15,484 research outputs found
A Design Science Research Approach to Smart and Collaborative Urban Supply Networks
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
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
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
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
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
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
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
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 veÃculos autónomos e em distribuir conteúdos,
não só entre veÃculos mas também para a "nuvem" (Cloud). Tipicamente,
fazer um download/upload de/para um veÃculo exige a utilização
de uma ligação celular (SIM), mas os custos associados a fazer transferências
com dados móveis em centenas ou milhares de veÃculos 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 veÃculos (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
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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