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Polytechnic Institute of Bragança, Portugal

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9 Papers

Distributed Machine Learning and Multi-Agent Systems for Enhanced Attack Detection and Resilience in IoT Networks

Topics: Intrusion Detection and Response; Security Frameworks, Architectures and Protocols; Security in IoT and Edge Computing; AI and Machine Learning for Security

Authors: Gustavo Funchal ¹ , Tiago Pedrosa ¹ , Fernando de la Prieta ² and Paulo Leitão ¹

Affiliations: ¹ Research Centre in Digitalization and Intelligent Robotics (CeDRI), Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, 5300-253 Bragança, Portugal , ² BISITE Digital Innovation Hub, University of Salamanca, Edificio I+D+i, C/ Espejos s/n, 37007, Salamanca, Spain

Digital Object Identifier: 10.5220/0013154400003899

Proceedings of ICISSP 2025, ISBN: 978-989-758-735-1

Quick Abstract

The exponential growth of connected devices, including sensors, mobile devices, and various Internet of Things (IoT) devices, has resulted in a substantial increase in data generation. Traditionally, data analysis involves transferring data to cloud computing systems, leading to latency issues and excessive network traffic. Edge computing emerges as a promising solution by bringing processing closer to the data sources. However, edge computing faces challenges, particularly in terms of limited computational power, which can create constraints in the execution of machine learning (ML) tasks. This paper aims to analyze strategies for distributing ML tasks among multiple nodes based on multi-agent systems (MAS) technology to have a collaborative approach and compare these strategies to provide an overview of best practices for achieving the optimal performance in intrusion detection for Industrial Internet of Things (IIoT). In this way, the well-known CICIoT2023 data set was used, and centralized and distributed ML techniques were implemented, and evaluated. The distributed edge ML approach achieved promising results, presenting an improvement of between 7.73% and 32.18% in the correction of wrong predictions of detection of attacks on IoT devices, significantly improving the precision and recall of the applied techniques.

Data Acquisition System for a Wearable-Based Fall Prevention

Topics: Wearable Healthcare; Body Sensor/Area Networks; Tele-healthcare

Authors: Raul Kaizer ¹ , Leonardo Sestrem ¹ , Tiago Franco ¹ , João Gonçalves ¹ , João Teixeira ^{1

;

2} , José Lima ^{1

;

2} , José Carvalho ^{1

;

2} and Paulo Leitão ^{1

;

2}

Affiliations: ¹ Research Center in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal , ² Associate Laboratory for Sustainability and Technology (Sustec), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal

Digital Object Identifier: 10.5220/0011926500003414

Proceedings of WHC 2023, ISBN: 978-989-758-631-6

Quick Abstract

Reliable ways to treat and monitor patients remotely have been researched and proposed by numerous people. Many of these propositions are under the wearable category due to it usually not requiring deep knowledge to be handled and its durability. Among the many applicable ways, fall monitoring has gained importance as the world population ages and countries aim to increase the quality of life. For it to be possible, there are many ways such as analyzing muscle response, body position, or brain activities, but for most of them, the result ends up being expensive and or inaccurate. With this in mind, this paper brings the development of an acquisition system for electromyography, electrocardiography, body position and temperature. The acquired data is transmitted to the smartphone through Bluetooth Low Energy (BLE) and then sent to a secure cloud to be provided to the physician. In future works, artificial intelligence codes will analyze the data patterns to predict fall occurrences and establish functional electrical stimulation (FES) routines to prevent falls and or treat the patients according to their necessities.

Data Acquisition, Conditioning and Processing System for a Wearable-based Biostimulation

Topics: Health Monitoring Devices; Rehabilitation Technology; Wearable and Mobile Devices; Diagnostic devices; Bioelectronics

Authors: Leonardo Sestrem , Raul Kaizer , João Paulo Gonçalves , Paulo Leitão , João Paulo Teixeira , José Augusto Lima , Tiago Franco and José Augusto Carvalho

Affiliation: Research Center in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal

Digital Object Identifier: 10.5220/0011002300003123

Proceedings of BIODEVICES 2022/BIOSTEC, ISBN: 978-989-758-552-4

Quick Abstract

Data acquisition by electromyography, as well as the muscle stimulation, has become more accessible with the new developments in the wearable technology and medicine. In fact, for treatments, games or sports, it is possible to find examples of the use of muscle signals to analyse specific aspects related, e.g., to disease, injuries or movement impulses. However, these systems are usually expensive, does not integrate data acquisition with the muscle stimulation and does not exhibit an adaptive control behaviour that consider the pathology and the patient response. This paper presents a wearable system that integrates the signal acquisition and the electrostimulation using dry thin-film titanium-based electrodes. The acquired data is transmitted to a mobile application running on a smartphone by using Bluetooth Low Energy (BLE) technology, where it is analysed by employing artificial intelligence algorithms to provide customised treatments for each patient profile and type of pathology, and taking into consideration the feedback of the acquired electromyography signal. The acquired patient’s data is also stored in a secure cloud database to support the physician to analyse and follow-up the clinical results from the rehabilitation process.

Integrating a Multi-Agent Smart Parking System using Cloud Technologies

Topics: Interoperability; Internet of Things; Distributed Systems; Cyber-Social Systems; Emerging Applications of Agent-based Systems

Authors: Milton Boos Junior ¹ , Lucas Sakurada ¹ , Paulo Leitão ¹ , Paulo Alves ¹ , Gleifer Vaz Alves ² , André Pinz Borges ² and Diego Roberto Antunes ²

Affiliations: ¹ Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Bragança, Portugal , ² Federal University of Technology - Paraná (UTFPR), Campus Ponta Grossa, Paraná, Brazil

Digital Object Identifier: 10.5220/0010978300003179

Proceedings of ICEIS 2022, ISBN: 978-989-758-569-2

Quick Abstract

Smart parking (SP) systems are becoming a solution to address the increasing traffic in major cities, which are related to the traffic congestion, unnecessary time spent searching for parking spots, and, consequently, environmental issues. These systems intend to help drivers that are searching for available parking spaces in a given desired location. This paper presents a cloud-based solution to integrate a Multi-Agent System (MAS) for SP, which enables the modularization, scalability and robustness of such large-scale systems. The MAS abstraction is a suitable approach to represent the dynamic features of a SP, where multiple drivers arrive, request, search, and leave the parking spots. The cloud services enable to scale up the use of a MAS, being an intermediary in the communication between the MAS and the end user, providing a broad architecture that involves database, asynchronous functions activated by events and real-time message exchange. The cloud agent-based system was deployed in the parking of an University campus, where users driving bicycles and cars can request and schedule parking slots that are managed in a distributed manner by the MAS. The obtained results show the user friendly interaction with the system, the scalability of the system in terms of drivers and parking spots, as well as the efficient management of the parking spots by the MAS system.

A Real Framework to Apply Collaborative Robots in Upper Limb Rehabilitation

Topic: Rehabilitation Technology

Authors: Lucas de Azevedo Fernandes ¹ , Thadeu Brito ² , Luis Piardi ² , José Lima ³ and Paulo Leitão ²

Affiliations: ¹ Universidade Tecnológica Ferderal do Paraná, Paraná, Brazil , ² CeDRI - Research Centre in Digitalization and Intelligent Robotics, Portugal , ³ INESC TEC - INESC Technology and Science and CeDRI - Research Centre in Digitalization and Intelligent Robotics and Polytechnic Institute of Bragança, Portugal

Digital Object Identifier: 10.5220/0009004501760183

Proceedings of BIODEVICES 2020/BIOSTEC, ISBN: 978-989-758-398-8

Quick Abstract

Rehabilitation is an important recovery process from dysfunctions that improves the patient’s activities of daily living. On the other hand, collaborative robotic applications, where humans and machines can share the same space, are increasing once it allows splitting a task between the accuracy of a robot and the ability and flexibility of a human. This paper describes an innovative approach that uses a collaborative robot to support the rehabilitation of the upper limb of patients, complemented by an intelligent system that learns and adapts its behaviour according to the patient’s performance during the therapy. This intelligent system implements the reinforcement learning algorithm, which makes the system robust and independent of the path of motion. The validation of the proposed approach uses a UR3 collaborative robot training in a real environment. The main control is the resistance force that the robot is able to do against the movement performed by the human arm.

Automatic Nurse Allocation based on a Population Algorithm for Home Health Care

Authors: Filipe Alves ^{1

;

2} , Ana Maria A. C. Rocha ¹ , Ana I. Pereira ^{1

;

2} and Paulo Leitão ²

Affiliations: ¹ ALGORITMI Center, University of Minho, Braga, Portugal , ² Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Bragança, Portugal

Digital Object Identifier: 10.5220/0009386103950402

Proceedings of ND2A 2020, ISBN: 978-989-758-396-4

Quick Abstract

The provision of home health care services is becoming an important research area, mainly because in Portugal the population is ageing and it is necessary to perform home care services. Home care visits are organized taking into account the medical treatments and general support that elder/sick people need at home. This health service can be provided by nurses teams from Health Units, requiring some logistics for this purpose. Usually, the visits are manually planned and without computational support. The main goal of this work is to carry out the automatic nurse’s allocation of home care visits, of one Bragança Health Unit, in order to minimize the nurse’s workload balancing, spent time in all home care visits and, consequently, reduce the costs involved. The developed methodology was coded in MatLab Software and the problems were efficiently solved by the particle swarm optimization method. The nurse’s allocation solution of home care visits for the presented case study shows a significant improvement and reduction in the maximum time, in the nurse workload balancing, as well as the patients waiting time.

A Multi-objective Approach to the Optimization of Home Care Visits Scheduling

Topics: Decision Support Systems; Optimization; OR in Health

Authors: Filipe Alves ¹ , Lino Costa ² , Ana Maria A. C. Rocha ² , Ana I. Pereira ¹ and Paulo Leitão ³

Affiliations: ¹ Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Bragança, Portugal, Algoritmi R&D Centre, University of Minho, Braga and Portugal , ² Department of Production and Systems, Algoritmi R&D Centre, University of Minho, Braga and Portugal , ³ Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Bragança and Portugal

Digital Object Identifier: 10.5220/0007565704350442

Proceedings of ICORES 2019, ISBN: 978-989-758-352-0

Quick Abstract

Due to the increasing of life expectancy in the developed countries, the demand for home health care services is growing dramatically. Usually, home services are planned manually and lead to various optimization problems in their activities. In this sense, health units are confronted with appropriate scheduling which may contain multiple, often conflicting, objectives such as minimizing the costs related to the traveling distance while minimizing the traveling time. In order to analyze and discuss different trade-offs between these objectives, it is proposed a multi-objective approach to home health care scheduling in which the problem is solved using the Tchebycheff method and a Genetic algorithm. Different alternative solutions are presented to the decision maker that taking into account his/her preferences chooses the appropriate solution. A problem with real data from a home health care service is solved. The results highlight the importance of a multi-objective approach to optimize and support decision making in home health care services. Moreover, this approach provides efficient and good solutions in a reasonable time.

Related Ontology Subjects/Areas/Topics (18)

Applications	Decision Support Systems	Operational Research
Artificial Intelligence	Decision Support Systems, Remote Data Analysis	Optimization
Business Analytics	Health Engineering and Technology Applications	OR in Health
Cardiovascular Technologies	Knowledge Discovery and Information Retrieval	Pattern Recognition
Computing and Telecommunications in Cardiology	Knowledge-Based Systems	Software Engineering
Data Engineering	Methodologies and Technologies	Symbolic Systems

Distributing Intelligence among Cloud, Fog and Edge in Industrial Cyber-physical Systems

Topics: Machine Learning in Control Applications; Distributed Control Systems; Software Agents for Intelligent Control Systems; Industrial Internet of Things

Authors: Jonas Queiroz ¹ , Paulo Leitão ² , José Barbosa ² and Eugénio Oliveira ¹

Affiliations: ¹ University of Porto, Faculty of Engineering - LIACC, Porto and Portugal , ² Research Center in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Bragança and Portugal

Digital Object Identifier: 10.5220/0007979404470454

Proceedings of ICINCO 2019, ISBN: 978-989-758-380-3

Quick Abstract

The 4th industrial revolution advent promotes the reorganization of the traditional hierarchical automation systems towards decentralized Cyber-Physical Systems (CPS). In this context, Artificial Intelligence (AI) can address the new requirements through the use of data-driven and distributed problem solving approaches, such those based on Machine-Learning and Multi-agent Systems. Although their promising perspectives to enable and manage intelligent Internet of Things environments, the traditional Cloud-based AI approaches are not suitable to handle many industrial scenarios, constrained by responsiveness and data sensitive. The solution lies in taking advantage of Edge and Fog computing to create a decentralized multi-level data analysis computing infrastructure that supports the development of industrial CPS. However, this is not a straightforward task, posing several challenges and demanding new approaches and technologies. In this context, this work discusses the distribution of intelligence along Cloud, Fog and Edge computing layers in industrial CPS, leveraging some research challenges and future directions.

Related Ontology Subjects/Areas/Topics (5)

Distributed Control Systems		Intelligent Control Systems and Optimization		Software Agents for Intelligent Control Systems
Informatics in Control, Automation and Robotics		Machine Learning in Control Applications

Periodic Vehicle Routing Problem in a Health Unit

Topics: Decision Support Systems; Scheduling; Routing; OR in Health

Authors: F. Alves ¹ , F. Alvelos ² , A. M. A. C. Rocha ² , Ana I. Pereira ¹ and Paulo Leitão ³

Affiliations: ¹ Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal, Algoritmi R&D Centre, University of Minho, Braga and Portugal , ² Department of Production and Systems, Algoritmi Research Centre, University of Minho, Braga and Portugal , ³ Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança and Portugal

Digital Object Identifier: 10.5220/0007392803840389

Proceedings of ICORES 2019, ISBN: 978-989-758-352-0

Quick Abstract

In logistics of home health care services in the Health Units, the managers and nurses need to carry out the schedule and the vehicles routes for the provision of care at the patients’ homes. Currently, in Portugal, these services are increasingly used but the problem is still, usually, solved manually and without computational resources. The increased demand for home health care due to the boost of the elderly people number entails a high associated cost which, sometimes, does not guarantee the quality of the service. In this sense, the periodic vehicle routing problem is a generalization of the classical vehicle routing problem in which routes are determined for a time horizon of several days. In this work, it is provided a periodic vehicle routing problem applied in the Health Unit in Bragança. An integer linear programming formulation for the real database, allowed to solve the problem in an efficient and optimized way using the CPLEX R software.

Related Ontology Subjects/Areas/Topics (17)

Applications	Decision Support Systems	Pattern Recognition
Artificial Intelligence	Decision Support Systems, Remote Data Analysis	Routing
Business Analytics	Health Engineering and Technology Applications	Scheduling
Cardiovascular Technologies	Knowledge-Based Systems	Software Engineering
Computing and Telecommunications in Cardiology	Operational Research	Symbolic Systems
Data Engineering	OR in Health