Smart City Gnosys
Smart city article details
| Title | Exploring Architectural Choices And Emerging Challenges In Data Management For Iot: A Focus On Digital Innovation And Smart Cities |
|---|---|
| ID_Doc | 25486 |
| Authors | Barra S.; D'Alessandro F.; Sosovskyy O. |
| Year | 2024 |
| Published | UMAP 2024 - Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization |
| DOI | http://dx.doi.org/10.1145/3631700.3665238 |
| Abstract | Digital innovation is an important and evolving process that refers to the transformation and integration of digital technologies into various aspects of everyday life and work. In this context, innovative digital solutions are often adopted and developed, including the Internet of Things (IoT). In order to fully understand the potential, trends, and challenges related to the implementation of these technologies in data management, we seek to identify the most promising architectural choices for an IoT infrastructure from the perspective of key aspects in the context of Data Management. In this way, we aim to provide a comprehensive overview of IoT systems to guide organizations towards more informed decisions, offering cutting-edge solutions and contributing to digital transformation. This work aims to examine aspects such as Data Collection, Data Aggregation, Data Integration, Data Security, Data Retention, and Data Analysis, with particular attention to emerging challenges in these contexts. Within the context of digital innovation, our study focuses on several areas of interest. For example, in the field of smart cities, we explore how the use of Cloud, Fog, and Edge Computing technologies can improve video surveillance and promote the use of Artificial Intelligence (AI) for better management of smart cities. Cloud computing, characterized by centralized data processing and storage, represents a well established paradigm of IoT infrastructure. Fog computing extends these capabilities to the network edge, bringing computation and data storage closer to the data source, thereby reducing latency and enhancing real-time processing. Edge computing takes this concept further by processing data directly at or near the data source, minimizing the need for data to traverse long distances to centralized hubs, thus enabling even faster response times and improved efficiency. Additionally, we examine how human-centric methods and user modeling tools can facilitate the transition to intelligent environments, such as workplaces, healthcare, and cities. Considering the emerging challenges in IoT data management, our work provides an overview of the solutions offered by Cloud, Fog, and Edge Computing, enabling industries and small and medium-sized enterprises to adopt targeted and sustainable digital transformation strategies. © 2024 ACM. |
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