Revolutionising the Quality of Life: The Role of Real-Time Sensing in Smart Cities
To further evolve urban quality of life, this paper explores the potential of crowdsensing and crowdsourcing in the context of smart cities. To aid urban planners and residents in understanding the nuances of day-to-day urban dynamics, we actively pursue the improvement of data visualisation tools that can adapt to changing conditions. An architecture was created and implemented that ensures secure and easy connectivity between various sources, such as a network of Internet of Things (IoT) devices, to merge with crowdsensing data and use them efficiently. In addition, we expanded the scope of our study to include the development of mobile and online applications, emphasizing the integration of autonomous and geo-surveillance. The main findings highlight the importance of sensor data in urban knowledge. Their incorporation via Tepresentational State Transfer (REST) Application Programming Interface (APIs) improves data access and informed decision-making, and dynamic data visualisation provides better insights. The geofencing of the application encourages community participation in urban planning and resource allocation, supporting sustainable urban innovation.
ORCID
Behaviour of Machine Learning algorithms in the classification of energy consumption in school buildings
Abstract The significance of energy efficiency in the development of smart cities cannot be overstated. It is essential to have a clear understanding of the current energy consumption (EC) patterns in both public and private buildings. One way to achieve this is by employing machine learning classification algorithms, which offer a broader perspective on the factors influencing EC. These algorithms can be applied to real data from databases, making them valuable tools for smart city applications. In this paper, our focus is specifically on the EC of public schools in a Portuguese city, as this plays a crucial role in designing a Smart City. By utilizing a comprehensive dataset on school EC, we thoroughly evaluate multiple ML algorithms. The objective is to identify the most effective algorithm for classifying average EC patterns. The outcomes of this study hold significant value for school administrators and facility managers. By leveraging the predictions generated from the selected algorithm, they can optimize energy usage and, consequently, reduce costs. The use of a comprehensive dataset ensures the reliability and accuracy of our evaluations of various ML algorithms for EC classification.
Keywords
Future Perspectives in Healthcare: An Analysis of Augmented Reality and Spatial Computing in Hospital Environments
This article investigates the applicability of Augmented Reality (AR) and Spatial Computing in hospital environments, exploring how these technologies can enhance interaction between patients and healthcare professionals as well as the efficiency of medical procedures. Through a systematic literature review using the PRISMA methodology, relevant articles highlighting the potential of these technologies in the hospital setting were selected and analysed. The results suggest a growing adoption of AR and Spatial Computing, driven by the advancement of 5G technology, with the potential to improve diagnostic accuracy, the effectiveness of surgical procedures, and the educational experience of healthcare professionals. The study concludes that the integration of these emerging technologies promises to transform hospital environments, emphasising the need to overcome technical, ethical, and privacy challenges to maximise their positive impact on health.
Keywords
Transforming Sales in Healthcare: The Impact of Augmented Reality and Spatial Computing on the Medical Equipment Industry
Lately, there has been a surge of interest in spatial computing, which combines AR, VR and other technologies, especially in the health industry. While a lot has been done on its applications in such clinical aspects as surgeries and patient experience enhancement, there is less info on its application in medical equipment marketing in hospitals. This paper presents the current trends and practices in the use of networks of immersive technology in marketing medical devices. Besides surveying the secondary literature, the present investigation examines three case studies in this sector. Nowadays, most applications are not focused on promoting medical devices, making it easy to achieve innovation in this field mainly by utilising immersive technology, which has been developed for other industries.
Keywords
Data Platforms for Real-time Insights in Healthcare: Systematic Review
The ever-growing usage and popularity of Internet of Things devices, coupled with Big Data technologies and machine learning algorithms, have allowed for data engineers to explore new opportunities in healthcare and continuous care. Furthermore, there is a need to reduce the gap on time from when information is created to when actions and insights can be offered. However, a challenge in implementing a large-scale data processing architecture is deciding which tools are appropriate, and how to apply them in the best way possible. For example, streaming systems are now mature enough that hospitals worldwide can use their extremely large datasets, along with data producers, to predict and influence future events. Thus, the main objective of this systematic review is to identify the state-of-the-art in data platforms on healthcare that allow the creation of metrics and actions in real-time. The PRISMA guideline for reporting systematic reviews was implemented to deliver a transparent and consistent report, validating the technological advances in a critical sector. Multiple pertinent articles and papers were retrieved from the SCOPUS abstract and citation database on May 13, 2022, using several relevant keywords to identify potentially relevant documents published from January 2020 onward. These documents must have already been published in English and been already published, and accessible through the B-ON consortium that allows Portuguese students to legally download from most publishers. Over seven studies have been selected for deeper discussion based on their relevance and impact for this review, showcasing their main objectives, data sources, and tools used, as well as their approaches for interoperability and support of machine learning algorithms for decision support. In closing, the collected articles have shown that while Big Data is currently in use at health institutions of all sizes, the ability of processing large amounts of data from sensors and events, and notifying stakeholders as quickly as possible is still in its infancy.
Keywords
Software tools for conducting real-time information processing and visualization in industry: An up-to-date review
The processing of information in real-time (through the processing of complex events) has become an essential task for the optimal functioning of manufacturing plants. Only in this way can artificial intelligence, data extraction, and even business intelligence techniques be applied, and the data produced daily be used in a beneficent way, enhancing automation processes and improving service delivery. Therefore, professionals and researchers need a wide range of tools to extract, transform, and load data in real-time efficiently. Additionally, the same tool supports or at least facilitates the visualization of this data intuitively and interactively. The review presented in this document aims to provide an up-to-date review of the various tools available to perform these tasks. Of the selected tools, a brief description of how they work, as well as the advantages and disadvantages of their use, will be presented. Furthermore, a critical analysis of overall operation and performance will be presented. Finally, a hybrid architecture that aims to synergize all tools and technologies is presented and discussed.
Keywords