15,484 research outputs found

    A Design Science Research Approach to Smart and Collaborative Urban Supply Networks

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    Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness. A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense. Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice

    Reinforcement Learning-based User-centric Handover Decision-making in 5G Vehicular Networks

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    The advancement of 5G technologies and Vehicular Networks open a new paradigm for Intelligent Transportation Systems (ITS) in safety and infotainment services in urban and highway scenarios. Connected vehicles are vital for enabling massive data sharing and supporting such services. Consequently, a stable connection is compulsory to transmit data across the network successfully. The new 5G technology introduces more bandwidth, stability, and reliability, but it faces a low communication range, suffering from more frequent handovers and connection drops. The shift from the base station-centric view to the user-centric view helps to cope with the smaller communication range and ultra-density of 5G networks. In this thesis, we propose a series of strategies to improve connection stability through efficient handover decision-making. First, a modified probabilistic approach, M-FiVH, aimed at reducing 5G handovers and enhancing network stability. Later, an adaptive learning approach employed Connectivity-oriented SARSA Reinforcement Learning (CO-SRL) for user-centric Virtual Cell (VC) management to enable efficient handover (HO) decisions. Following that, a user-centric Factor-distinct SARSA Reinforcement Learning (FD-SRL) approach combines time series data-oriented LSTM and adaptive SRL for VC and HO management by considering both historical and real-time data. The random direction of vehicular movement, high mobility, network load, uncertain road traffic situation, and signal strength from cellular transmission towers vary from time to time and cannot always be predicted. Our proposed approaches maintain stable connections by reducing the number of HOs by selecting the appropriate size of VCs and HO management. A series of improvements demonstrated through realistic simulations showed that M-FiVH, CO-SRL, and FD-SRL were successful in reducing the number of HOs and the average cumulative HO time. We provide an analysis and comparison of several approaches and demonstrate our proposed approaches perform better in terms of network connectivity

    Deep Transfer Learning Applications in Intrusion Detection Systems: A Comprehensive Review

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    Globally, the external Internet is increasingly being connected to the contemporary industrial control system. As a result, there is an immediate need to protect the network from several threats. The key infrastructure of industrial activity may be protected from harm by using an intrusion detection system (IDS), a preventive measure mechanism, to recognize new kinds of dangerous threats and hostile activities. The most recent artificial intelligence (AI) techniques used to create IDS in many kinds of industrial control networks are examined in this study, with a particular emphasis on IDS-based deep transfer learning (DTL). This latter can be seen as a type of information fusion that merge, and/or adapt knowledge from multiple domains to enhance the performance of the target task, particularly when the labeled data in the target domain is scarce. Publications issued after 2015 were taken into account. These selected publications were divided into three categories: DTL-only and IDS-only are involved in the introduction and background, and DTL-based IDS papers are involved in the core papers of this review. Researchers will be able to have a better grasp of the current state of DTL approaches used in IDS in many different types of networks by reading this review paper. Other useful information, such as the datasets used, the sort of DTL employed, the pre-trained network, IDS techniques, the evaluation metrics including accuracy/F-score and false alarm rate (FAR), and the improvement gained, were also covered. The algorithms, and methods used in several studies, or illustrate deeply and clearly the principle in any DTL-based IDS subcategory are presented to the reader

    Interference mitigation in LiFi networks

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    Due to the increasing demand for wireless data, the radio frequency (RF) spectrum has become a very limited resource. Alternative approaches are under investigation to support the future growth in data traffic and next-generation high-speed wireless communication systems. Techniques such as massive multiple-input multiple-output (MIMO), millimeter wave (mmWave) communications and light-fidelity (LiFi) are being explored. Among these technologies, LiFi is a novel bi-directional, high-speed and fully networked wireless communication technology. However, inter-cell interference (ICI) can significantly restrict the system performance of LiFi attocell networks. This thesis focuses on interference mitigation in LiFi attocell networks. The angle diversity receiver (ADR) is one solution to address the issue of ICI as well as frequency reuse in LiFi attocell networks. With the property of high concentration gain and narrow field of view (FOV), the ADR is very beneficial for interference mitigation. However, the optimum structure of the ADR has not been investigated. This motivates us to propose the optimum structures for the ADRs in order to fully exploit the performance gain. The impact of random device orientation and diffuse link signal propagation are taken into consideration. The performance comparison between the select best combining (SBC) and maximum ratio combining (MRC) is carried out under different noise levels. In addition, the double source (DS) system, where each LiFi access point (AP) consists of two sources transmitting the same information signals but with opposite polarity, is proven to outperform the single source (SS) system under certain conditions. Then, to overcome issues around ICI, random device orientation and link blockage, hybrid LiFi/WiFi networks (HLWNs) are considered. In this thesis, dynamic load balancing (LB) considering handover in HLWNs is studied. The orientation-based random waypoint (ORWP) mobility model is considered to provide a more realistic framework to evaluate the performance of HLWNs. Based on the low-pass filtering effect of the LiFi channel, we firstly propose an orthogonal frequency division multiple access (OFDMA)-based resource allocation (RA) method in LiFi systems. Also, an enhanced evolutionary game theory (EGT)-based LB scheme with handover in HLWNs is proposed. Finally, due to the characteristic of high directivity and narrow beams, a vertical-cavity surface-emitting laser (VCSEL) array transmission system has been proposed to mitigate ICI. In order to support mobile users, two beam activation methods are proposed. The beam activation based on the corner-cube retroreflector (CCR) can achieve low power consumption and almost-zero delay, allowing real-time beam activation for high-speed users. The mechanism based on the omnidirectional transmitter (ODTx) is suitable for low-speed users and very robust to random orientation

    The Adirondack Chronology

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    The Adirondack Chronology is intended to be a useful resource for researchers and others interested in the Adirondacks and Adirondack history.https://digitalworks.union.edu/arlpublications/1000/thumbnail.jp

    Network Slicing for Industrial IoT and Industrial Wireless Sensor Network: Deep Federated Learning Approach and Its Implementation Challenges

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    5G networks are envisioned to support heterogeneous Industrial IoT (IIoT) and Industrial Wireless Sensor Network (IWSN) applications with a multitude Quality of Service (QoS) requirements. Network slicing is being recognized as a beacon technology that enables multi-service IIoT networks. Motivated by the growing computational capacity of the IIoT and the challenges of meeting QoS, federated reinforcement learning (RL) has become a propitious technique that gives out data collection and computation tasks to distributed network agents. This chapter discuss the new federated learning paradigm and then proposes a Deep Federated RL (DFRL) scheme to provide a federated network resource management for future IIoT networks. Toward this goal, the DFRL learns from Multi-Agent local models and provides them the ability to find optimal action decisions on LoRa parameters that satisfy QoS to IIoT virtual slice. Simulation results prove the effectiveness of the proposed framework compared to the early tools

    NOMA Transmission Systems: Overview of SIC Design and New Findings

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    Non-Orthogonal Multiple Access (NOMA) has been recently proposed as a good alternative to meet 5G and beyond requirements in terms of high spectral efficiency, massive connectivity, and low latency. It has been demonstrated that the use of NOMA in downlink has superior performance in terms of throughput, whereas the use in uplink outperforms OMA techniques in terms of fairness. A distinctive feature of NOMA is the presence of excessive multiple-access interference due to the case of usage of power domain to multiplex signals, thus the functional implementation of NOMA implies Successive Interference Cancelation (SIC) to combat this interference. Therefore, SIC design becomes the main point in the effectiveness of NOMA systems. On the other hand, hybrid schemes, NOMA/OMA, have been recently proposed to reduce the drawbacks of pure NOMA systems. However, in these schemes, it becomes necessary to distinguish NOMA and OMA users. Cognitive Radio techniques turn to be a good option to effectively separate NOMA/OMA users as well as to distinguish NOMA users. In this chapter, a brief overview of NOMA techniques related to Cognitive Radio technology (CR-NOMA) and SIC design reported in the literature is presented. Also, new findings about NOMA/OMA users’ recognition are described

    Estudo do IPFS como protocolo de distribuição de conteúdos em redes veiculares

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    Over the last few years, vehicular ad-hoc networks (VANETs) have been the focus of great progress due to the interest in autonomous vehicles and in distributing content not only between vehicles, but also to the Cloud. Performing a download/upload to/from a vehicle typically requires the existence of a cellular connection, but the costs associated with mobile data transfers in hundreds or thousands of vehicles quickly become prohibitive. A VANET allows the costs to be several orders of magnitude lower - while keeping the same large volumes of data - because it is strongly based in the communication between vehicles (nodes of the network) and the infrastructure. The InterPlanetary File System (IPFS) is a protocol for storing and distributing content, where information is addressed by its content, instead of its location. It was created in 2014 and it seeks to connect all computing devices with the same system of files, comparable to a BitTorrent swarm exchanging Git objects. It has been tested and deployed in wired networks, but never in an environment where nodes have intermittent connectivity, such as a VANET. This work focuses on understanding IPFS, how/if it can be applied to the vehicular network context, and comparing it with other content distribution protocols. In this dissertation, IPFS has been tested in a small and controlled network to understand its working applicability to VANETs. Issues such as neighbor discoverability times and poor hashing performance have been addressed. To compare IPFS with other protocols (such as Veniam’s proprietary solution or BitTorrent) in a relevant way and in a large scale, an emulation platform was created. The tests in this emulator were performed in different times of the day, with a variable number of files and file sizes. Emulated results show that IPFS is on par with Veniam’s custom V2V protocol built specifically for V2V, and greatly outperforms BitTorrent regarding neighbor discoverability and data transfers. An analysis of IPFS’ performance in a real scenario was also conducted, using a subset of STCP’s vehicular network in Oporto, with the support of Veniam. Results from these tests show that IPFS can be used as a content dissemination protocol, showing it is up to the challenge provided by a constantly changing network topology, and achieving throughputs up to 2.8 MB/s, values similar or in some cases even better than Veniam’s proprietary solution.Nos últimos anos, as redes veiculares (VANETs) têm sido o foco de grandes avanços devido ao interesse em veículos autónomos e em distribuir conteúdos, não só entre veículos mas também para a "nuvem" (Cloud). Tipicamente, fazer um download/upload de/para um veículo exige a utilização de uma ligação celular (SIM), mas os custos associados a fazer transferências com dados móveis em centenas ou milhares de veículos rapidamente se tornam proibitivos. Uma VANET permite que estes custos sejam consideravelmente inferiores - mantendo o mesmo volume de dados - pois é fortemente baseada na comunicação entre veículos (nós da rede) e a infraestrutura. O InterPlanetary File System (IPFS - "sistema de ficheiros interplanetário") é um protocolo de armazenamento e distribuição de conteúdos, onde a informação é endereçada pelo conteúdo, em vez da sua localização. Foi criado em 2014 e tem como objetivo ligar todos os dispositivos de computação num só sistema de ficheiros, comparável a um swarm BitTorrent a trocar objetos Git. Já foi testado e usado em redes com fios, mas nunca num ambiente onde os nós têm conetividade intermitente, tal como numa VANET. Este trabalho tem como foco perceber o IPFS, como/se pode ser aplicado ao contexto de rede veicular e compará-lo a outros protocolos de distribuição de conteúdos. Numa primeira fase o IPFS foi testado numa pequena rede controlada, de forma a perceber a sua aplicabilidade às VANETs, e resolver os seus primeiros problemas como os tempos elevados de descoberta de vizinhos e o fraco desempenho de hashing. De modo a poder comparar o IPFS com outros protocolos (tais como a solução proprietária da Veniam ou o BitTorrent) de forma relevante e em grande escala, foi criada uma plataforma de emulação. Os testes neste emulador foram efetuados usando registos de mobilidade e conetividade veicular de alturas diferentes de um dia, com um número variável de ficheiros e tamanhos de ficheiros. Os resultados destes testes mostram que o IPFS está a par do protocolo V2V da Veniam (desenvolvido especificamente para V2V e VANETs), e que o IPFS é significativamente melhor que o BitTorrent no que toca ao tempo de descoberta de vizinhos e transferência de informação. Uma análise do desempenho do IPFS em cenário real também foi efetuada, usando um pequeno conjunto de nós da rede veicular da STCP no Porto, com o apoio da Veniam. Os resultados destes testes demonstram que o IPFS pode ser usado como protocolo de disseminação de conteúdos numa VANET, mostrando-se adequado a uma topologia constantemente sob alteração, e alcançando débitos até 2.8 MB/s, valores parecidos ou nalguns casos superiores aos do protocolo proprietário da Veniam.Mestrado em Engenharia de Computadores e Telemátic

    Channel estimation and beam training with machine learning applications for millimetre-wave communication systems

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    The fifth generation (5G) wireless system will extend the capabilities of the fourth generation (4G) standards to serve more users and provide timely communication. To this end, the carriers of 5G systems will be able to operate at higher frequency bands, such as the millimetre-wave (mmWave) bands that span from 30 GHz to 300 GHz, to obtain greater bandwidths and higher data rates. As a result, the deployment of 5G networks is required to accommodate more antennas and offer pervasive coverage with controlled power consumption. The complexity of 5G systems introduces new challenges to traditional signal processing techniques. To address these challenges, a major step is to integrate machine learning (ML) algorithms into wireless communication systems. ML can learn patterns from datasets to achieve control and optimisation of complex radio frequency (RF) networks. This PhD thesis focuses on developing efficient channel estimation methods and beam training strategies with the application of ML algorithms for mmWave wireless systems. Firstly, the channel estimation and signal detection problem is investigated for orthogonal frequency-division multiplexing (OFDM) systems that operate at mmWave bands. A deep neural network (DNN)-based joint channel estimation and signal detection approach is proposed to achieve multi-user detection in a one-shot process for non-orthogonal multiple access (NOMA) systems. The DNN acts as the receiver, which can recover the transmitted data by learning the channel implicitly from suitable training. The proposed approach can be adapted to work for both single-input and single-output (SISO) systems and multiple-output and multipleoutput (MIMO) systems. This DNN-based approach is shown to provide good performance for OFDM systems that suffer from severe inter-symbol interference or where small numbers of pilot symbols are used. Secondly, the beam training and tracking problem is studied for mmWave channels with receiver mobility. To reduce the signalling overhead caused by frequent beam training, a lowcomplexity beam training strategy is proposed for mobile mmWave channels, which searches a set of selected beams obtained based on the recent beam search results. By searching only the adjacent beams to the one recently used, the proposed beam training strategy can reduce the beam training delay significantly while maintaining high transmission rates. The proposed strategy works effectively for channel datasets generated using either the stochastic or the raytracing channel model. This strategy is shown to approach the performance for an exhaustive beam search while saving up to 92% on the required beam training overhead. Thirdly, the proposed low-complexity beam training strategy is enhanced with the use of deep reinforcement learning (DRL) for mobile mmWave channels. A DRL-based beam training algorithm is proposed, which can intelligently switch between different beam training methods such that the average beam training overhead is minimised while achieving good spectral efficiency or energy efficiency performance. Given the desired performance requirement in the reward function for the DRL model, the spectral efficiency or energy efficiency can be maximised for the current channel condition by controlling the number of activated RF chains. The DRL-based approach can adjust the amount of beam training overhead required according to the dynamics of the environment. This approach can provide a good overhead-performance trade-off and achieve higher data rates in channels with significant levels of signal blockage
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