http://hdl.handle.net/10071/5704 2026-04-05T21:16:40Z 2026-04-05T21:16:40Z Santos, T. Marinheiro, R. N. Brito e Abreu, F. http://hdl.handle.net/10071/35870 2026-01-09T09:54:07Z 2025-01-01T00:00:00Z

Título próprio: Wireless crowd detection for smart overtourism mitigation Autoria: Santos, T.; Marinheiro, R. N.; Brito e Abreu, F. Editor: Kornyshova, Elena; Deneckère, Rébecca; Brinkkemper, Sjaak Resumo: Overtourism occurs when the number of tourists exceeds the carrying capacity of a destination, leading to negative impacts on the environment, culture, and quality of life for residents. By monitoring overtourism, destination managers can identify areas of concern and implement measures to mitigate the negative impacts of tourism while promoting smarter tourism practices. This can help ensure that tourism benefits both visitors and residents while preserving the natural and cultural resources that make these destinations so appealing. This chapter describes a low-cost approach to monitoring overtourism based on mobile devices’ wireless activity. A flexible architecture was designed for a smart tourism toolkit to be used by small and medium-sized enterprises (SMEs) in crowding management solutions, to build better tourism services, improve efficiency and sustainability, and reduce the overwhelming feeling of pressure in critical hotspots. The crowding sensors count the number of surrounding mobile devices, by detecting trace elements of wireless technologies, overcoming MAC address randomization. They run detection programs for several technologies, and fingerprinting analysis results are only stored locally in an anonymized database, without infringing privacy rights. After that edge computing, sensors communicate the crowding information to a cloud server, by using a variety of uplink techniques to mitigate local connectivity limitations, something that has been often disregarded in alternative approaches. Field validation of sensors has been performed on Iscte’s campus before their planned use in other locations, such as the Pena Palace in Sintra. Preliminary results show that these sensors can be deployed in multiple scenarios and provide a diversity of spatiotemporal crowding data and analysis in order to promote smart engineering techniques to be used for tourism overcrowding management.

2025-01-01T00:00:00Z Ferreira, R. Cardoso, E. Oliveira, J. http://hdl.handle.net/10071/31537 2024-04-15T08:22:00Z 2023-01-01T00:00:00Z

Título próprio: How can LMS affect student’s motivation and engagement? Autoria: Ferreira, R.; Cardoso, E.; Oliveira, J. Editor: Ruben Pereira; Isaias Bianchi; Álvaro Rocha Resumo: Technology has revolutionized the education system. Many tools such as Learning Management Systems (LMS) were developed to enhance the learning process. With this new technology, teachers and universities can explore options otherwise difficult to implement. Keeping students engaged is one of the biggest challenges that educational institutions face. Students’ motivation, engagement, and performance can be affected by using LMS. Strategies like self-regulated learning, gamification, and real-time at-risk student detection can be more easily implemented. The analysis of the effects of LMS on learning is made in form of a systematic literature review (SLR). 33 studies published after 2017 were extracted for full-text analysis.

2023-01-01T00:00:00Z Antonio, N. de Almeida, A. Nunes, L. http://hdl.handle.net/10071/29001 2023-07-14T09:07:09Z 2022-01-01T00:00:00Z

Título próprio: Data mining and predictive analytics for E-tourism Autoria: Antonio, N.; de Almeida, A.; Nunes, L. Editor: Zheng Xiang; Matthias Fuchs; Ulrike Gretzel; Wolfram Höpken Resumo: Computers and devices, today ubiquitous in our daily life, foster the generation of vast amounts of data. Turning data into information and knowledge is the core of data mining and predictive analytics. Data mining uses machine learning, statistics, data visualization, databases, and other computer science methods to find patterns in data and extract knowledge from information. While data mining is usually associated with causal-explanatory statistical modeling, predictive analytics is associated with empirical prediction modeling, including the assessment of the quality of the prediction. This chapter intends to offer the readers, even those unfamiliar with this topic, a general overview of the key concepts and potential applications of data mining and predictive analytics and to help them to successfully apply e-tourism concepts in their research projects. As such, the chapter presents the fundamentals and common definitions of/in data mining and predictive analytics, including the types of problems to which it can be applied and the most common methods and techniques employed. The chapter also explains what is known as the life cycle of data mining and predictive analytics projects, describing the tasks that compose the most widely employed process model, both for industry and academia: the Cross-Industry Standard Process for Data Mining, CRISP-DM.

2022-01-01T00:00:00Z Moura, J. Marinheiro, R. N. Silva, J. http://hdl.handle.net/10071/28976 2023-07-12T09:16:15Z 2022-01-01T00:00:00Z

Título próprio: Game theory for cooperation in multi-access edge computing Autoria: Moura, J.; Marinheiro, R. N.; Silva, J. Resumo: Cooperative strategies amongst network players can improve network performance and spectrum utilization in future networking environments. Game Theory is very suitable for these emerging scenarios, since it models high-complex interactions among distributed decision makers. It also finds the more convenient management policies for the diverse players (e.g., content providers, cloud providers, edge providers, brokers, network providers, or users). These management policies optimize the performance of the overall network infrastructure with a fair utilization of their resources. This chapter discusses relevant theoretical models that enable cooperation amongst the players in distinct ways through, namely, pricing or reputation. In addition, the authors highlight open problems, such as the lack of proper models for dynamic and incomplete information scenarios. These upcoming scenarios are associated to computing and storage at the network edge, as well as, the deployment of large-scale IoT systems. The chapter finalizes by discussing a business model for future networks.

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