809 research outputs found

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    UMSL Bulletin 2022-2023

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    The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp

    Discussion on drivers and proposition of approaches to support the transition of traditional electricity consumers to prosumers

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    In recent years, traditional power systems have undergone a significant transition, mainly related to the massive penetration of Renewable Energy Sources (RES). More specifically, the transformation of residential consumers into prosumers has been challenging to the traditional operation of electricity markets. This transition brings new challenges and opportunities to the power system, leading to new Business Model (BM). One widely discussed change is related to a consumer-centric or prosumer-driven approach, promoting increased participation of small consumers in power systems. The present thesis aims at discussing the recent BMs as enablers of the increasing prosumers’ role in the energy market and power system worldwide, deepening the discussion with a holistic view of the Brazilian context. To do so, it defines the main features of prosumers and their general related regulation as well as possible market designs within power systems. Moreover, the work intends to contribute to the knowledge, identification and understanding of the main regulatory barriers and enablers for the development of those BMs in the Brazilian energy market. In addition, it discusses enabling technologies to properly create the conditions that sustain new prosumer-driven markets. Then, it presents a comprehensive review of existing and innovative BMs and a discussion on their future roles in modern power systems and, in the Brazilian regulatory framework seeking to guide the decisions for the country to develop its political and regulatory environment in the future. Moreover, a set of recommendations for promoting these BMs in the power system worldwide is provided along with policy recommendations to promote prosumers aggregation in the Brazilian energy sector. An important conclusion is that, even though economically possible, not all innovative BMs can spread around the world due to regulatory issues. Seeking to further explore one of the prosumer-driven approaches presented and the challenges imposed by this innovative BM, a study of energy and reserve markets based on the Peer-to-Peer (P2P) structure is carried out. This structure is very promising for the prosumers’ promotion but presents some challenges for the network operation. A critical challenge is to ensure that network constraints are not violated due to energy trades between peers and neither due to the use of reserve capacity. Therefore, two methodologies are proposed. First, is proposed a three-step approach (P2PTDF), using Topological Distribution Factors (TDF) to penalize peers responsible for violations that may occur in the network constraints, ensuring a feasible solution. Second, it is proposed a new integrated prosumers-DSO approach applied in P2P energy and reserve tradings that also ensures the feasibility of both energy and reserve transactions under network constraints. The proposed approach includes the estimation of reserve requirements based on the RES uncertain behavior from historical generation data, which allows identifying RES patterns. The proposed models are assessed through a case study that uses a 14-bus system, under the technical and economic criteria. The results show that the approaches can ensure a feasible network operation.Nos últimos anos, os sistemas tradicionais de energia passaram por uma transição significativa, principalmente relacionada à penetração massiva de fontes de energia renováveis (do inglês, Renewable energy sources-RES). Mais especificamente, a transformação de consumidores residenciais em prosumidores tem desafiado a atual operação do mercado de energia elétrica. Essa transição traz novos desafios e oportunidades para o sistema elétrico, levando a novos modelos de negócios (do inglês, Business Models-BM). Uma mudança amplamente discutida está relacionada a uma abordagem centrada no consumidor ou direcionada ao prossumidor, promovendo maior participação de pequenos consumidores nos sistemas de energia. A presente tese tem como objetivo discutir os recentes BMs como facilitadores do crescente papel dos prosumidores no mercado de energia e no sistema elétrico mundial, aprofundando a discussão com uma visão holística do contexto brasileiro. Para tanto, define as principais características dos prosumidores e sua regulamentação geral relacionada, bem como possíveis designs de mercado dentro dos sistemas de energia. Além disso, o trabalho pretende contribuir para o conhecimento, identificação e compreensão das principais barreiras regulatórias e facilitadoras para o desenvolvimento desses BMs no mercado brasileiro de energia. Assim como, discutir as tecnologias importantes para criar adequadamente as condições que sustentam novos mercados orientados ao consumidor final. Em seguida, apresenta uma revisão abrangente dos BMs existentes e inovadores e uma discussão sobre seus papéis futuros nos sistemas de energia modernos e, no quadro regulatório brasileiro, buscando orientar as decisões para que o país desenvolva seu ambiente político e regulatório no futuro. Além disso, um conjunto de recomendações para promover esses BMs no sistema de energia em todo o mundo é fornecido juntamente com recomendações de políticas para promover a agregação de prosumidores no setor de energia brasileiro. Uma conclusão importante é que, mesmo sendo economicamente possível, nem todos os BMs inovadores podem se espalhar pelo mundo devido a obstáculos regulatórias. Buscando explorar ainda mais uma das abordagens orientadas ao prosumidor apresentadas e os desafios impostos por este BM inovador, é realizado um estudo dos mercados de energia e de reserva com base na estrutura ponto a ponto (do inglês, peer-to-peer-P2P). Esta estrutura é muito promissora para a promoção dos prosumidores mas apresenta alguns desafios para o funcionamento da rede. Um desafio crítico é garantir que as restrições da rede não sejam violadas devido a negociações de energia entre pares e nem devido ao uso da capacidade de reserva. Portanto, duas metodologias são propostas. Primeiramente, é proposta uma abordagem em três passos (P2PTDF), utilizando Fatores de Distribuição Topológica (do inglês, Topological Distribution Factors-TDF ) para penalizar os peers responsáveis por violações que possam ocorrer nas restrições da rede, garantindo uma solução viável. Em segundo lugar, é proposta uma nova abordagem integrada de prosumidores-DSO aplicada em transações P2P de energia e reserva que também garante a viabilidade de transações de energia e reserva sob restrições de rede. A abordagem proposta inclui a estimativa dos requisitos de reserva com base no comportamento incerto da RES a partir de dados históricos de geração, o que permite identificar padrões de RES. Os modelos propostos são avaliados através de um estudo de caso que utiliza um sistema de 14 barras, sob os critérios técnico e econômico. Os resultados mostram que as abordagens podem garantir uma operação de rede viável abrangendo energia e mercados de reserva

    Microcredentials to support PBL

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    Optimizing Flow Routing Using Network Performance Analysis

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    Relevant conferences were attended at which work was often presented and several papers were published in the course of this project. • Muna Al-Saadi, Bogdan V Ghita, Stavros Shiaeles, Panagiotis Sarigiannidis. A novel approach for performance-based clustering and management of network traffic flows, IWCMC, ©2019 IEEE. • M. Al-Saadi, A. Khan, V. Kelefouras, D. J. Walker, and B. Al-Saadi: Unsupervised Machine Learning-Based Elephant and Mice Flow Identification, Computing Conference 2021. • M. Al-Saadi, A. Khan, V. Kelefouras, D. J. Walker, and B. Al-Saadi: SDN-Based Routing Framework for Elephant and Mice Flows Using Unsupervised Machine Learning, Network, 3(1), pp.218-238, 2023.The main task of a network is to hold and transfer data between its nodes. To achieve this task, the network needs to find the optimal route for data to travel by employing a particular routing system. This system has a specific job that examines each possible path for data and chooses the suitable one and transmit the data packets where it needs to go as fast as possible. In addition, it contributes to enhance the performance of network as optimal routing algorithm helps to run network efficiently. The clear performance advantage that provides by routing procedures is the faster data access. For example, the routing algorithm take a decision that determine the best route based on the location where the data is stored and the destination device that is asking for it. On the other hand, a network can handle many types of traffic simultaneously, but it cannot exceed the bandwidth allowed as the maximum data rate that the network can transmit. However, the overloading problem are real and still exist. To avoid this problem, the network chooses the route based on the available bandwidth space. One serious problem in the network is network link congestion and disparate load caused by elephant flows. Through forwarding elephant flows, network links will be congested with data packets causing transmission collision, congestion network, and delay in transmission. Consequently, there is not enough bandwidth for mice flows, which causes the problem of transmission delay. Traffic engineering (TE) is a network application that concerns with measuring and managing network traffic and designing feasible routing mechanisms to guide the traffic of the network for improving the utilization of network resources. The main function of traffic engineering is finding an obvious route to achieve the bandwidth requirements of the network consequently optimizing the network performance [1]. Routing optimization has a key role in traffic engineering by finding efficient routes to achieve the desired performance of the network [2]. Furthermore, routing optimization can be considered as one of the primary goals in the field of networks. In particular, this goal is directly related to traffic engineering, as it is based on one particular idea: to achieve that traffic is routed according to accurate traffic requirements [3]. Therefore, we can say that traffic engineering is one of the applications of multiple improvements to routing; routing can also be optimized based on other factors (not just on traffic requirements). In addition, these traffic requirements are variable depending on analyzed dataset that considered if it is data or traffic control. In this regard, the logical central view of the Software Defined Network (SDN) controller facilitates many aspects compared to traditional routing. The main challenge in all network types is performance optimization, but the situation is different in SDN because the technique is changed from distributed approach to a centralized one. The characteristics of SDN such as centralized control and programmability make the possibility of performing not only routing in traditional distributed manner but also routing in centralized manner. The first advantage of centralized routing using SDN is the existence of a path to exchange information between the controller and infrastructure devices. Consequently, the controller has the information for the entire network, flexible routing can be achieved. The second advantage is related to dynamical control of routing due to the capability of each device to change its configuration based on the controller commands [4]. This thesis begins with a wide review of the importance of network performance analysis and its role for understanding network behavior, and how it contributes to improve the performance of the network. Furthermore, it clarifies the existing solutions of network performance optimization using machine learning (ML) techniques in traditional networks and SDN environment. In addition, it highlights recent and ongoing studies of the problem of unfair use of network resources by a particular flow (elephant flow) and the possible solutions to solve this problem. Existing solutions are predominantly, flow routing-based and do not consider the relationship between network performance analysis and flow characterization and how to take advantage of it to optimize flow routing by finding the convenient path for each type of flow. Therefore, attention is given to find a method that may describe the flow based on network performance analysis and how to utilize this method for managing network performance efficiently and find the possible integration for the traffic controlling in SDN. To this purpose, characteristics of network flows is identified as a mechanism which may give insight into the diversity in flow features based on performance metrics and provide the possibility of traffic engineering enhancement using SDN environment. Two different feature sets with respect to network performance metrics are employed to characterize network traffic. Applying unsupervised machine learning techniques including Principal Component Analysis (PCA) and k-means cluster analysis to derive a traffic performance-based clustering model. Afterward, thresholding-based flow identification paradigm has been built using pre-defined parameters and thresholds. Finally, the resulting data clusters are integrated within a unified SDN architectural solution, which improves network management by finding the best flow routing based on the type of flow, to be evaluated against a number of traffic data sources and different performance experiments. The validation process of the novel framework performance has been done by making a performance comparison between SDN-Ryu controller and the proposed SDN-external application based on three factors: throughput, bandwidth,and data transfer rate by conducting two experiments. Furthermore, the proposed method has been validated by using different Data Centre Network (DCN) topologies to demonstrate the effectiveness of the network traffic management solution. The overall validation metrics shows real gains, the results show that 70% of the time, it has high performance with different flows. The proposed routing SDN traffic-engineering paradigm for a particular flow therefore, dynamically provisions network resources among different flow types

    Optimal Drive-by Sensing in Urban Road Networks with Large-scale Ridesourcing Vehicles

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    The sensing and monitoring of the urban road network contribute to the efficient operation of the urban transportation system and the functionality of urban systems. However, traditional sensing methods, such as inductive loop sensors, roadside cameras, and crowdsourcing data from massive urban travelers (e.g., Google Maps), are often hindered by high costs, limited coverage, and low reliability. This study explores the potential of drive-by sensing, an innovative approach that employs large-scale ridesourcing vehicles (RVs) for urban road network monitoring. We first evaluate RV sensing performance by coverage and reliability through historical road segment visits. Next, we propose an optimal trip-based RV rerouting model to maximize the sensing coverage and reliability while preserving the same level of service for the RVs' mobility service. Furthermore, a scalable column generation-based heuristic is designed to guide the cruising trajectory of RVs, assuming trip independence. The effectiveness of the proposed model is validated through experiments and sensitivity analyses using real-world RV trajectory data of over 20,000 vehicles in New York City. The optimized rerouting strategy has yielded significantly improved results, elevating explicit sensing coverage of the road network by 15.0\% to 17.3\% (varies by time of day) and achieving an impressive enhancement in sensing reliability by at least 24.6\% compared to historical records. Expanding the path-searching space further improved sensing coverage of up to 4.5\% and reliability of over 4.2\%. Moreover, considering incentives for RV drivers, the enhanced sensing performance comes at a remarkably low cost of \$0.10 per RV driver, highlighting its cost-effectiveness

    Blockchain-Coordinated Frameworks for Scalable and Secure Supply Chain Networks

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    Supply chains have progressed through time from being limited to a few regional traders to becoming complicated business networks. As a result, supply chain management systems now rely significantly on the digital revolution for the privacy and security of data. Due to key qualities of blockchain, such as transparency, immutability and decentralization, it has recently gained a lot of interest as a way to solve security, privacy and scalability problems in supply chains. However conventional blockchains are not appropriate for supply chain ecosystems because they are computationally costly, have a limited potential to scale and fail to provide trust. Consequently, due to limitations with a lack of trust and coordination, supply chains tend to fail to foster trust among the network’s participants. Assuring data privacy in a supply chain ecosystem is another challenge. If information is being shared with a large number of participants without establishing data privacy, access control risks arise in the network. Protecting data privacy is a concern when sending corporate data, including locations, manufacturing supplies and demand information. The third challenge in supply chain management is scalability, which continues to be a significant barrier to adoption. As the amount of transactions in a supply chain tends to increase along with the number of nodes in a network. So scalability is essential for blockchain adoption in supply chain networks. This thesis seeks to address the challenges of privacy, scalability and trust by providing frameworks for how to effectively combine blockchains with supply chains. This thesis makes four novel contributions. It first develops a blockchain-based framework with Attribute-Based Access Control (ABAC) model to assure data privacy by adopting a distributed framework to enable fine grained, dynamic access control management for supply chain management. To solve the data privacy challenge, AccessChain is developed. This proposed AccessChain model has two types of ledgers in the system: local and global. Local ledgers are used to store business contracts between stakeholders and the ABAC model management, whereas the global ledger is used to record transaction data. AccessChain can enable decentralized, fine-grained and dynamic access control management in SCM when combined with the ABAC model and blockchain technology (BCT). The framework enables a systematic approach that advantages the supply chain, and the experiments yield convincing results. Furthermore, the results of performance monitoring shows that AccessChain’s response time with four local ledgers is acceptable, and therefore it provides significantly greater scalability. Next, a framework for reducing the bullwhip effect (BWE) in SCM is proposed. The framework also focuses on combining data visibility with trust. BWE is first observed in SC and then a blockchain architecture design is used to minimize it. Full sharing of demand data has been shown to help improve the robustness of overall performance in a multiechelon SC environment, especially for BWE mitigation and cumulative cost reduction. It is observed that when it comes to providing access to data, information sharing using a blockchain has some obvious benefits in a supply chain. Furthermore, when data sharing is distributed, parties in the supply chain will have fair access to other parties’ data, even though they are farther downstream. Sharing customer demand is important in a supply chain to enhance decision-making, reduce costs and promote the final end product. This work also explores the ability of BCT as a solution in a distributed ledger approach to create a trust-enhanced environment where trust is established so that stakeholders can share their information effectively. To provide visibility and coordination along with a blockchain consensus process, a new consensus algorithm, namely Reputation-based proof-of cooperation (RPoC), is proposed for blockchain-based SCM, which does not involve validators to solve any mathematical puzzle before storing a new block. The RPoC algorithm is an efficient and scalable consensus algorithm that selects the consensus node dynamically and permits a large number of nodes to participate in the consensus process. The algorithm decreases the workload on individual nodes while increasing consensus performance by allocating the transaction verification process to specific nodes. Through extensive theoretical analyses and experimentation, the suitability of the proposed algorithm is well grounded in terms of scalability and efficiency. The thesis concludes with a blockchain-enabled framework that addresses the issue of preserving privacy and security for an open-bid auction system. This work implements a bid management system in a private BC environment to provide a secure bidding scheme. The novelty of this framework derives from an enhanced approach for integrating BC structures by replacing the original chain structure with a tree structure. Throughout the online world, user privacy is a primary concern, because the electronic environment enables the collection of personal data. Hence a suitable cryptographic protocol for an open-bid auction atop BC is proposed. Here the primary aim is to achieve security and privacy with greater efficiency, which largely depends on the effectiveness of the encryption algorithms used by BC. Essentially this work considers Elliptic Curve Cryptography (ECC) and a dynamic cryptographic accumulator encryption algorithm to enhance security between auctioneer and bidder. The proposed e-bidding scheme and the findings from this study should foster the further growth of BC strategies
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