| Title | AirScope: Mobile Robots-Assisted Cooperative Indoor Air Quality Sensing by Distributed Deep Reinforcement Learning |
| Authors | Hu, Zhiwen Cong, Shuchang Song, Tiankuo Bian, Kaigui Song, Lingyang |
| Affiliation | Peking Univ, Natl Engn Lab Big Data Anal & Applicat, Sch Elect Engn & Comp Sci, Beijing 100871, Peoples R China Beijing 101 Middle Sch, Beijing 100091, Peoples R China Peking Univ, Sch Elect Engn & Comp Sci, State Key Lab Adv Opt Commun Syst & Networks, Beijing 100035, Peoples R China |
| Keywords | POLLUTION |
| Issue Date | 2020 |
| Publisher | IEEE INTERNET OF THINGS JOURNAL |
| Abstract | Indoor air pollution has become a growing health risk, but it is challenging to provide low-cost air quality monitoring for the indoor environment. In this article, we present "AirScope," a mobile sensing system that employs cooperative robots to monitor the indoor air quality. Since the wireless coverage can be incomplete in some indoor areas, AirScope allows the robots to defer uploading the data to the central server by utilizing their own data buffers. In order to guarantee the timeliness of the data in the server, AirScope aims to minimize the average data latency by properly planning the routes of the robots. Such a route planning strategy has to be implemented in a distributed way since the robots that are out of wireless coverage can only make plans on their own. In addition, the cooperation of the robots is also necessary because the aggregation of the robots in a small area increases the average data latency of the other unattended areas. To solve this distributed and cooperative routing planning problem, we propose a solution based on distributed deep Q-learning (DDQL). We evaluate the system performance by simulations and real-world experiments. The results show that AirScope is effective to reduce data latency, where the proposed DDQL is 8% better than the greedy algorithm and 24% better than the random strategy. |
| URI | http://hdl.handle.net/20.500.11897/592110 |
| ISSN | 2327-4662 |
| DOI | 10.1109/JIOT.2020.3004339 |
| Indexed | SCI(E) |
| Appears in Collections: | 信息科学技术学院 区域光纤通信网与新型光通信系统国家重点实验室 |
