23 research outputs found
Multi-Agent Decision Support Tool to Enable Interoperability among Heterogeneous Energy Systems
Worldwide electricity markets are undergoing a major restructuring process. One of the main reasons for the ongoing changes is to enable the adaptation of current market models to the new paradigm that arises from the large-scale integration of distributed generation sources. In order to deal with the unpredictability caused by the intermittent nature of the distributed generation and the large number of variables that contribute to the energy sector balance, it is extremely important to use simulation systems that are capable of dealing with the required complexity. This paper presents the Tools Control Center (TOOCC), a framework that allows the interoperability between heterogeneous energy and power simulation systems through the use of ontologies, allowing the simulation of scenarios with a high degree of complexity, through the cooperation of the individual capacities of each system. A case study based on real data is presented in order to demonstrate the interoperability capabilities of TOOCC. The simulation considers the energy management of a microgrid of a real university campus, from the perspective of the network manager and also of its consumers/producers, in a projection for a typical day of the winter of 2050.This work has been developed in the scope of the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 641794 (project DREAM-GO); CONTEST project - SAICT-POL/23575/2016; and has also been supported by FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013.info:eu-repo/semantics/publishedVersio
Recommended from our members
ARIES: a novel multivariate intrusion detection system for smart grid
The advent of the Smart Grid (SG) raises severe cybersecurity risks that can lead to devastating consequences. In this paper, we present a novel anomaly-based Intrusion Detection System (IDS), called ARIES (smArt gRid Intrusion dEtection System), which is capable of protecting efficiently SG communications. ARIES combines three detection layers that are devoted to recognising possible cyberattacks and anomalies against (a) network flows, (b) Modbus/Transmission Control Protocol (TCP) packets and (c) operational data. Each detection layer relies on a Machine Learning (ML) model trained using data originating from a power plant. In particular, the first layer (network flow-based detection) performs a supervised multiclass classification, recognising Denial of Service (DoS), brute force attacks, port scanning attacks and bots. The second layer (packet-based detection) detects possible anomalies related to the Modbus packets, while the third layer (operational data based detection) monitors and identifies anomalies upon operational data (i.e., time series electricity measurements). By emphasising on the third layer, the ARIES Generative Adversarial Network (ARIES GAN) with novel error minimisation functions was developed, considering mainly the reconstruction difference. Moreover, a novel reformed conditional input was suggested, consisting of random noise and the signal features at any given time instance. Based on the evaluation analysis, the proposed GAN network overcomes the efficacy of conventional ML methods in terms of Accuracy and the F1 score
Exploiting resource contention in highly mobile environments and its application to vehicular ad-hoc networks
As network resources are shared between many users, resource management must be a key part of any communication system as it is needed to provide seamless communication and to ensure that applications and servers receive their required Quality-of-Service. However, mobile environments also need to consider handover issues. Furthermore, in a highly mobile environment, traditional reactive approaches to handover are inadequate and thus proactive techniques have been investigated. Recent research in proactive handover techniques, defined two key parameters: Time Before Handover and Network Dwell Time for a mobile node in any given networking topology. Using this approach, it is possible to enhance resource management in common networks using probabilistic mechanisms because it is possible to express contention for resources in terms of: No Contention, Partial Contention and Full Contention. This proactive approach is further enhanced by the use of a contention queue to detect contention between incoming requests and those waiting for service. This paper therefore presents a new methodology to support proactive resource allocation for future networks such as Vehicular Ad-Hoc Networks. The proposed approach has been applied to a vehicular testbed and results are presented that show that this approach can improve overall network performance in mobile heterogeneous environments
Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service
This paper formulates a new problem for the optimal placement of Unmanned Aerial Vehicles (UAVs) geared towards wireless coverage provision for Voice over WiFi (VoWiFi) service to a set of ground users confined in an open area. Our objective function is constrained by coverage and by VoIP speech quality and minimizes the ratio between the number of UAVs deployed and energy efficiency in UAVs, hence providing the layout that requires fewer UAVs per hour of service. Solutions provide the number and position of UAVs to be deployed, and are found using well-known heuristic search methods such as genetic algorithms (used for the initial deployment of UAVs), or particle swarm optimization (used for the periodical update of the positions). We examine two communication services: (a) one bidirectional VoWiFi channel per user; (b) single broadcast VoWiFi channel for announcements. For these services, we study the results obtained for an increasing number of users confined in a small area of 100 m2 as well as in a large area of 10,000 m2. Results show that the drone turnover rate is related to both users’ sparsity and the number of users served by each UAV. For the unicast service, the ratio of UAVs per hour of service tends to increase with user sparsity and the power of radio communication represents 14–16% of the total UAV energy consumption depending on ground user density. In large areas, solutions tend to locate UAVs at higher altitudes seeking increased coverage, which increases energy consumption due to hovering. However, in the VoWiFi broadcast communication service, the traffic is scarce, and solutions are mostly constrained only by coverage. This results in fewer UAVs deployed, less total power consumption (between 20% and 75%), and less sensitivity to the number of served users.Junta de Andalucía Beca 2020/00000172Unión Europea FEDER 2014-202
Argumentation dialogues in web-based GDSS: an approach using machine learning techniques
Tese de doutoramento em InformaticsA tomada de decisão está presente no dia a dia de qualquer pessoa, mesmo que muitas vezes ela
não tenha consciência disso. As decisões podem estar relacionadas com problemas quotidianos, ou
podem estar relacionadas com questões mais complexas, como é o caso das questões organizacionais.
Normalmente, no contexto organizacional, as decisões são tomadas em grupo.
Os Sistemas de Apoio à Decisão em Grupo têm sido estudados ao longo das últimas décadas com o
objetivo de melhorar o apoio prestado aos decisores nas mais diversas situações e/ou problemas a resolver.
Existem duas abordagens principais à implementação de Sistemas de Apoio à Decisão em Grupo:
a abordagem clássica, baseada na agregação matemática das preferências dos diferentes elementos do
grupo e as abordagens baseadas na negociação automática (e.g. Teoria dos Jogos, Argumentação, entre
outras).
Os atuais Sistemas de Apoio à Decisão em Grupo baseados em argumentação podem gerar uma
enorme quantidade de dados. O objetivo deste trabalho de investigação é estudar e desenvolver modelos
utilizando técnicas de aprendizagem automática para extrair conhecimento dos diálogos argumentativos
realizados pelos decisores, mais concretamente, pretende-se criar modelos para analisar, classificar e
processar esses dados, potencializando a geração de novo conhecimento que será utilizado tanto por
agentes inteligentes, como por decisiores reais. Promovendo desta forma a obtenção de consenso entre
os membros do grupo. Com base no estudo da literatura e nos desafios em aberto neste domínio,
formulou-se a seguinte hipótese de investigação - É possível usar técnicas de aprendizagem automática
para apoiar diálogos argumentativos em Sistemas de Apoio à Decisão em Grupo baseados na web.
No âmbito dos trabalhos desenvolvidos, foram aplicados algoritmos de classificação supervisionados
a um conjunto de dados contendo argumentos extraídos de debates online, criando um classificador
de frases argumentativas que pode classificar automaticamente (A favor/Contra) frases argumentativas
trocadas no contexto da tomada de decisão. Foi desenvolvido um modelo de clustering dinâmico para
organizar as conversas com base nos argumentos utilizados. Além disso, foi proposto um Sistema de
Apoio à Decisão em Grupo baseado na web que possibilita apoiar grupos de decisores independentemente
de sua localização geográfica. O sistema permite a criação de problemas multicritério e a configuração
das preferências, intenções e interesses de cada decisor. Este sistema de apoio à decisão baseado na
web inclui os dashboards de relatórios inteligentes que são gerados através dos resultados dos trabalhos
alcançados pelos modelos anteriores já referidos. A concretização de cada um dos objetivos permitiu
validar as questões de investigação identificadas e assim responder positivamente à hipótese definida.Decision-making is present in anyone’s daily life, even if they are often unaware of it. Decisions can be
related to everyday problems, or they can be related to more complex issues, such as organizational
issues. Normally, in the organizational context, decisions are made in groups.
Group Decision Support Systems have been studied over the past decades with the aim of improving
the support provided to decision-makers in the most diverse situations and/or problems to be solved.
There are two main approaches to implementing Group Decision Support Systems: the classical approach,
based on the mathematical aggregation of the preferences of the different elements of the group, and the
approaches based on automatic negotiation (e.g. Game Theory, Argumentation, among others).
Current argumentation-based Group Decision Support Systems can generate an enormous amount
of data. The objective of this research work is to study and develop models using automatic learning techniques
to extract knowledge from argumentative dialogues carried out by decision-makers, more specifically,
it is intended to create models to analyze, classify and process these data, enhancing the generation
of new knowledge that will be used both by intelligent agents and by real decision-makers. Promoting in
this way the achievement of consensus among the members of the group. Based on the literature study
and the open challenges in this domain, the following research hypothesis was formulated - It is possible
to use machine learning techniques to support argumentative dialogues in web-based Group Decision
Support Systems.
As part of the work developed, supervised classification algorithms were applied to a data set containing
arguments extracted from online debates, creating an argumentative sentence classifier that can
automatically classify (For/Against) argumentative sentences exchanged in the context of decision-making.
A dynamic clustering model was developed to organize conversations based on the arguments used. In
addition, a web-based Group Decision Support System was proposed that makes it possible to support
groups of decision-makers regardless of their geographic location. The system allows the creation of multicriteria
problems and the configuration of preferences, intentions, and interests of each decision-maker.
This web-based decision support system includes dashboards of intelligent reports that are generated
through the results of the work achieved by the previous models already mentioned. The achievement of
each objective allowed validation of the identified research questions and thus responded positively to the
defined hypothesis.I also thank to Fundação para a Ciência e a Tecnologia, for the Ph.D. grant funding with the reference: SFRH/BD/137150/2018
RFID application in a multi-agent cyber physical manufacturing system
In manufacturing supply chains with labour-intensive operations and processes, individuals perform various types of manual tasks and quality checks. These operations and processes embrace engagement with various forms of paperwork, regulation obligations and external agreements between multiple stakeholders. Such manual activities can increase human error and near misses, which may ultimately lead to a lack of productivity and performance. In this paper, a multi-agent cyber-physical system (CPS) architecture with radio frequency identification (RFID) technology is presented to assist inter-layer interactions between different manufacturing phases on the shop floor and external interactions with other stakeholders within a supply chain. A dynamic simulation model in the AnyLogic software is developed to implement the CPS-RFID solution by using the agent-based technique. A case study from cryogenic warehousing in cell and gene therapy has been chosen to test the validity of the presented CPS-RFID architecture. The analyses of the simulation results show improvement in efficiency and productivity, in terms of resource time-in-syste
Exploiting resource contention in highly mobile environments and its application to vehicular ad-hoc networks
As network resources are shared between many users, resource management must be a key part of any communication system as it is needed to provide seamless communication and to ensure that applications and servers receive their required Quality-of-Service. However, mobile environments also need to consider handover issues. Furthermore, in a highly mobile environment, traditional reactive approaches to handover are inadequate and thus proactive techniques have been investigated. Recent research in proactive handover techniques, defined two key parameters: Time Before Handover and Network Dwell Time for a mobile node in any given networking topology. Using this approach, it is possible to enhance resource management in common networks using probabilistic mechanisms because it is possible to express contention for resources in terms of: No Contention, Partial Contention and Full Contention. This proactive approach is further enhanced by the use of a contention queue to detect contention between incoming requests and those waiting for service. This paper therefore presents a new methodology to support proactive resource allocation for future networks such as Vehicular Ad-Hoc Networks. The proposed approach has been applied to a vehicular testbed and results are presented that show that this approach can improve overall network performance in mobile heterogeneous environments
Internet of Things and Sensors Networks in 5G Wireless Communications
This book is a printed edition of the Special Issue Internet of Things and Sensors Networks in 5G Wireless Communications that was published in Sensors
Internet of Things and Sensors Networks in 5G Wireless Communications
This book is a printed edition of the Special Issue Internet of Things and Sensors Networks in 5G Wireless Communications that was published in Sensors
Internet of Things and Sensors Networks in 5G Wireless Communications
The Internet of Things (IoT) has attracted much attention from society, industry and academia as a promising technology that can enhance day to day activities, and the creation of new business models, products and services, and serve as a broad source of research topics and ideas. A future digital society is envisioned, composed of numerous wireless connected sensors and devices. Driven by huge demand, the massive IoT (mIoT) or massive machine type communication (mMTC) has been identified as one of the three main communication scenarios for 5G. In addition to connectivity, computing and storage and data management are also long-standing issues for low-cost devices and sensors. The book is a collection of outstanding technical research and industrial papers covering new research results, with a wide range of features within the 5G-and-beyond framework. It provides a range of discussions of the major research challenges and achievements within this topic