mobile applications with distinct requirements and priorities. The tool is able to evaluate and rank all frameworks as expected — hybrid frameworks are generally ranked lower than cross-platform frameworks generating interpreted applications. During the evaluation of the tool, some criteria were found not to be factored in during the process. Firstly, it is the relative number of developers with the knowledge of the technologies used by a particular framework and secondly, the difference of development effort for transforming an existing web application into a cross-platform framework compared to integrating with a hybrid framework