Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11861/7455
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yi, Wei-Ying | en_US |
dc.contributor.author | Prof. LEUNG Kwong Sak | en_US |
dc.contributor.author | Leung, Yee | en_US |
dc.contributor.author | Meng, Mei-Ling | en_US |
dc.contributor.author | Mak, Terrence | en_US |
dc.date.accessioned | 2023-03-02T09:31:19Z | - |
dc.date.available | 2023-03-02T09:31:19Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | 2016 IEEE SENSORS, 2016, pp. 1-3 | en_US |
dc.identifier.uri | http://hdl.handle.net/20.500.11861/7455 | - |
dc.description.abstract | Air pollution attracts extensive attention globally due to its critical impacts on human life. Monitoring systems providing real-time micro-level pollution information have been developed to provide authorities with data to mitigate these impacts. However, current systems are usually application-specific with fixed hardware and software configurations. They are inconvenient in maintenance, infeasible in reconfiguration, and unexpandable in sensing capabilities. This paper proposes a novel Modular Sensor System (MSS), which aims at tackling these issues by adopting the proposed Universal Sensor Interface (USI) and modular design in a sensor node. A compact MSS senor node with expandable plug-and-play sensor modules and multiple Wireless Sensor Networks (WSNs) compatibility is implemented and evaluated. Results indicate that MSS sensor node can be deployed in different scenarios while dynamically adapting to reconfigurations and monitoring air pollution at low concentration levels with high energy efficiency. We anticipate that MSS is able to relax the efforts on system maintenance, adaptation, and evolution in real-life large-scale deployment situation. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartof | 2016 IEEE SENSORS | en_US |
dc.title | Modular sensor system (MSS) for urban air pollution monitoring | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.doi | 10.1109/ICSENS.2016.7808924. | - |
item.fulltext | No Fulltext | - |
crisitem.author.dept | Department of Applied Data Science | - |
Appears in Collections: | Applied Data Science - Publication |
Page view(s)
60
Last Week
1
1
Last month
checked on Nov 24, 2024
Google ScholarTM
Impact Indices
Altmetric
PlumX
Metrics
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.