iKaaS Data Modeling: A Data Model for Community Services and Environment Monitoring in Smart City

Hashimoto, Kazuo; Yamada, Keiji; Tabata, Kenichi; Oda, Michio; Suganuma, Takuo; Rahim, Abdur; Vlacheas, Panagiotis; Stavroulaki, Vera; Kelaidonis, Dimitrios; Γεωργακόπουλος, Ανδρέας

doi:10.1109/icac.2015.64

Cited by 3 publications

(1 citation statement)

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“…Most of the current smart cities are based on networks of static sensors and cameras, which are distributed in points of interest throughout the city [12]. However, some recent projects integrate sensors in public transportation systems, overcoming some of the issues derived from fixed sensors [13]. Moreover, there are research works that go further proposing crowdsourcing schemes, in which the sensors are installed in private vehicles and mobile devices [14].…”

Section: Related Workmentioning

confidence: 99%

SwarmCity project: monitoring traffic, pedestrians, climate, and pollution with an aerial robotic swarm

Roldán‐Gómez

Garcia-Aunon

Mazariegos

et al. 2020

Pers Ubiquit Comput

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Smart cities have emerged as a strategy to solve problems that current cities face, such as traffic, security, resource management, waste, and pollution. Most of the current approaches are based on deploying large numbers of sensors throughout the city and have some limitations to get relevant and updated data. In this paper, as an extension of our previous investigations, we propose a robotic swarm to collect the data of traffic, pedestrians, climate, and pollution. This data is sent to a base station, where it is treated to generate maps and presented in an immersive interface. To validate these developments, we use a virtual city called SwarmCity with models of traffic, pedestrians, climate, and pollution based on real data. The whole system has been tested with several subjects to assess whether the information collected by the drones, processed in the base station, and represented in the virtual reality interface is appropriate. Results show that the complete solution, i.e., fleet control, data fusion, and operator interface, allows monitoring the relevant variables in the simulated city.

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Section: Related Workmentioning

confidence: 99%