期刊
IEEE INTERNET OF THINGS JOURNAL
卷 9, 期 19, 页码 18461-18472出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2022.3160739
关键词
Monitoring; Real-time systems; Wireless communication; Wireless sensor networks; Lighting control; Sensors; Lighting; Data filtering; Internet of Things (IoT); low delay; real-time monitoring; smart streetlights
资金
- Ministry of SMEs and Start-ups, South Korea [S2829065, S3010704]
- National Research Foundation of Korea [2020R1A4A101777511, 2021R1I1A3056900]
- Korea Technology & Information Promotion Agency for SMEs (TIPA) [S2829065, S3010704] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- National Research Foundation of Korea [2021R1I1A3056900] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
This article proposes an IoT-based low-delay smart streetlight monitoring system that provides real-time monitoring and greatly reduces data storage usage through a data filtering algorithm.
Smart streetlight is an outdoor infrastructure that uses technologies, such as sensors and actuators, to provide intelligent outdoor lighting and replace the power-consuming traditional streetlights. Although the current smart streetlight systems are employing these technologies for the maintenance, they simply gather data wirelessly on a periodical basis and still have drawbacks to provide real-time monitoring operation. To address these issues, the implementation of an IoT-based low-delay smart streetlight monitoring system is proposed in this article. In addition, a data filtering algorithm is also proposed in this article where redundant data are ignored to avoid overloading and excessive data storage consumption. Implementation results show that the proposed monitoring system and data filtering algorithm are able to provide real-time monitoring of smart streetlights with minimal time execution up to 0.11 ms and greatly reduce data storage usage up to 88.57%, respectively.
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