| Title | A context agnostic air quality service to exploit data in the IoE era |
|---|---|
| ID_Doc | 39915 |
| Authors | Aiello, G; Camillò, A; Del Coco, M; Giangreco, E; Pinnella, M; Pino, S; Storelli, D |
| Title | A context agnostic air quality service to exploit data in the IoE era |
| Year | 2019 |
| Published | |
| Abstract | The upcoming IoE paradigm is taking the IoT era to a new shift, and that because of the natural inter-connection of processes, people, devices and stakeholders. From the smart city perspective, the main goal is to make well-informed decisions, on the base of a variable number of sensors and sources, exposing different data with different protocols and structures. The urban contexts may change, and with them, the number of sensors deployed. The novel smart city service must go beyond an integration strategy, it needs an exploitation model to optimally retrieve useful and highly contextualized information. In this paper we focus on the development of a model which fuses together the IoE potential and machine learning techniques for the cognitive smart city: retrive useful intelligent information, optimally exploiting the infrastructure the specific physical context may offer. We propose an approach and related techniques for realizing context agnostic services, namely services that do not depend on the enabling infrastructure beneath. The purpose is to create an IoE-based self-contextualizing service, which potentially consider the entire range of data that is being collected in smart cities and use such data to provide highly-personalized information about each environment, i.e., information that best suits the context of each Smart City. To prove the proposed context agnostic service, we take into account the air quality observation issue: we provide two high-contextualized informative services to leverage data related to two different physical environments, thus building location awareness for different geographic areas and stakeholders. But still managed by the same application which can adapts itself. Finally we present the evaluation of this prototype to illustrate the benefits of our solution and the future work. |
Similar Articles
| ID | Score | Article |
|---|---|---|
36095
|
Money, WH; Cohen, S Leveraging AI and Sensor Fabrics to Evolve Smart City Solution Designs(2019) | |
| 38139
|
Lymperis, D; Goumopoulos, C SEDIA: A Platform for Semantically Enriched IoT Data Integration and Development of Smart City Applications(2023)Future Internet, 15, 8 | |
| 41644
|
Abbas, Q; Ahmad, G; Alyas, T; Alghamdi, T; Alsaawy, Y; Alzahrani, A Revolutionizing Urban Mobility: IoT-Enhanced Autonomous Parking Solutions with Transfer Learning for Smart Cities(2023)Sensors, 23, 21 | |
| 44027
|
Zhang, NY; Chen, HJ; Chen, X; Chen, JY Semantic Framework of Internet of Things for Smart Cities: Case Studies(2016)Sensors, 16, 9 | |
| 42789
|
Santos, J; Vanhove, T; Sebrechts, M; Dupont, T; Kerckhove, W; Braem, B; Van Seghbroeck, G; Wauters, T; Leroux, P; Latré, S; Volckaert, B; De Turck, F City of Things: Enabling Resource Provisioning in Smart Cities(2018)Ieee Communications Magazine, 56, 7 | |
| 39788
|
Kaginalkar, A; Kumar, S; Gargava, P; Niyogi, D Review of urban computing in air quality management as smart city service: An integrated IoT, AI, and cloud technology perspective(2021) |