264 research outputs found

    A BIM - GIS Integrated Information Model Using Semantic Web and RDF Graph Databases

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    In recent years, 3D virtual indoor and outdoor urban modelling has become an essential geospatial information framework for civil and engineering applications such as emergency response, evacuation planning, and facility management. Building multi-sourced and multi-scale 3D urban models are in high demand among architects, engineers, and construction professionals to achieve these tasks and provide relevant information to decision support systems. Spatial modelling technologies such as Building Information Modelling (BIM) and Geographical Information Systems (GIS) are frequently used to meet such high demands. However, sharing data and information between these two domains is still challenging. At the same time, the semantic or syntactic strategies for inter-communication between BIM and GIS do not fully provide rich semantic and geometric information exchange of BIM into GIS or vice-versa. This research study proposes a novel approach for integrating BIM and GIS using semantic web technologies and Resources Description Framework (RDF) graph databases. The suggested solution's originality and novelty come from combining the advantages of integrating BIM and GIS models into a semantically unified data model using a semantic framework and ontology engineering approaches. The new model will be named Integrated Geospatial Information Model (IGIM). It is constructed through three stages. The first stage requires BIMRDF and GISRDF graphs generation from BIM and GIS datasets. Then graph integration from BIM and GIS semantic models creates IGIMRDF. Lastly, the information from IGIMRDF unified graph is filtered using a graph query language and graph data analytics tools. The linkage between BIMRDF and GISRDF is completed through SPARQL endpoints defined by queries using elements and entity classes with similar or complementary information from properties, relationships, and geometries from an ontology-matching process during model construction. The resulting model (or sub-model) can be managed in a graph database system and used in the backend as a data-tier serving web services feeding a front-tier domain-oriented application. A case study was designed, developed, and tested using the semantic integrated information model for validating the newly proposed solution, architecture, and performance

    Full Issue: Volume 56, No. 1

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    Configurable data center switch architectures

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    In this thesis, we explore alternative architectures for implementing con_gurable Data Center Switches along with the advantages that can be provided by such switches. Our first contribution centers around determining switch architectures that can be implemented on Field Programmable Gate Array (FPGA) to provide configurable switching protocols. In the process, we identify a gap in the availability of frameworks to realistically evaluate the performance of switch architectures in data centers and contribute a simulation framework that relies on realistic data center traffic patterns. Our framework is then used to evaluate the performance of currently existing as well as newly proposed FPGA-amenable switch designs. Through collaborative work with Meng and Papaphilippou, we establish that only small-medium range switches can be implemented on today's FPGAs. Our second contribution is a novel switch architecture that integrates a custom in-network hardware accelerator with a generic switch to accelerate Deep Neural Network training applications in data centers. Our proposed accelerator architecture is prototyped on an FPGA, and a scalability study is conducted to demonstrate the trade-offs of an FPGA implementation when compared to an ASIC implementation. In addition to the hardware prototype, we contribute a light weight load-balancing and congestion control protocol that leverages the unique communication patterns of ML data-parallel jobs to enable fair sharing of network resources across different jobs. Our large-scale simulations demonstrate the ability of our novel switch architecture and light weight congestion control protocol to both accelerate the training time of machine learning jobs by up to 1.34x and benefit other latency-sensitive applications by reducing their 99%-tile completion time by up to 4.5x. As for our final contribution, we identify the main requirements of in-network applications and propose a Network-on-Chip (NoC)-based architecture for supporting a heterogeneous set of applications. Observing the lack of tools to support such research, we provide a tool that can be used to evaluate NoC-based switch architectures.Open Acces

    Migration Planning Framework for Legacy Systems’ Cloud Migration

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    Cloud computing is being adopted at a fast phase by organizations all over the world. By utilizing cloud computing, significant benefits compared to traditional on-premises solutions can be achieved. Most of the new systems and applications are built to be cloud native applications but many large organizations still depend on legacy systems that have been built tens of years ago. Migrating these legacy applications or systems to the cloud have become major objective for the organizations. Legacy applications and systems have their own concerns and risks regarding cloud migration and require profound evaluation before the migration process can even be started. In this thesis, literature review was done to identify the possible migration strategies as well as common concerns and risks regarding legacy system’s or application’s cloud migration. Literature review was utilized to build a migration planning framework for legacy system’s or application’s cloud migration. The migration planning framework was validated with a case study. In the case study, a legacy application’s tentative cloud migration was planned by utilizing the created migration planning framework

    A geo-informatics approach to sustainability assessments of floatovoltaic technology in South African agricultural applications

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    South African project engineers recently pioneered the first agricultural floating solar photovoltaic tech nology systems in the Western Cape wine region. This effort prepared our country for an imminent large scale diffusion of this exciting new climate solver technology. However, hydro-embedded photovoltaic sys tems interact with environmentally sensitive underlying aquatic ecosystems, causing multiple project as sessment uncertainties (energy, land, air, water) compared to ground-mounted photovoltaics. The dissimi lar behaviour of floatovoltaic technologies delivers a broader and more diversified range of technical advan tages, environmental offset benefits, and economic co-benefits, causing analytical modelling imperfections and tooling mismatches in conventional analytical project assessment techniques. As a universal interna tional real-world problem of significance, the literature review identified critical knowledge and methodology gaps as the primary causes of modelling deficiencies and assessment uncertainties. By following a design thinking methodology, the thesis views the sustainability assessment and modelling problem through a geo graphical information systems lens, thus seeing an academic research opportunity to fill critical knowledge gaps through new theory formulation and geographical knowledge creation. To this end, this philosophi cal investigation proposes a novel object-oriented systems-thinking and climate modelling methodology to study the real-world geospatial behaviour of functioning floatovoltaic systems from a dynamical system thinking perspective. As an empirical feedback-driven object-process methodology, it inspired the thesis to create new knowledge by postulating a new multi-disciplinary sustainability theory to holistically characterise agricultural floatovoltaic projects through ecosystems-based quantitative sustainability profiling criteria. The study breaks new ground at the frontiers of energy geo-informatics by conceptualising a holistic theoretical framework designed for the theoretical characterisation of floatovoltaic technology ecosystem operations in terms of the technical energy, environmental and economic (3E) domain responses. It campaigns for a fully coupled model in ensemble analysis that advances the state-of-the-art by appropriating the 3E theo retical framework as underpinning computer program logic blueprint to synthesise the posited theory in a digital twin simulation. Driven by real-world geo-sensor data, this geospatial digital twin can mimic the geo dynamical behaviour of floatovoltaics through discrete-time computer simulations in real-time and lifetime digital project enactment exercises. The results show that the theoretical 3E framing enables project due diligence and environmental impact assessment reporting as it uniquely incorporates balanced scorecard performance metrics, such as the water-energy-land-food resource impacts, environmental offset benefits and financial feasibility of floatovoltaics. Embedded in a geoinformatics decision-support platform, the 3E theory, framework and model enable numerical project decision-supporting through an analytical hierarchy process. The experimental results obtained with the digital twin model and decision support system show that the desktop-based parametric floatovoltaic synthesis toolset can uniquely characterise the broad and diverse spectrum of performance benefits of floatovoltaics in a 3E sustainability profile. The model uniquely predicts important impact aspects of the technology’s land, air and water preservation qualities, quantifying these impacts in terms of the water, energy, land and food nexus parameters. The proposed GIS model can quantitatively predict most FPV technology unknowns, thus solving a contemporary real-world prob lem that currently jeopardises floating PV project licensing and approvals. Overall, the posited theoretical framework, methodology model, and reported results provide an improved understanding of floating PV renewable energy systems and their real-world behaviour. Amidst a rapidly growing international interest in floatovoltaic solutions, the research advances fresh philosophical ideas with novel theoretical principles that may have far-reaching implications for developing electronic, photovoltaic performance models worldwide.GeographyPh. D. (Geography

    Naval Postgraduate School Academic Catalog - February 2023

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    Autonomy, Efficiency, Privacy and Traceability in Blockchain-enabled IoT Data Marketplace

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    Personal data generated from IoT devices is a new economic asset that individuals can trade to generate revenue on the emerging data marketplaces. Blockchain technology can disrupt the data marketplace and make trading more democratic, trustworthy, transparent and secure. Nevertheless, the adoption of blockchain to create an IoT data marketplace requires consideration of autonomy and efficiency, privacy, and traceability. Conventional centralized approaches are built around a trusted third party that conducts and controls all management operations such as managing contracts, pricing, billing, reputation mechanisms etc, raising concern that providers lose control over their data. To tackle this issue, an efficient, autonomous and fully-functional marketplace system is needed, with no trusted third party involved in operational tasks. Moreover, an inefficient allocation of buyers’ demands on battery-operated IoT devices poses a challenge for providers to serve multiple buyers’ demands simultaneously in real-time without disrupting their SLAs (service level agreements). Furthermore, a poor privacy decision to make personal data accessible to unknown or arbitrary buyers may have adverse consequences and privacy violations for providers. Lastly, a buyer could buy data from one marketplace and without the knowledge of the provider, resell bought data to users registered in other marketplaces. This may either lead to monetary loss or privacy violation for the provider. To address such issues, a data ownership traceability mechanism is essential that can track the change in ownership of data due to its trading within and across marketplace systems. However, data ownership traceability is hard because of ownership ambiguity, undisclosed reselling, and dispersal of ownership across multiple marketplaces. This thesis makes the following novel contributions. First, we propose an autonomous and efficient IoT data marketplace, MartChain, offering key mechanisms for a marketplace leveraging smart contracts to record agreement details, participant ratings, and data prices in blockchain without involving any mediator. Second, MartChain is underpinned by an Energy-aware Demand Selection and Allocation (EDSA) mechanism for optimally selecting and allocating buyers' demands on provider’s IoT devices while satisfying the battery, quality and allocation constraints. EDSA maximizes the revenue of the provider while meeting the buyers’ requirements and ensuring the completion of the selected demands without any interruptions. The proof-of-concept implementation on the Ethereum blockchain shows that our approach is viable and benefits the provider and buyer by creating an autonomous and efficient real-time data trading model. Next, we propose KYBChain, a Know-Your-Buyer in the privacy-aware decentralized IoT data marketplace that performs a multi-faceted assessment of various characteristics of buyers and evaluates their privacy rating. Privacy rating empowers providers to make privacy-aware informed decisions about data sharing. Quantitative analysis to evaluate the utility of privacy rating demonstrates that the use of privacy rating by the providers results in a decrease of data leakage risk and generated revenue, correlating with the classical risk-utility trade-off. Evaluation results of KYBChain on Ethereum reveal that the overheads in terms of gas consumption, throughput and latency introduced by our privacy rating mechanism compared to a marketplace that does not incorporate a privacy rating system are insignificant relative to its privacy gains. Finally, we propose TrailChain which generates a trusted trade trail for tracking the data ownership spanning multiple decentralized marketplaces. Our solution includes mechanisms for detecting any unauthorized data reselling to prevent privacy violations and a fair resell payment sharing scheme to distribute payment among data owners for authorized reselling. We performed qualitative and quantitative evaluations to demonstrate the effectiveness of TrailChain in tracking data ownership using four private Ethereum networks. Qualitative security analysis demonstrates that TrailChain is resilient against several malicious activities and security attacks. Simulations show that our method detects undisclosed reselling within the same marketplace and across different marketplaces. Besides, it also identifies whether the provider has authorized the reselling and fairly distributes the revenue among the data owners at marginal overhead

    SusTrainable: Promoting Sustainability as a Fundamental Driver in Software Development Training and Education. 2nd Teacher Training, January 23-27, 2023, Pula, Croatia. Revised lecture notes

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    This volume exhibits the revised lecture notes of the 2nd teacher training organized as part of the project Promoting Sustainability as a Fundamental Driver in Software Development Training and Education, held at the Juraj Dobrila University of Pula, Croatia, in the week January 23-27, 2023. It is the Erasmus+ project No. 2020-1-PT01-KA203-078646 - Sustrainable. More details can be found at the project web site https://sustrainable.github.io/ One of the most important contributions of the project are two summer schools. The 2nd SusTrainable Summer School (SusTrainable - 23) will be organized at the University of Coimbra, Portugal, in the week July 10-14, 2023. The summer school will consist of lectures and practical work for master and PhD students in computing science and closely related fields. There will be contributions from Babe\c{s}-Bolyai University, E\"{o}tv\"{o}s Lor\'{a}nd University, Juraj Dobrila University of Pula, Radboud University Nijmegen, Roskilde University, Technical University of Ko\v{s}ice, University of Amsterdam, University of Coimbra, University of Minho, University of Plovdiv, University of Porto, University of Rijeka. To prepare and streamline the summer school, the consortium organized a teacher training in Pula, Croatia. This was an event of five full days, organized by Tihana Galinac Grbac and Neven Grbac. The Juraj Dobrila University of Pula is very concerned with the sustainability issues. The education, research and management are conducted with sustainability goals in mind. The contributions in the proceedings were reviewed and provide a good overview of the range of topics that will be covered at the summer school. The papers in the proceedings, as well as the very constructive and cooperative teacher training, guarantee the highest quality and beneficial summer school for all participants.Comment: 85 pages, 8 figures, 3 code listings and 1 table; editors: Tihana Galinac Grbac, Csaba Szab\'{o}, Jo\~{a}o Paulo Fernande

    Exploring the darkverse: a multi-perspective analysis of the negative societal impacts of the metaverse

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    The Metaverse has the potential to form the next pervasive computing archetype that can transform many aspects of work and life at a societal level. Despite the many forecasted benefits from the metaverse, its negative outcomes have remained relatively unexplored with the majority of views grounded on logical thoughts derived from prior data points linked with similar technologies, somewhat lacking academic and expert perspective. This study responds to the dark side perspectives through informed and multifaceted narratives provided by invited leading academics and experts from diverse disciplinary backgrounds. The metaverse dark side perspectives covered include: technological and consumer vulnerability, privacy, and diminished reality, human–computer interface, identity theft, invasive advertising, misinformation, propaganda, phishing, financial crimes, terrorist activities, abuse, pornography, social inclusion, mental health, sexual harassment and metaverse-triggered unintended consequences. The paper concludes with a synthesis of common themes, formulating propositions, and presenting implications for practice and policy

    Machine Tool Communication (MTComm) Method and Its Applications in a Cyber-Physical Manufacturing Cloud

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    The integration of cyber-physical systems and cloud manufacturing has the potential to revolutionize existing manufacturing systems by enabling better accessibility, agility, and efficiency. To achieve this, it is necessary to establish a communication method of manufacturing services over the Internet to access and manage physical machines from cloud applications. Most of the existing industrial automation protocols utilize Ethernet based Local Area Network (LAN) and are not designed specifically for Internet enabled data transmission. Recently MTConnect has been gaining popularity as a standard for monitoring status of machine tools through RESTful web services and an XML based messaging structure, but it is only designed for data collection and interpretation and lacks remote operation capability. This dissertation presents the design, development, optimization, and applications of a service-oriented Internet-scale communication method named Machine Tool Communication (MTComm) for exchanging manufacturing services in a Cyber-Physical Manufacturing Cloud (CPMC) to enable manufacturing with heterogeneous physically connected machine tools from geographically distributed locations over the Internet. MTComm uses an agent-adapter based architecture and a semantic ontology to provide both remote monitoring and operation capabilities through RESTful services and XML messages. MTComm was successfully used to develop and implement multi-purpose applications in in a CPMC including remote and collaborative manufacturing, active testing-based and edge-based fault diagnosis and maintenance of machine tools, cross-domain interoperability between Internet-of-things (IoT) devices and supply chain robots etc. To improve MTComm’s overall performance, efficiency, and acceptability in cyber manufacturing, the concept of MTComm’s edge-based middleware was introduced and three optimization strategies for data catching, transmission, and operation execution were developed and adopted at the edge. Finally, a hardware prototype of the middleware was implemented on a System-On-Chip based FPGA device to reduce computational and transmission latency. At every stage of its development, MTComm’s performance and feasibility were evaluated with experiments in a CPMC testbed with three different types of manufacturing machine tools. Experimental results demonstrated MTComm’s excellent feasibility for scalable cyber-physical manufacturing and superior performance over other existing approaches
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