6 research outputs found
A NOMA-enhanced reconfigurable access scheme with device pairing for M2M networks
This paper aims to address the distinct requirements
of machine-to-machine networks, particularly heterogeneity and
massive transmissions. To this end, a reconfigurable medium
access control (MAC) with the ability to choose a proper access
scheme with the optimal configuration for devices based on
the network status is proposed. In this scheme, in each frame,
a separate time duration is allocated for each of the nonorthogonal multiple access (NOMA)-based, orthogonal multiple
access (OMA)-based, and random access-based segments, where
the length of each segment can be optimized. To solve this
optimization problem, an iterative algorithm consisting of two
sub-problems is proposed. The first sub-problem deals with
selecting devices for the NOMA/OMA-based transmissions, while
the second one optimizes the parameter of the random access
scheme. To show the efficacy of the proposed scheme, the results
are compared with the reconfigurable scheme which does not
support NOMA. The results demonstrate that by using a proper
device pairing scheme for the NOMA-based transmissions, the
proposed reconfigurable scheme achieves better performance
when NOMA is adopted
Energy efficiency maximization in a wireless powered IoT sensor network for water quality monitoring
This study presents novel approaches to the allocation of resources in Internet-of-Things sensor network (IoTSN) systems applied to water-quality monitoring for optimal and more sustainable utilization of resources. To tackle the long-standing energy scarcity issue that currently plagues sensor network (SN) systems, energy harvesting is explored and exploited to maximize its untapped potential to develop asuccessive wireless power sensor network (WPSN) system embedded with a scheduling algorithm, and operate as a non-orthogonal multiple access (NOMA) system. Similarly, quality of service parameters are crucial design considerations for network efficiency,and energy efficiency (EE) is considered here. Consequently, an EE optimization problem is formulated for the successiveWPSN system and solved by exploiting the problem structure and through a meta-heuristic algorithm. The new system is validated through the numerical simulation results presented in this work by thoroughly analyzing, evaluating and comparing the proposed meta-heuristic based WPSN system with the baseline state-of-the-art WPSN systems that combined a meta-heuristic algorithm, two additional meta-heuristic algorithms including genetic algorithm (GA) and ant-colony optimization (ACO) algorithm as well as a non-meta-heuristic algorithm – specifically an iterative based Dinkelbach algorithm.The experimental outcomes show that the proposed system significantly outperforms the contemporary WPSN systems in terms of EE performance gains.http://www.elsevier.com/locate/comnet2022-05-22hj2021Electrical, Electronic and Computer Engineerin
Production Optimization Indexed to the Market Demand Through Neural Networks
Connectivity, mobility and real-time data analytics are the prerequisites for a new model of intelligent
production management that facilitates communication between machines, people and
processes and uses technology as the main driver.
Many works in the literature treat maintenance and production management in separate approaches,
but there is a link between these areas, with maintenance and its actions aimed at ensuring the
smooth operation of equipment to avoid unnecessary downtime in production.
With the advent of technology, companies are rushing to solve their problems by resorting to technologies
in order to fit into the most advanced technological concepts, such as industries 4.0 and
5.0, which are based on the principle of process automation. This approach brings together database
technologies, making it possible to monitor the operation of equipment and have the opportunity
to study patterns of data behavior that can alert us to possible failures.
The present thesis intends to forecast the pulp production indexed to the stock market value.The
forecast will be made by means of the pulp production variables of the presses and the stock exchange
variables supported by artificial intelligence (AI) technologies, aiming to achieve an effective
planning. To support the decision of efficient production management, in this thesis algorithms
were developed and validated with from five pulp presses, as well as data from other sources, such
as steel production and stock exchange, which were relevant to validate the robustness of the model.
This thesis demonstrated the importance of data processing methods and that they have great relevance
in the model input since they facilitate the process of training and testing the models. The
chosen technologies demonstrated good efficiency and versatility in performing the prediction of
the values of the variables of the equipment, also demonstrating robustness and optimization in
computational processing. The thesis also presents proposals for future developments, namely
in further exploration of these technologies, so that there are market variables that can calibrate
production through forecasts supported on these same variables.Conectividade, mobilidade e análise de dados em tempo real são pré-requisitos para um novo
modelo de gestão inteligente da produção que facilita a comunicação entre máquinas, pessoas e
processos, e usa a tecnologia como motor principal.
Muitos trabalhos na literatura tratam a manutenção e a gestão da produção em abordagens separadas,
mas existe uma correlação entre estas áreas, sendo que a manutenção e as suas políticas
têm como premissa garantir o bom funcionamento dos equipamentos de modo a evitar paragens
desnecessárias na linha de produção.
Com o advento da tecnologia há uma corrida das empresas para solucionar os seus problemas
recorrendo às tecnologias, visando a sua inserção nos conceitos tecnológicos, mais avançados,
tais como as indústrias 4.0 e 5.0, as quais têm como princípio a automatização dos processos.
Esta abordagem junta as tecnologias de sistema de informação, sendo possível fazer o acompanhamento
do funcionamento dos equipamentos e ter a possibilidade de realizar o estudo de padrões
de comportamento dos dados que nos possam alertar para possíveis falhas.
A presente tese pretende prever a produção da pasta de papel indexada às bolsas de valores. A
previsão será feita por via das variáveis da produção da pasta de papel das prensas e das variáveis
da bolsa de valores suportadas em tecnologias de artificial intelligence (IA), tendo como objectivo
conseguir um planeamento eficaz. Para suportar a decisão de uma gestão da produção eficiente,
na presente tese foram desenvolvidos algoritmos, validados em dados de cinco prensas de pasta de
papel, bem como dados de outras fontes, tais como, de Produção de Aço e de Bolsas de Valores,
os quais se mostraram relevantes para a validação da robustez dos modelos.
A presente tese demonstrou a importância dos métodos de tratamento de dados e que os mesmos
têm uma grande relevância na entrada do modelo, visto que facilita o processo de treino e testes dos
modelos. As tecnologias escolhidas demonstraram uma boa eficiência e versatilidade na realização
da previsão dos valores das variáveis dos equipamentos, demonstrando ainda robustez e otimização
no processamento computacional.
A tese apresenta ainda propostas para futuros desenvolvimentos, designadamente na exploração
mais aprofundada destas tecnologias, de modo a que haja variáveis de mercado que possam calibrar
a produção através de previsões suportadas nestas mesmas variáveis