| Title |
Real-Time Fine-Grained Air Quality Sensing Networks in Smart City: Design, Implementation, and Optimization |
| ID_Doc |
41973 |
| Authors |
Hu, ZW; Bai, ZX; Bian, KG; Wang, T; Song, LY |
| Title |
Real-Time Fine-Grained Air Quality Sensing Networks in Smart City: Design, Implementation, and Optimization |
| Year |
2019 |
| Published |
Ieee Internet Of Things Journal, 6, 5 |
| DOI |
10.1109/JIOT.2019.2900751 |
| Abstract |
Driven by the increasingly serious air pollution problem, the monitoring of air quality has gained much attention in both theoretical studies and practical implementations. In this paper, we present the architecture, implementation, and optimization of our own air quality sensing system, which provides real-time and fine-grained air quality map of the monitored area. As the major component, the optimization problem of our system is studied in detail. Our objective is to minimize the average joint error of the established real-time air quality map, which involves data inference for the unmeasured data values. A deep Q-learning solution has been proposed for the power control problem to reasonably plan the sensing tasks of the power-limited sensing devices online. A genetic algorithm has been designed for the location selection problem to efficiently find the suitable locations to deploy limited number of sensing devices. The performance of the proposed solutions are evaluated by simulations, showing a significant performance gain when adopting both strategies. |
| Author Keywords |
Air quality; genetic algorithm; power efficiency; reinforcement learning |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000491295800017 |
| WoS Category |
Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications |
| Research Area |
Computer Science; Engineering; Telecommunications |
| PDF |
https://arxiv.org/pdf/1810.08514
|