1,826 research outputs found

    A novel smart contract based blockchain with sidechain for electronic voting

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    Several countries have been researching digital voting methods in order to overcome the challenges of paper balloting and physical voting. The recent coronavirus disease 2019 (COVID-19) epidemic has compelled the remote implementation of existing systems and procedures. Online voting will ultimately become the norm just like unified payments interface (UPI) payments and online banking. With digital voting or electronic voting (e-voting) a small bug can cause massive vote rigging. E-voting must be honest, exact, safe, and simple. E-voting is vulnerable to malware, which can disrupt servers. Blockchain’s end-to-end validation solves these problems. Three smart contracts-voter, candidate, and voting-are employed. The problem of fraudulent actions is addressed using vote coins. Vote coins indicate voter status. Sidechain technology complements blockchain. Sidechains improve blockchain functionality by performing operations outside of blockchains and delivering the results to the mainchain. Thus, storing the encrypted vote on the sidechain and using the decrypted result on the mainchain reduces cost. Building access control policies to grant only authorized users’ access to the votes for counting is made simpler by this authorization paradigm. Results of the approach depict the proposed e-voting system improves system security against replay attacks and reduces the processing cost as well as processing time

    Authentication enhancement in command and control networks: (a study in Vehicular Ad-Hoc Networks)

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    Intelligent transportation systems contribute to improved traffic safety by facilitating real time communication between vehicles. By using wireless channels for communication, vehicular networks are susceptible to a wide range of attacks, such as impersonation, modification, and replay. In this context, securing data exchange between intercommunicating terminals, e.g., vehicle-to-everything (V2X) communication, constitutes a technological challenge that needs to be addressed. Hence, message authentication is crucial to safeguard vehicular ad-hoc networks (VANETs) from malicious attacks. The current state-of-the-art for authentication in VANETs relies on conventional cryptographic primitives, introducing significant computation and communication overheads. In this challenging scenario, physical (PHY)-layer authentication has gained popularity, which involves leveraging the inherent characteristics of wireless channels and the hardware imperfections to discriminate between wireless devices. However, PHY-layerbased authentication cannot be an alternative to crypto-based methods as the initial legitimacy detection must be conducted using cryptographic methods to extract the communicating terminal secret features. Nevertheless, it can be a promising complementary solution for the reauthentication problem in VANETs, introducing what is known as “cross-layer authentication.” This thesis focuses on designing efficient cross-layer authentication schemes for VANETs, reducing the communication and computation overheads associated with transmitting and verifying a crypto-based signature for each transmission. The following provides an overview of the proposed methodologies employed in various contributions presented in this thesis. 1. The first cross-layer authentication scheme: A four-step process represents this approach: initial crypto-based authentication, shared key extraction, re-authentication via a PHY challenge-response algorithm, and adaptive adjustments based on channel conditions. Simulation results validate its efficacy, especially in low signal-to-noise ratio (SNR) scenarios while proving its resilience against active and passive attacks. 2. The second cross-layer authentication scheme: Leveraging the spatially and temporally correlated wireless channel features, this scheme extracts high entropy shared keys that can be used to create dynamic PHY-layer signatures for authentication. A 3-Dimensional (3D) scattering Doppler emulator is designed to investigate the scheme’s performance at different speeds of a moving vehicle and SNRs. Theoretical and hardware implementation analyses prove the scheme’s capability to support high detection probability for an acceptable false alarm value ≤ 0.1 at SNR ≥ 0 dB and speed ≤ 45 m/s. 3. The third proposal: Reconfigurable intelligent surfaces (RIS) integration for improved authentication: Focusing on enhancing PHY-layer re-authentication, this proposal explores integrating RIS technology to improve SNR directed at designated vehicles. Theoretical analysis and practical implementation of the proposed scheme are conducted using a 1-bit RIS, consisting of 64 × 64 reflective units. Experimental results show a significant improvement in the Pd, increasing from 0.82 to 0.96 at SNR = − 6 dB for multicarrier communications. 4. The fourth proposal: RIS-enhanced vehicular communication security: Tailored for challenging SNR in non-line-of-sight (NLoS) scenarios, this proposal optimises key extraction and defends against denial-of-service (DoS) attacks through selective signal strengthening. Hardware implementation studies prove its effectiveness, showcasing improved key extraction performance and resilience against potential threats. 5. The fifth cross-layer authentication scheme: Integrating PKI-based initial legitimacy detection and blockchain-based reconciliation techniques, this scheme ensures secure data exchange. Rigorous security analyses and performance evaluations using network simulators and computation metrics showcase its effectiveness, ensuring its resistance against common attacks and time efficiency in message verification. 6. The final proposal: Group key distribution: Employing smart contract-based blockchain technology alongside PKI-based authentication, this proposal distributes group session keys securely. Its lightweight symmetric key cryptography-based method maintains privacy in VANETs, validated via Ethereum’s main network (MainNet) and comprehensive computation and communication evaluations. The analysis shows that the proposed methods yield a noteworthy reduction, approximately ranging from 70% to 99%, in both computation and communication overheads, as compared to the conventional approaches. This reduction pertains to the verification and transmission of 1000 messages in total

    Risk and threat mitigation techniques in internet of things (IoT) environments: a survey

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    Security in the Internet of Things (IoT) remains a predominant area of concern. Although several other surveys have been published on this topic in recent years, the broad spectrum that this area aims to cover, the rapid developments and the variety of concerns make it impossible to cover the topic adequately. This survey updates the state of the art covered in previous surveys and focuses on defences and mitigations against threats rather than on the threats alone, an area that is less extensively covered by other surveys. This survey has collated current research considering the dynamicity of the IoT environment, a topic missed in other surveys and warrants particular attention. To consider the IoT mobility, a life-cycle approach is adopted to the study of dynamic and mobile IoT environments and means of deploying defences against malicious actors aiming to compromise an IoT network and to evolve their attack laterally within it and from it. This survey takes a more comprehensive and detailed step by analysing a broad variety of methods for accomplishing each of the mitigation steps, presenting these uniquely by introducing a “defence-in-depth” approach that could significantly slow down the progress of an attack in the dynamic IoT environment. This survey sheds a light on leveraging redundancy as an inherent nature of multi-sensor IoT applications, to improve integrity and recovery. This study highlights the challenges of each mitigation step, emphasises novel perspectives, and reconnects the discussed mitigation steps to the ground principles they seek to implement

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    The applications of Internet of Things (IoT) in industrial management: a science mapping review

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    With the rise of Internet of Things (IoT) technology, the seamless connection between the physical and digital worlds has been realized. This review paper aims to conduct a science mapping review of IoT applications in industrial management and to identify mainstream research topics, research gaps, and future research directions. Using VOSviewer as a visualization tool, 142 articles retrieved from the Scopus database were quantitatively discussed using scientometric analysis. Additionally, a follow-up qualitative discussion was focused on mainstream research topics, existing research gaps, and future research directions as the main research goals. The results revealed influential findings for the co-occurrence of keywords, journals, countries, authors, and documents analyses. Moreover, it was found that the existing research mainly focused on four main research topics including (1) application of IoT in manufacturing based on cyber-physical systems, (2) IoT-related technologies on logistics and supply chain management, (3) The impact of IoT on business models, and (4) Industrial IoT (IIoT) in the context of Industry 4.0. On this basis, the existing research gaps and future research directions are proposed. This review paper would help relevant practitioners and researchers to better understand the existing body of knowledge and lay the foundation for further research

    Harnessing energy for wearables: a review of radio frequency energy harvesting technologies

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    Wireless energy harvesting enables the conversion of ambient energy into electrical power for small wireless electronic devices. This technology offers numerous advantages, including availability, ease of implementation, wireless functionality, and cost-effectiveness. Radio frequency energy harvesting (RFEH) is a specific type of wireless energy harvesting that enables wireless power transfer by utilizing RF signals. RFEH holds immense potential for extending the lifespan of wireless sensors and wearable electronics that require low-power operation. However, despite significant advancements in RFEH technology for self-sustainable wearable devices, numerous challenges persist. This literature review focuses on three key areas: materials, antenna design, and power management, to delve into the research challenges of RFEH comprehensively. By providing an up-to-date review of research findings on RFEH, this review aims to shed light on the critical challenges, potential opportunities, and existing limitations. Moreover, it emphasizes the importance of further research and development in RFEH to advance its state-of-the-art and offer a vision for future trends in this technology

    Investigating the Effects of Network Dynamics on Quality of Delivery Prediction and Monitoring for Video Delivery Networks

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    Video streaming over the Internet requires an optimized delivery system given the advances in network architecture, for example, Software Defined Networks. Machine Learning (ML) models have been deployed in an attempt to predict the quality of the video streams. Some of these efforts have considered the prediction of Quality of Delivery (QoD) metrics of the video stream in an effort to measure the quality of the video stream from the network perspective. In most cases, these models have either treated the ML algorithms as black-boxes or failed to capture the network dynamics of the associated video streams. This PhD investigates the effects of network dynamics in QoD prediction using ML techniques. The hypothesis that this thesis investigates is that ML techniques that model the underlying network dynamics achieve accurate QoD and video quality predictions and measurements. The thesis results demonstrate that the proposed techniques offer performance gains over approaches that fail to consider network dynamics. This thesis results highlight that adopting the correct model by modelling the dynamics of the network infrastructure is crucial to the accuracy of the ML predictions. These results are significant as they demonstrate that improved performance is achieved at no additional computational or storage cost. These techniques can help the network manager, data center operatives and video service providers take proactive and corrective actions for improved network efficiency and effectiveness

    Imagining the Internet in Future

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    In this paper, we imagining the Future Internet (ICN and NDN architecture). We discuss examples of edge computing and IoT. We chose these areas because they are very important topics in today research. We also discuss provider mobility, P2P architectures, sync, and simulation tools. We discuss open questions for research

    Modell bedarfsorientierter Leistungserbringung im FM auf Grundlage von Sensortechnologien und BIM

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    Während der Digitalisierung im Bauwesen insbesondere im Bereich der Planungs- und Errichtungsphase von Bauwerken immer größere Aufmerksamkeit zuteilwird, ist das digitale Potenzial im Facility Management weit weniger ausgeschöpft, als dies möglich wäre. Vor dem Hintergrund, dass die Bewirtschaftung von Gebäuden jedoch einen wesentlichen Kostenanteil im Lebenszyklus darstellt, ist eine Fokussierung auf digitale Prozesse im Gebäudebetrieb erforderlich. Im Facility Management werden Dienstleistungen häufig verrichtungsorientiert, d. h. nach statischen Intervallen, oder bedarfsorientiert erbracht. Beide Arten der Leistungserbringung weisen Defizite auf, beispielweise weil Tätigkeiten auf Basis definierter Intervalle erbracht werden, ohne dass eine Notwendigkeit besteht oder weil bestehende Bedarfe mangels Möglichkeiten der Bedarfsermittlung nicht identifiziert werden. Speziell die Definition und Ermittlung eines Bedarfs zur Leistungserbringung ist häufig subjektiv geprägt. Auch sind Dienstleister oft nicht in frühen Phasen der Gebäudeplanung involviert und erhalten für ihre Dienstleistungen notwendige Daten und Informationen erst kurz vor Inbetriebnahme des zu betreibenden Gebäudes. Aktuelle Ansätze des Building Information Modeling (BIM) und die zunehmende Verfügbarkeit von Sensortechnologien in Gebäuden bieten Chancen, die o. g. Defizite zu beheben. In der vorliegenden Arbeit werden deshalb Datenmodelle und Methoden entwickelt, die mithilfe von BIM-basierten Datenbankstrukturen sowie Auswertungs- und Entscheidungsmethodiken Dienstleistungen der Gebäudebewirtschaftung objektiviert und automatisiert auslösen können. Der Fokus der Arbeit liegt dabei auf dem Facility Service der Reinigungs- und Pflegedienste des infrastrukturellen Facility Managements. Eine umfangreiche Recherche etablierter Normen und Standards sowie öffentlich zugänglicher Leistungsausschreibungen bilden die Grundlage der Definition erforderlicher Informationen zur Leistungserbringung. Die identifizierten statischen Gebäude- und Prozessinformationen werden in einem relationalen Datenbankmodell strukturiert, das nach einer Darstellung von Messgrößen und der Beschreibung des Vorgehens zur Auswahl geeigneter Sensoren für die Erfassung von Bedarfen, um Sensorinformationen erweitert wird. Um Messwerte verschiedener und bereits in Gebäuden existenten Sensoren für die Leistungsauslösung verwenden zu können, erfolgt die Implementierung einer Normierungsmethodik in das Datenbankmodell. Auf diese Weise kann der Bedarf zur Leistungserbringung ausgehend von Grenzwerten ermitteln werden. Auch sind Verknüpfungsmethoden zur Kombination verschiedener Anwendungen in dem Datenbankmodell integriert. Zusätzlich zur direkten Auslösung erforderlicher Aktivitäten ermöglicht das entwickelte Modell eine opportune Auslösung von Leistungen, d. h. eine Leistungserbringung vor dem eigentlich bestehenden Bedarf. Auf diese Weise können tätigkeitsähnliche oder räumlich nah beieinander liegende Tätigkeiten sinnvoll vorzeitig erbracht werden, um für den Dienstleister eine Wegstreckeneinsparung zu ermöglichen. Die Arbeit beschreibt zudem die für die Auswertung, Entscheidungsfindung und Auftragsüberwachung benötigen Algorithmen. Die Validierung des entwickelten Modells bedarfsorientierter Leistungserbringung erfolgt in einer relationalen Datenbank und zeigt simulativ für unterschiedliche Szenarien des Gebäudebetriebs, dass Bedarfsermittlungen auf Grundlage von Sensortechnologien erfolgen und Leistungen opportun ausgelöst, beauftragt und dokumentiert werden können
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